exemplar.txt

Below follows an example of how you have previously solved a similar task. IMPORTANT: The repository and details of your current task differ.
Github Issue description for repository m/a/t/p/l/o/t/l/i/b/_/_/m/a/t/p/l/o/t/l/i/b:
linear_model.RidgeClassifierCV's Parameter store_cv_values issue
#### Description
Parameter store_cv_values error on sklearn.linear_model.RidgeClassifierCV

#### Steps/Code to Reproduce
import numpy as np
from sklearn import linear_model as lm

#test database
n = 100
x = np.random.randn(n, 30)
y = np.random.normal(size = n)

rr = lm.RidgeClassifierCV(alphas = np.arange(0.1, 1000, 0.1), normalize = True, 
                                         store_cv_values = True).fit(x, y)

#### Expected Results
Expected to get the usual ridge regression model output, keeping the cross validation predictions as attribute.

#### Actual Results
TypeError: __init__() got an unexpected keyword argument 'store_cv_values'

lm.RidgeClassifierCV actually has no parameter store_cv_values, even though some attributes depends on it.

#### Versions
Windows-10-10.0.14393-SP0
Python 3.6.3 |Anaconda, Inc.| (default, Oct 15 2017, 03:27:45) [MSC v.1900 64 bit (AMD64)]
NumPy 1.13.3
SciPy 0.19.1
Scikit-Learn 0.19.1


Add store_cv_values boolean flag support to RidgeClassifierCV
Add store_cv_values support to RidgeClassifierCV - documentation claims that usage of this flag is possible:

> cv_values_ : array, shape = [n_samples, n_alphas] or shape = [n_samples, n_responses, n_alphas], optional
> Cross-validation values for each alpha (if **store_cv_values**=True and `cv=None`).

While actually usage of this flag gives 

> TypeError: **init**() got an unexpected keyword argument 'store_cv_values'

