html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k |
|---|---|---|---|
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27506 | [
"Bug"
] | Test failure in i686 with version 1.3.1
### Describe the bug
During the build of scikit-learn for Fedora Linux, I'm obtaining an error runing the tests in i686. The test that fails is:
`sklearn/tree/tests/test_export.py::test_graphviz_toy`
### Steps/Code to Reproduce
In a i686 machine
```
pytest sklearn/tree... | 27,506 |
https://github.com/scikit-learn/scikit-learn/issues/27505 | [
"Documentation"
] | Impact of class weights in LogisticRegression
### Describe the issue linked to the documentation
The impact of class weights and the exact objective function with (all kinds of) weights for `LogisticRegression` should be mentioned in the user guide. Importantly, the scale of weights interact with the (anti-) penalty ... | 27,505 |
https://github.com/scikit-learn/scikit-learn/issues/27504 | [
"New Feature",
"Needs Triage"
] | Returning number of samples in leaf nodes in decision trees.
### Describe the workflow you want to enable
In the paper "Towards Practical Lipschitz Bandits" by Wang, Ye, Geng and Rudin (https://dl.acm.org/doi/10.1145/3412815.3416885), the authors used a modified version of the DecisionTreeRegressor in their algorithm... | 27,504 |
https://github.com/scikit-learn/scikit-learn/issues/27504 | [
"New Feature",
"Needs Triage"
] | Returning number of samples in leaf nodes in decision trees.
### Describe the workflow you want to enable
In the paper "Towards Practical Lipschitz Bandits" by Wang, Ye, Geng and Rudin (https://dl.acm.org/doi/10.1145/3412815.3416885), the authors used a modified version of the DecisionTreeRegressor in their algorithm... | 27,504 |
https://github.com/scikit-learn/scikit-learn/issues/27504 | [
"New Feature",
"Needs Triage"
] | Returning number of samples in leaf nodes in decision trees.
### Describe the workflow you want to enable
In the paper "Towards Practical Lipschitz Bandits" by Wang, Ye, Geng and Rudin (https://dl.acm.org/doi/10.1145/3412815.3416885), the authors used a modified version of the DecisionTreeRegressor in their algorithm... | 27,504 |
https://github.com/scikit-learn/scikit-learn/issues/27504 | [
"New Feature",
"Needs Triage"
] | Returning number of samples in leaf nodes in decision trees.
### Describe the workflow you want to enable
In the paper "Towards Practical Lipschitz Bandits" by Wang, Ye, Geng and Rudin (https://dl.acm.org/doi/10.1145/3412815.3416885), the authors used a modified version of the DecisionTreeRegressor in their algorithm... | 27,504 |
https://github.com/scikit-learn/scikit-learn/issues/27504 | [
"New Feature",
"Needs Triage"
] | Returning number of samples in leaf nodes in decision trees.
### Describe the workflow you want to enable
In the paper "Towards Practical Lipschitz Bandits" by Wang, Ye, Geng and Rudin (https://dl.acm.org/doi/10.1145/3412815.3416885), the authors used a modified version of the DecisionTreeRegressor in their algorithm... | 27,504 |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 |
https://github.com/scikit-learn/scikit-learn/issues/27503 | [
"Bug",
"Needs Triage"
] | Cannot save any model
### Describe the bug
Hi,
Hope everything is going well. I have been having issues saving any model either using pickle or joblib getting this error:
`PicklingError: Can't pickle <function <lambda> at 0x28bf58fe0>: it's not found as __main__.<lambda>`
When using Skops, I am able to sav... | 27,503 |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 |
https://github.com/scikit-learn/scikit-learn/issues/27499 | [
"Bug",
"Needs Triage"
] | Numpy "BracketError" appears in some cases when using power transformer with columns that contain the same values
### Describe the bug
I encountered this error for the first time while transforming a metabolomics dataset using power transformer. Prior to using PowerTransformer I had imputed the dataset with "median... | 27,499 |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 |
https://github.com/scikit-learn/scikit-learn/issues/27498 | [
"Enhancement"
] | `check_array` error on Pandas series is confusing
### Describe the bug
I don't know if this is a bug or a feature request.
When inputing a Pandas or Polars series for estimators or transformers accepting only 2D arrays, `check_array()` raises the following error:
```
ValueError: Expected 2D array, got 1D array... | 27,498 |
https://github.com/scikit-learn/scikit-learn/issues/27493 | [
"Documentation",
"good first issue",
"help wanted"
] | Survey: Open-Source Documentation for Newcomers
### Describe the issue linked to the documentation
Hello Scikit-learn Community!
We are researchers from George Mason University in the United States, looking for open-source contributors to participate in our survey on open-source software (OSS) project documentatio... | 27,493 |
https://github.com/scikit-learn/scikit-learn/issues/27493 | [
"Documentation",
"good first issue",
"help wanted"
] | Survey: Open-Source Documentation for Newcomers
### Describe the issue linked to the documentation
Hello Scikit-learn Community!
