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/22977 | [
"New Feature",
"Needs Triage"
] | SequentialFeatureSelector should have a feature order attribute
### Describe the workflow you want to enable
I want to be able to run Forward Sequential Feature Selector and know the order which it picked the features.
Right now you can only get the indices or a boolean mask of all the features you've selected. Of c... | 22,977 |
https://github.com/scikit-learn/scikit-learn/issues/22977 | [
"New Feature",
"Needs Triage"
] | SequentialFeatureSelector should have a feature order attribute
### Describe the workflow you want to enable
I want to be able to run Forward Sequential Feature Selector and know the order which it picked the features.
Right now you can only get the indices or a boolean mask of all the features you've selected. Of c... | 22,977 |
https://github.com/scikit-learn/scikit-learn/issues/22975 | [
"Build / CI"
] | CI MacOS job pylatest_conda_mkl_no_openmp install fails
see https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=40226&view=logs&jobId=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2&j=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2&t=83107d01-18db-5293-bb0f-49ac2bf2f625 for instance
Fails because of
```
CondaVerific... | 22,975 |
https://github.com/scikit-learn/scikit-learn/issues/22975 | [
"Build / CI"
] | CI MacOS job pylatest_conda_mkl_no_openmp install fails
see https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=40226&view=logs&jobId=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2&j=e6d5b7c0-0dfd-5ddf-13d5-c71bebf56ce2&t=83107d01-18db-5293-bb0f-49ac2bf2f625 for instance
Fails because of
```
CondaVerific... | 22,975 |
https://github.com/scikit-learn/scikit-learn/issues/22974 | [
"module:decomposition"
] | Variance explained by the components of an pca, zero from a certain moment
### Describe the bug
I'm doing a PCA on data of quite large dimension (about 5000 individuals, about 3000 variables).
The problem presented does not depend on the type of data, I find it with my data but also with synthetically generated... | 22,974 |
https://github.com/scikit-learn/scikit-learn/issues/22974 | [
"module:decomposition"
] | Variance explained by the components of an pca, zero from a certain moment
### Describe the bug
I'm doing a PCA on data of quite large dimension (about 5000 individuals, about 3000 variables).
The problem presented does not depend on the type of data, I find it with my data but also with synthetically generated... | 22,974 |
https://github.com/scikit-learn/scikit-learn/issues/22969 | [
"Bug",
"Hard",
"module:model_selection"
] | cross_validate with multiple scorers sets ALL results to nan after just one has errored
### Describe the bug
I'm supplying `cross_validate` with `scoring = (
"r2",
"neg_median_absolute_error",
"neg_mean_absolute_error",
"neg_mean_absolute_percentage_error",
"neg_mean_squared_log_error",
... | 22,969 |
https://github.com/scikit-learn/scikit-learn/issues/22969 | [
"Bug",
"Hard",
"module:model_selection"
] | cross_validate with multiple scorers sets ALL results to nan after just one has errored
### Describe the bug
I'm supplying `cross_validate` with `scoring = (
"r2",
"neg_median_absolute_error",
"neg_mean_absolute_error",
"neg_mean_absolute_percentage_error",
"neg_mean_squared_log_error",
... | 22,969 |
https://github.com/scikit-learn/scikit-learn/issues/22969 | [
"Bug",
"Hard",
"module:model_selection"
] | cross_validate with multiple scorers sets ALL results to nan after just one has errored
### Describe the bug
I'm supplying `cross_validate` with `scoring = (
"r2",
"neg_median_absolute_error",
"neg_mean_absolute_error",
"neg_mean_absolute_percentage_error",
"neg_mean_squared_log_error",
... | 22,969 |
https://github.com/scikit-learn/scikit-learn/issues/22969 | [
"Bug",
"Hard",
"module:model_selection"
] | cross_validate with multiple scorers sets ALL results to nan after just one has errored
### Describe the bug
I'm supplying `cross_validate` with `scoring = (
"r2",
"neg_median_absolute_error",
"neg_mean_absolute_error",
"neg_mean_absolute_percentage_error",
"neg_mean_squared_log_error",
... | 22,969 |
https://github.com/scikit-learn/scikit-learn/issues/22967 | [
"Needs Triage"
] | Why does GridSearchCV cause the computer to overheat when n_job=-1?