Your previous trajectory for this task was:
[{"role": "system", "content": "You are a software developer maintaining a large project.\nYou are working on an issue submitted to your project.\nThe issue contains a description marked between <issue> and </issue>.\nYou ultimate goal is to write a patch that resolves this issue.\n"}, {"role": "user", "content": "<issue>linear_model.RidgeClassifierCV's Parameter store_cv_values issue\n#### Description\nParameter store_cv_values error on sklearn.linear_model.RidgeClassifierCV\n#### Steps/Code to Reproduce\nimport numpy as np\nfrom sklearn import linear_model as lm\n#test database\nn = 100\nx = np.random.randn(n, 30)\ny = np.random.normal(size = n)\nrr = lm.RidgeClassifierCV(alphas = np.arange(0.1, 1000, 0.1), normalize = True,\nstore_cv_values = True).fit(x, y)\n#### Expected Results\nExpected to get the usual ridge regression model output, keeping the cross validation predictions as attribute.\n#### Actual Results\nTypeError: __init__() got an unexpected keyword argument 'store_cv_values'\nlm.RidgeClassifierCV actually has no parameter store_cv_values, even though some attributes depends on it.\n#### Versions\nWindows-10-10.0.14393-SP0\nPython 3.6.3 |Anaconda, Inc.| (default, Oct 15 2017, 03:27:45) [MSC v.1900 64 bit (AMD64)]\nNumPy 1.13.3\nSciPy 0.19.1\nScikit-Learn 0.19.1\nAdd store_cv_values boolean flag support to RidgeClassifierCV\nAdd store_cv_values support to RidgeClassifierCV - documentation claims that usage of this flag is possible:\n> cv_values_ : array, shape = [n_samples, n_alphas] or shape = [n_samples, n_responses, n_alphas], optional\n> Cross-validation values for each alpha (if **store_cv_values**=True and `cv=None`).\nWhile actually usage of this flag gives\n> TypeError: **init**() got an unexpected keyword argument 'store_cv_values'\n</issue>"}, {"role": "user", "content": "Based on the files, classes, methods, code statements from the issue that related to the bug, you can use below search APIs to get more context of the project.\n        search_class(class_name: str): Search for a class in the codebase.\n        search_method_in_file(method_name: str, file_path: str): Search for a method in a given file.\n        search_method_in_class(method_name: str, class_name: str): Search for a method in a given class.\n        search_method(method_name: str): Search for a method in the entire codebase.\n        search_code(code_str: str): Search for a code snippet in the entire codebase.\n        search_code_in_file(code_str: str, file_path: str): Search for a code snippet in a given file file.\n        Note that you can use multiple search APIs in one round.\n        Now analyze the issue and select necessary APIs to get more context of the project, each API call must have concrete arguments as inputs.\n        "}, {"role": "assistant", "content": "To address the issue described, we need to gather more context about the `RidgeClassifierCV` class and its initialization method to understand why the `store_cv_values` parameter is causing an unexpected keyword argument error. We should also look into the documentation or any related methods that might hint at the intended functionality of `store_cv_values`. Here are the API calls we will make:\n\n1. Search for the `RidgeClassifierCV` class to understand its structure and current parameters.\n2. Search for the `__init__` method within the `RidgeClassifierCV` class to see its current implementation and parameters.\n3. Search for any documentation or comments related to `store_cv_values` within the codebase to understand its intended usage and why it might be missing.\n\n```python\nsearch_class(\"RidgeClassifierCV\")\nsearch_method_in_class(\"__init__\", \"RidgeClassifierCV\")\nsearch_code(\"store_cv_values\")\n```"}, {"role": "user", "content": "Result of search_class(\"RidgeClassifierCV\"):\nFound 1 classes with name RidgeClassifierCV in the codebase.\nSearch result 1: <file>sklearn/linear_model/ridge.py</file> <class>RidgeClassifierCV</class> <code>class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):\n    def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True,\n                 normalize=False, scoring=None, cv=None, class_weight=None):\n    def fit(self, X, y, sample_weight=None):\n    @property\n    def classes_(self):\n</code>\n\n\n\nResult of search_method_in_class(\"__init__\", \"RidgeClassifierCV\"):\nFound 1 methods with name __init__ in class RidgeClassifierCV.\nSearch result 1: <file>sklearn/linear_model/ridge.py</file> <class>RidgeClassifierCV</class> <func>__init__</func> <code>    def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True,\n                 normalize=False, scoring=None, cv=None, class_weight=None):\n        super(RidgeClassifierCV, self).__init__(\n            alphas=alphas, fit_intercept=fit_intercept, normalize=normalize,\n            scoring=scoring, cv=cv)\n        self.class_weight = class_weight\n</code>\n\nOther results are in these files:\n\n\nResult of search_code(\"store_cv_values\"):\nFound 19 snippets containing `store_cv_values` in the codebase.\nThey appeared in the following files:\n<file>sklearn/linear_model/ridge.py</file> (15 matches)\n<file>sklearn/linear_model/tests/test_ridge.py</file> (4 matches)\n\n\n"}, {"role": "user", "content": "Let's analyze collected context first"}, {"role": "assistant", "content": "Based on the information gathered from the search results, let's analyze the context around the `RidgeClassifierCV` class, its `__init__` method, and the usage of `store_cv_values` in the codebase.\n\n1. **RidgeClassifierCV Class Structure**:\n   - The `RidgeClassifierCV` class is found in `sklearn/linear_model/ridge.py`.\n   - It inherits from `LinearClassifierMixin` and `_BaseRidgeCV`.