We are researchers from George Mason University in the United States, looking for open-source contributors to participate in our survey on open-source software (OSS) project documentatio... | 27,493 |
https://github.com/scikit-learn/scikit-learn/issues/27493 | [
"Documentation",
"good first issue",
"help wanted"
] | Survey: Open-Source Documentation for Newcomers
### Describe the issue linked to the documentation
Hello Scikit-learn Community!
We are researchers from George Mason University in the United States, looking for open-source contributors to participate in our survey on open-source software (OSS) project documentatio... | 27,493 |
https://github.com/scikit-learn/scikit-learn/issues/27493 | [
"Documentation",
"good first issue",
"help wanted"
] | Survey: Open-Source Documentation for Newcomers
### Describe the issue linked to the documentation
Hello Scikit-learn Community!
We are researchers from George Mason University in the United States, looking for open-source contributors to participate in our survey on open-source software (OSS) project documentatio... | 27,493 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27484 | [
"Enhancement",
"API",
"Needs Decision"
] | Allow LogisticRegression with lbfgs solver to control `maxfun` parameter of solver
### Describe the workflow you want to enable
Similarly to what is mentioned on https://github.com/scikit-learn/scikit-learn/issues/9273
> Training an MLP regressor (or classifier) using l-bfgs currently cannot run for more than (app... | 27,484 |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 |
https://github.com/scikit-learn/scikit-learn/issues/27483 | [
"Enhancement",
"Moderate",
"Performance",
"Array API"
] | Solve PCA via `np.linalg.eigh(X_centered.T @ X_centered)` instead of `np.linalg.svd(X_centered)` when `X.shape[1]` is small enough.
### Describe the workflow you want to enable
Assuming that `X.shape[0] >> X.shape[1]` and `X.shape[1]` is small enough to materialize the covariance matrix `X.T @ X`, then using an eig... | 27,483 |
https://github.com/scikit-learn/scikit-learn/issues/27482 | [
"Bug"
] | ColumnTransformer converts pandas extension datatypes to `object`
### Describe the bug
pandas has some [extension data types](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#) such as `pd.Int64DType` and `pd.Float64DType` that use `pd.NA` to represent null values.
These datatypes in DataFrames g... | 27,482 |
https://github.com/scikit-learn/scikit-learn/issues/27482 | [
"Bug"
] | ColumnTransformer converts pandas extension datatypes to `object`
### Describe the bug
pandas has some [extension data types](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#) such as `pd.Int64DType` and `pd.Float64DType` that use `pd.NA` to represent null values.
These datatypes in DataFrames g... | 27,482 |
https://github.com/scikit-learn/scikit-learn/issues/27482 | [
"Bug"
] | ColumnTransformer converts pandas extension datatypes to `object`
### Describe the bug
pandas has some [extension data types](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#) such as `pd.Int64DType` and `pd.Float64DType` that use `pd.NA` to represent null values.
These datatypes in DataFrames g... | 27,482 |
https://github.com/scikit-learn/scikit-learn/issues/27482 | [
"Bug"
] | ColumnTransformer converts pandas extension datatypes to `object`
### Describe the bug
pandas has some [extension data types](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#) such as `pd.Int64DType` and `pd.Float64DType` that use `pd.NA` to represent null values.
These datatypes in DataFrames g... | 27,482 |
https://github.com/scikit-learn/scikit-learn/issues/27481 | [
"Bug",
"Needs Triage"
] | Homogeneity Score is Not Consistently Correct For Trivial Clustering
### Describe the bug
The homogeneity_score is not being computed consistently when you have a single truth label for different array sizes. It seems not to matter how many unique labels are in the predicted labels, just so long as there is only on... | 27,481 |
https://github.com/scikit-learn/scikit-learn/issues/27481 | [
"Bug",
"Needs Triage"
] | Homogeneity Score is Not Consistently Correct For Trivial Clustering
### Describe the bug
The homogeneity_score is not being computed consistently when you have a single truth label for different array sizes. It seems not to matter how many unique labels are in the predicted labels, just so long as there is only on... | 27,481 |
https://github.com/scikit-learn/scikit-learn/issues/27481 | [
"Bug",
"Needs Triage"
] | Homogeneity Score is Not Consistently Correct For Trivial Clustering
### Describe the bug
The homogeneity_score is not being computed consistently when you have a single truth label for different array sizes. It seems not to matter how many unique labels are in the predicted labels, just so long as there is only on... | 27,481 |
https://github.com/scikit-learn/scikit-learn/issues/27481 | [
"Bug",
"Needs Triage"
] | Homogeneity Score is Not Consistently Correct For Trivial Clustering
### Describe the bug
The homogeneity_score is not being computed consistently when you have a single truth label for different array sizes. It seems not to matter how many unique labels are in the predicted labels, just so long as there is only on... | 27,481 |
https://github.com/scikit-learn/scikit-learn/issues/27481 | [
"Bug",
"Needs Triage"
] | Homogeneity Score is Not Consistently Correct For Trivial Clustering
### Describe the bug
The homogeneity_score is not being computed consistently when you have a single truth label for different array sizes. It seems not to matter how many unique labels are in the predicted labels, just so long as there is only on... | 27,481 |
https://github.com/scikit-learn/scikit-learn/issues/27473 | [
"Bug",
"Needs Triage"
] | check_estimator is broken
### Describe the bug
Since the version 1.3.0, the check_estimator function is broken for all our custom estimators, but for native estimators as well.