In GridsSarchCV, when n_job=-1, the computer overheats and spyder disconnects.
I use TimeSeriesSplit together, but does using TimeSeriesSplit together cause this problem?
`tscv = TimeSeriesSplit(n_splits=3)
lstm = GridSearchCV(estimator=model, ... | 22,967 |
https://github.com/scikit-learn/scikit-learn/issues/22967 | [
"Needs Triage"
] | Why does GridSearchCV cause the computer to overheat when n_job=-1?
In GridsSarchCV, when n_job=-1, the computer overheats and spyder disconnects.
I use TimeSeriesSplit together, but does using TimeSeriesSplit together cause this problem?
`tscv = TimeSeriesSplit(n_splits=3)
lstm = GridSearchCV(estimator=model, ... | 22,967 |
https://github.com/scikit-learn/scikit-learn/issues/22967 | [
"Needs Triage"
] | Why does GridSearchCV cause the computer to overheat when n_job=-1?
In GridsSarchCV, when n_job=-1, the computer overheats and spyder disconnects.
I use TimeSeriesSplit together, but does using TimeSeriesSplit together cause this problem?
`tscv = TimeSeriesSplit(n_splits=3)
lstm = GridSearchCV(estimator=model, ... | 22,967 |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 |
https://github.com/scikit-learn/scikit-learn/issues/22966 | [
"New Feature",
"module:model_selection"
] | Out of Fold score calculation for Cross validation
### Describe the workflow you want to enable
What roughly happens in cross-validation as of now:
```
# k-fold cross validation
scores = list()
kfold = KFold(n_splits=10, shuffle=True)
# enumerate splits
for train_ix, test_ix in kfold.split(X):
# get da... | 22,966 |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 |
https://github.com/scikit-learn/scikit-learn/issues/22947 | [
"Bug",
"module:linear_model"
] | BUG unpenalized Ridge does not give minimum norm solution
#### Describe the bug
As noted in #22910, `Ridge(alpha=0, fit_intercept=True)` does not give the minimal norm solution for wide data, i.e. `n_features > n_samples`.
Note that we nowhere guarantee that we provide the **minimum norm solution**.
Edit: Same ... | 22,947 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22945 | [
"module:gaussian_process"
] | GaussianProcessRegressor (predict)
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/22925
<div type='discussions-op-text'>
<sup>Originally posted by **jecampagne** March 23, 2022</sup>
Hello,
I am questioning the code of `predict` of the `GaussianProcessRegressor`. The code is based o... | 22,945 |
https://github.com/scikit-learn/scikit-learn/issues/22944 | [
"Needs Triage"
] | AttributeError when use ‘CountVectorizer‘ with ‘preprocessor ’
I define a preprocessing function like:
def clean_parens(text):
text = re.sub("=","e",text)
return text
Then, word vector extraction is defined as：
SelfDicCvB = CountVectorizer(input='filename',vocabulary=voc['word'].tolist(),
... | 22,944 |
https://github.com/scikit-learn/scikit-learn/issues/22939 | [
"New Feature",
"module:preprocessing"
] | sklearn.preprocessing.LabelEncoder: new feature to manage unknown value in `transform()` method
### Describe the workflow you want to enable
Hi, I'm working with `from sklearn.preprocessing import LabelEncoder` and I would find interesting (and useful) to have an option to not get an error in case of when using the... | 22,939 |
https://github.com/scikit-learn/scikit-learn/issues/22939 | [
"New Feature",
"module:preprocessing"
] | sklearn.preprocessing.LabelEncoder: new feature to manage unknown value in `transform()` method
### Describe the workflow you want to enable
Hi, I'm working with `from sklearn.preprocessing import LabelEncoder` and I would find interesting (and useful) to have an option to not get an error in case of when using the... | 22,939 |
https://github.com/scikit-learn/scikit-learn/issues/22931 | [
"Documentation",
"Build / CI"
] | CI: CircleCI artifact link has changed