\n   - The `__init__` method of `RidgeClassifierCV` accepts parameters such as `alphas`, `fit_intercept`, `normalize`, `scoring`, `cv`, and `class_weight`. Notably, `store_cv_values` is not among the parameters listed in the `__init__` method.\n\n2. **__init__ Method of RidgeClassifierCV**:\n   - The `__init__` method's parameters do not include `store_cv_values`, which aligns with the issue reported. The method initializes the class with the parameters provided and passes them to the superclass initializer.\n   - The absence of `store_cv_values` in the `__init__` method parameters explains why attempting to use it results in a `TypeError` for an unexpected keyword argument.\n\n3. **Usage of store_cv_values in the Codebase**:\n   - The search for `store_cv_values` yielded 19 snippets, with 15 matches in `sklearn/linear_model/ridge.py` and 4 matches in `sklearn/linear_model/tests/test_ridge.py`.\n   - This indicates that `store_cv_values` is referenced multiple times within the codebase, particularly in the same file where `RidgeClassifierCV` is defined and in test files for ridge models.\n\n**Analysis Summary**:\nThe issue stems from the fact that `store_cv_values` is referenced in the documentation and possibly expected to be a feature of `RidgeClassifierCV` based on its usage in tests and possibly other parts of the code. However, it is not implemented as a parameter in the `__init__` method of `RidgeClassifierCV`, leading to confusion and errors when users attempt to use it based on the documentation or their expectations of the class's functionality.\n\nTo resolve this issue, a patch would need to add support for `store_cv_values` in the `RidgeClassifierCV` class, ensuring it is handled appropriately in the class's methods and aligns with the intended functionality as suggested by its presence in the documentation and tests. This would involve modifying the `__init__` method to accept `store_cv_values` as a parameter and implementing the logic to utilize this parameter within the class."}, {"role": "user", "content": "Based on your analysis, answer below questions:  - do we need more context: construct search API calls to get more context of the project. (leave it empty if you don't need more context)  - where are bug locations: buggy files and methods. (leave it empty if you don't have enough information)"}, {"role": "assistant", "content": "- do we need more context:\n\n- where are bug locations:\n  - **Buggy File**: `sklearn/linear_model/ridge.py`\n  - **Buggy Method**: The `__init__` method of the `RidgeClassifierCV` class does not include `store_cv_values` as a parameter, which is the root cause of the reported issue."}, {"role": "user", "content": "Here are the code in buggy locations:\nFound 1 methods with name __init__ in class RidgeClassifierCV.\nSearch result 1: <file>sklearn/linear_model/ridge.py</file> <class>RidgeClassifierCV</class> <func>__init__</func> <code>    def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True,\n                 normalize=False, scoring=None, cv=None, class_weight=None):\n        super(RidgeClassifierCV, self).__init__(\n            alphas=alphas, fit_intercept=fit_intercept, normalize=normalize,\n            scoring=scoring, cv=cv)\n        self.class_weight = class_weight\n</code>\n\nOther results are in these files:\n\n"}, {"role": "user", "content": "Write a patch for the issue, based on the retrieved context. You can import necessary libraries.\nReturn the patch in the format below. Within <file></file>, replace \"...\" with actual file path. Within <original></original>, replace \"...\" with the original code snippet from the program. Within <patched></patched>, replace \"...\" with the fixed version of the original code. When adding orignal code and updated code, pay attention to indentation, as the code is in Python.\nYou can write multiple modifications if needed.\n\n# modification 1\n```python\n<file>...</file>\n<original>...</original>\n<patched>...</patched>\n```\n\n# modification 2\n```python\n<file>...</file>\n<original>...</original>\n<patched>...</patched>\n```\n\n# modification 3\n...\n"}, {"role": "assistant", "content": "# modification 1\n```python\n<file>sklearn/linear_model/ridge.py</file>\n<original>    def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True,\n                 normalize=False, scoring=None, cv=None, class_weight=None):\n        super(RidgeClassifierCV, self).__init__(\n            alphas=alphas, fit_intercept=fit_intercept, normalize=normalize,\n            scoring=scoring, cv=cv)\n        self.class_weight = class_weight\n</original>\n<patched>    def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True,\n                 normalize=False, scoring=None, cv=None, class_weight=None, store_cv_values=False):\n        super(RidgeClassifierCV, self).__init__(\n            alphas=alphas, fit_intercept=fit_intercept, normalize=normalize,\n            scoring=scoring, cv=cv, store_cv_values=store_cv_values)\n        self.class_weight = class_weight\n        self.store_cv_values = store_cv_values\n</patched>\n```\n\n**Explanation**: This modification adds the `store_cv_values` parameter to the `__init__` method of the `RidgeClassifierCV` class. It ensures that this parameter is also passed to the superclass initializer, where it's assumed that the superclass (`_BaseRidgeCV` or another relevant class) will handle or make use of this parameter appropriately. Additionally, it sets `store_cv_values` as an instance variable, aligning with the expectation that it can be used within the class to conditionally store cross-validation values. This change addresses the issue by allowing users to specify `store_cv_values` when initializing `RidgeClassifierCV`, thus preventing the `TypeError` and enabling the intended functionality."}, {"role": "user", "content": "Written a patch that resolves the issue. Congratulations!"}]