An exception is raised for the test `check_estimators_pickle`: `ValueError: When creating aligned memmap-backed arrays, input must be a ... | 27,473 |
https://github.com/scikit-learn/scikit-learn/issues/27467 | [
"Needs Triage"
] | ⚠️ CI failed on Linux.pylatest_pip_openblas_pandas ⚠️
**CI is still failing on [Linux.pylatest_pip_openblas_pandas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59597&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e)** (Sep 28, 2023)
- test_kneighbors_brute_backend[float32-manhattan]
COMMENT... | 27,467 |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 |
https://github.com/scikit-learn/scikit-learn/issues/27463 | [
"Build / CI",
"Needs Decision"
] | CI Issues regarding conda lock files
Opening a new issue regarding some of the discussions around https://github.com/scikit-learn/scikit-learn/pull/27448#issuecomment-1733374337
@lesteve these are maybe what I have in mind:
- they're generated files and usually it's a good idea not to have generated files in the... | 27,463 |
https://github.com/scikit-learn/scikit-learn/issues/27460 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI failed on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=59484&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Sep 25, 2023)
- test_pairwise_distances_argkmin[45-csr_matrix-float32-parallel_on_X-cityblo... | 27,460 |
https://github.com/scikit-learn/scikit-learn/issues/27455 | [
"Bug",
"Needs Triage"
] | Results of `LogisticRegression` are sensitive to the scale of `class_weight`
### Describe the bug
When fitting `LogisticRegression` to a dataset with imbalanced classes, `class_weight` parameter seems to produce different results that depend on the scale of weights, even though the ratio of the weights is the same, e... | 27,455 |
https://github.com/scikit-learn/scikit-learn/issues/27455 | [
"Bug",
"Needs Triage"
] | Results of `LogisticRegression` are sensitive to the scale of `class_weight`
### Describe the bug
When fitting `LogisticRegression` to a dataset with imbalanced classes, `class_weight` parameter seems to produce different results that depend on the scale of weights, even though the ratio of the weights is the same, e... | 27,455 |
https://github.com/scikit-learn/scikit-learn/issues/27455 | [
"Bug",
"Needs Triage"
] | Results of `LogisticRegression` are sensitive to the scale of `class_weight`
### Describe the bug
When fitting `LogisticRegression` to a dataset with imbalanced classes, `class_weight` parameter seems to produce different results that depend on the scale of weights, even though the ratio of the weights is the same, e... | 27,455 |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 |
https://github.com/scikit-learn/scikit-learn/issues/27447 | [
"New Feature"
] | Accept pathlib.Path for data_home in fetch_openml
### Describe the workflow you want to enable
When using `fetch_openml()` it would be nice if `pathlib.Path` objects were supported. Currently, there is a type check for `str | None`, so I have to convert my path objects first.
### Describe your proposed solution
Cha... | 27,447 |
https://github.com/scikit-learn/scikit-learn/issues/27441 | [
"Documentation",
"help wanted"
] | partial_dependence() with method recursion computes conditional partial dependence for trees
### Describe the bug
For the case of correlated predictors (clearly highly common) the `sklearn.inspection.partial_dependence()` function gives different answers for `method` = "recursion" and `method` = "brute", see my [po... | 27,441 |
https://github.com/scikit-learn/scikit-learn/issues/27441 | [
"Documentation",
"help wanted"
] | partial_dependence() with method recursion computes conditional partial dependence for trees
### Describe the bug
For the case of correlated predictors (clearly highly common) the `sklearn.inspection.partial_dependence()` function gives different answers for `method` = "recursion" and `method` = "brute", see my [po... | 27,441 |
https://github.com/scikit-learn/scikit-learn/issues/27441 | [
"Documentation",
"help wanted"
] | partial_dependence() with method recursion computes conditional partial dependence for trees
### Describe the bug
For the case of correlated predictors (clearly highly common) the `sklearn.inspection.partial_dependence()` function gives different answers for `method` = "recursion" and `method` = "brute", see my [po... | 27,441 |
https://github.com/scikit-learn/scikit-learn/issues/27441 | [
"Documentation",
"help wanted"
] | partial_dependence() with method recursion computes conditional partial dependence for trees
### Describe the bug
For the case of correlated predictors (clearly highly common) the `sklearn.inspection.partial_dependence()` function gives different answers for `method` = "recursion" and `method` = "brute", see my [po... | 27,441 |
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