see https://184075-843222-gh.circle-artifacts.com/0/doc/_changed.html
or https://184079-843222-gh.circle-artifacts.com/0/doc/_changed.html
COMMENT:
It looks like the URL changed. Here is an example of the URL to one of the PRS:
https://output.circle-artifacts.com/output/job/... | 22,931 |
https://github.com/scikit-learn/scikit-learn/issues/22931 | [
"Documentation",
"Build / CI"
] | CI: CircleCI artifact link has changed
see https://184075-843222-gh.circle-artifacts.com/0/doc/_changed.html
or https://184079-843222-gh.circle-artifacts.com/0/doc/_changed.html
COMMENT:
Cross linking the issue opened upstream : https://github.com/larsoner/circleci-artifacts-redirector/issues/8 | 22,931 |
https://github.com/scikit-learn/scikit-learn/issues/22931 | [
"Documentation",
"Build / CI"
] | CI: CircleCI artifact link has changed
see https://184075-843222-gh.circle-artifacts.com/0/doc/_changed.html
or https://184079-843222-gh.circle-artifacts.com/0/doc/_changed.html
COMMENT:
The link has been fixed upstream but only for the action for now (https://github.com/larsoner/circleci-artifacts-redirector-action... | 22,931 |
https://github.com/scikit-learn/scikit-learn/issues/22931 | [
"Documentation",
"Build / CI"
] | CI: CircleCI artifact link has changed
see https://184075-843222-gh.circle-artifacts.com/0/doc/_changed.html
or https://184079-843222-gh.circle-artifacts.com/0/doc/_changed.html
COMMENT:
Closing via #22991 | 22,931 |
https://github.com/scikit-learn/scikit-learn/issues/22930 | [
"New Feature",
"module:metrics"
] | roc_auc_score, support `average=None` for multiclass labels
### Describe the workflow you want to enable
Currently as of version 1.0.2 If you try to calculate the roc_auc_score for a multiclass `y_true` with `average=None` you get an error:
````
import sklearn.metrics as metrics
import numpy as np
y_true =... | 22,930 |
https://github.com/scikit-learn/scikit-learn/issues/22927 | [
"Bug",
"Needs Triage"
] | R squared is missing in Scikit learn
### Describe the bug
For the calculation of R squared, you need to determine the Correlation coefficient, and then you need to square the result.
Is not that R2 score with negative values...
### Steps/Code to Reproduce
from sklearn.metrics import r2_score
y_true = [1, 2, 3... | 22,927 |
https://github.com/scikit-learn/scikit-learn/issues/22927 | [
"Bug",
"Needs Triage"
] | R squared is missing in Scikit learn
### Describe the bug
For the calculation of R squared, you need to determine the Correlation coefficient, and then you need to square the result.
Is not that R2 score with negative values...
### Steps/Code to Reproduce
from sklearn.metrics import r2_score
y_true = [1, 2, 3... | 22,927 |
https://github.com/scikit-learn/scikit-learn/issues/22927 | [
"Bug",
"Needs Triage"
] | R squared is missing in Scikit learn
### Describe the bug
For the calculation of R squared, you need to determine the Correlation coefficient, and then you need to square the result.
Is not that R2 score with negative values...
### Steps/Code to Reproduce
from sklearn.metrics import r2_score
y_true = [1, 2, 3... | 22,927 |
https://github.com/scikit-learn/scikit-learn/issues/22927 | [
"Bug",
"Needs Triage"
] | R squared is missing in Scikit learn
### Describe the bug
For the calculation of R squared, you need to determine the Correlation coefficient, and then you need to square the result.
Is not that R2 score with negative values...
### Steps/Code to Reproduce
from sklearn.metrics import r2_score
y_true = [1, 2, 3... | 22,927 |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 |
https://github.com/scikit-learn/scikit-learn/issues/22922 | [
"Bug",
"module:linear_model"
] | QuantileRegressor unable to allocate memory for large datasets
### Describe the bug
I am using sklearn.linear_model.QuantileRegressor for a dataset with ~2.9 million datapoints.
When I use it as follows, I get a memory error.
`MemoryError: Unable to allocate 61.6 TiB for an array with shape (2909376, 2909376) ... | 22,922 |
https://github.com/scikit-learn/scikit-learn/issues/22914 | [
"Bug",
"module:linear_model"
] | Calculation of alphas in ElasticNetCV doesn't use sample_weight
### Describe the bug
In ElasticNetCV, the first and largest value of `alpha`, call it `alpha_max`, should be just large enough to force all of the coefficients to become zero. The existing code works correctly when `sample_weight` is not specified. How... | 22,914 |
https://github.com/scikit-learn/scikit-learn/issues/22914 | [
"Bug",
"module:linear_model"
] | Calculation of alphas in ElasticNetCV doesn't use sample_weight
### Describe the bug
In ElasticNetCV, the first and largest value of `alpha`, call it `alpha_max`, should be just large enough to force all of the coefficients to become zero. The existing code works correctly when `sample_weight` is not specified. How... | 22,914 |
https://github.com/scikit-learn/scikit-learn/issues/22914 | [
"Bug",
"module:linear_model"
] | Calculation of alphas in ElasticNetCV doesn't use sample_weight
### Describe the bug
In ElasticNetCV, the first and largest value of `alpha`, call it `alpha_max`, should be just large enough to force all of the coefficients to become zero. The existing code works correctly when `sample_weight` is not specified. How... | 22,914 |
https://github.com/scikit-learn/scikit-learn/issues/22914 | [
"Bug",
"module:linear_model"
] | Calculation of alphas in ElasticNetCV doesn't use sample_weight
### Describe the bug
In ElasticNetCV, the first and largest value of `alpha`, call it `alpha_max`, should be just large enough to force all of the coefficients to become zero. The existing code works correctly when `sample_weight` is not specified. How... | 22,914 |
https://github.com/scikit-learn/scikit-learn/issues/22907 | [
"Bug"
] | UserWarning is thrown when calling `HistGradientBoostingRegressor.fit` while specifying `scoring` argument
### Describe the bug
`UserWarning: X does not have valid feature names` is thrown when calling `HistGradientBoostingRegressor.fit` using `pandas.DataFrame`.
The regressor was constructed by specifying `scorin... | 22,907 |
https://github.com/scikit-learn/scikit-learn/issues/22904 | [
"Bug",
"Needs Triage"
] | Pytest plugins in a non-top-level conftest is no longer supported
### Describe the bug
When running pytest, an issue pops up due to https://github.com/scikit-learn/scikit-learn/blob/7116165f493998cde7989a29458f36bdfb0a9ab5/sklearn/conftest.py#L24-L25
I was able to fix this by moving the offending line to `scikit... | 22,904 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22893 | [
"Hard",
"Meta-issue",
"Metadata Routing"
] | SLEP006 - Metadata Routing task list
This issue is to track the work we need to do before we can merge `sample-props` branch into `main`:
- [x] Based on this prototype: https://github.com/scikit-learn/scikit-learn/pull/16079
- [x] Merge https://github.com/scikit-learn/scikit-learn/pull/22083 into `sample-props`. T... | 22,893 |
https://github.com/scikit-learn/scikit-learn/issues/22890 | [
"New Feature",
"Needs Triage"
] | KMeans support for complex numbers
### Describe the workflow you want to enable
It would be nice with K-means clustering (`KMeans`) for complex-valued vectors. Today complex-valued vectors in the `fit` method throw a `ValueError`.
### Describe your proposed solution
Each element of a complex vector is associated wi... | 22,890 |
https://github.com/scikit-learn/scikit-learn/issues/22885 | [
"Bug",
"module:model_selection"
] | Modified order of operations in _approximate_mode() changes function behavior
### Describe the bug
In issue [#20774](https://github.com/scikit-learn/scikit-learn/issues/20774) and subsequent [PR #20904](https://github.com/scikit-learn/scikit-learn/pull/20904), the order of operations in the calculation of `continuo... | 22,885 |
https://github.com/scikit-learn/scikit-learn/issues/22884 | [
"Bug",
"Needs Triage"
] | SGDRegressor intercept exactly around half of the value it should be
### Describe the bug
It seems that the recent sklearn.SGDRegressor produces intercepts half of the real values.
### Steps/Code to Reproduce
import numpy as np
from sklearn.linear_model import SGDRegressor, LinearRegression
x = np.random.randn(1... | 22,884 |
https://github.com/scikit-learn/scikit-learn/issues/22881 | [
"help wanted",
"Hard",
"module:test-suite",
"Meta-issue",
"float32"
] | Improve tests to make them run on variously typed data using the `global_dtype` fixture
## Context: the new `global_dtype` fixture and `SKLEARN_RUN_FLOAT32_TESTS` environment variable
Introduction of low-level computational routines for 32bit motivated an extension of tests to run them on 32bit.
In this regards,... | 22,881 |
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