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/23788 | [
"New Feature",
"module:pipeline"
] | allow sklearn pipeline cache to ignore certain arguments
### Describe the workflow you want to enable
To the best of my understanding, sklearn pipeline cache helps reduce computation when the full argument list matches what has been cached. The main motivation of caching seems to cache the transformer steps, and tran... | 23,788 |
https://github.com/scikit-learn/scikit-learn/issues/23788 | [
"New Feature",
"module:pipeline"
] | allow sklearn pipeline cache to ignore certain arguments
### Describe the workflow you want to enable
To the best of my understanding, sklearn pipeline cache helps reduce computation when the full argument list matches what has been cached. The main motivation of caching seems to cache the transformer steps, and tran... | 23,788 |
https://github.com/scikit-learn/scikit-learn/issues/23787 | [
"New Feature",
"Needs Triage"
] | allow sklearn pipeline cache to ignore certain arguments
### Describe the workflow you want to enable
To the best of my understanding, sklearn pipeline cache helps reduce computation when the full argument list matches what has been cached. The main motivation of caching seems to cache the transformer steps, and tran... | 23,787 |
https://github.com/scikit-learn/scikit-learn/issues/23786 | [
"Build / CI"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI is still failing on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=44957&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Jul 25, 2022)
- Test Collection Failure
COMMENT:
The nogil segfault was already ... | 23,786 |
https://github.com/scikit-learn/scikit-learn/issues/23786 | [
"Build / CI"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI is still failing on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=44957&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Jul 25, 2022)
- Test Collection Failure
COMMENT:
## CI is no longer failing! ✅
... | 23,786 |
https://github.com/scikit-learn/scikit-learn/issues/23786 | [
"Build / CI"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI is still failing on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=44957&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Jul 25, 2022)
- Test Collection Failure
COMMENT:
The last time this job failed w... | 23,786 |
https://github.com/scikit-learn/scikit-learn/issues/23786 | [
"Build / CI"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI is still failing on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=44957&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Jul 25, 2022)
- Test Collection Failure
COMMENT:
The failure still happens from ... | 23,786 |
https://github.com/scikit-learn/scikit-learn/issues/23786 | [
"Build / CI"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI is still failing on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=44957&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Jul 25, 2022)
- Test Collection Failure
COMMENT:
Looking a bit closer, it seems ... | 23,786 |
https://github.com/scikit-learn/scikit-learn/issues/23786 | [
"Build / CI"
] | ⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️
**CI is still failing on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=44957&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Jul 25, 2022)
- Test Collection Failure
COMMENT:
I'm closing this given #23994 i... | 23,786 |
https://github.com/scikit-learn/scikit-learn/issues/23779 | [
"Bug",
"module:multiclass"
] | `OneVsOneClassifier` does not accept custom input types
### Describe the bug
Due to the additional validation in #6626, `OneVsOneClassifier` cannot be used with custom types that work like arrays but cannot be converted to arrays. In my case I am the maintainer of [scikit-fda](https://fda.readthedocs.io/en/latest/), ... | 23,779 |
https://github.com/scikit-learn/scikit-learn/issues/23779 | [
"Bug",
"module:multiclass"
] | `OneVsOneClassifier` does not accept custom input types
### Describe the bug
Due to the additional validation in #6626, `OneVsOneClassifier` cannot be used with custom types that work like arrays but cannot be converted to arrays. In my case I am the maintainer of [scikit-fda](https://fda.readthedocs.io/en/latest/), ... | 23,779 |
https://github.com/scikit-learn/scikit-learn/issues/23779 | [
"Bug",
"module:multiclass"
] | `OneVsOneClassifier` does not accept custom input types
### Describe the bug
Due to the additional validation in #6626, `OneVsOneClassifier` cannot be used with custom types that work like arrays but cannot be converted to arrays. In my case I am the maintainer of [scikit-fda](https://fda.readthedocs.io/en/latest/), ... | 23,779 |
https://github.com/scikit-learn/scikit-learn/issues/23779 | [
"Bug",
"module:multiclass"
] | `OneVsOneClassifier` does not accept custom input types
### Describe the bug
Due to the additional validation in #6626, `OneVsOneClassifier` cannot be used with custom types that work like arrays but cannot be converted to arrays. In my case I am the maintainer of [scikit-fda](https://fda.readthedocs.io/en/latest/), ... | 23,779 |
https://github.com/scikit-learn/scikit-learn/issues/23779 | [
"Bug",
"module:multiclass"
] | `OneVsOneClassifier` does not accept custom input types
### Describe the bug
Due to the additional validation in #6626, `OneVsOneClassifier` cannot be used with custom types that work like arrays but cannot be converted to arrays. In my case I am the maintainer of [scikit-fda](https://fda.readthedocs.io/en/latest/), ... | 23,779 |
https://github.com/scikit-learn/scikit-learn/issues/23779 | [
"Bug",
"module:multiclass"
] | `OneVsOneClassifier` does not accept custom input types
### Describe the bug
Due to the additional validation in #6626, `OneVsOneClassifier` cannot be used with custom types that work like arrays but cannot be converted to arrays. In my case I am the maintainer of [scikit-fda](https://fda.readthedocs.io/en/latest/), ... | 23,779 |
https://github.com/scikit-learn/scikit-learn/issues/23769 | [
"Bug",
"Needs Triage"
] | Undefined behaviour while checking if a class variable contains a model
### Describe the bug
I want to check whether a class variable is None or has an actual model. When I do this with the RandomForestRegressor, it outputs a ```AttributeError: 'RandomForestRegressor' object has no attribute 'estimators_'```.
I h... | 23,769 |
https://github.com/scikit-learn/scikit-learn/issues/23769 | [
"Bug",
"Needs Triage"
] | Undefined behaviour while checking if a class variable contains a model
### Describe the bug
I want to check whether a class variable is None or has an actual model. When I do this with the RandomForestRegressor, it outputs a ```AttributeError: 'RandomForestRegressor' object has no attribute 'estimators_'```.
I h... | 23,769 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23767 | [
"New Feature",
"module:metrics",
"Needs Decision - Include Feature"
] | Additive Score/Metric decomposition into Miscalibration, Discrimination and Uncertainty
### Describe the workflow you want to enable
### Proposition
I'd like to decompose scores (at least the ones from consistent scoring functions for identifiable functionals) into meaningful additive components:
- MSC ≥ 0 miscal... | 23,767 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23758 | [
"New Feature",
"module:preprocessing",
"Needs Decision - Include Feature"
] | sklearn.preprocessing.StandardScaler() needs a degrees of freedom tunable parameter for scale_
### Describe the workflow you want to enable
I'm scaling a dataset using the standard procedure.
```
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
sc.transform(data)
```
... | 23,758 |
https://github.com/scikit-learn/scikit-learn/issues/23754 | [
"Bug"
] | ⚠️ CI failed on macOS.pylatest_conda_forge_mkl ⚠️
**CI failed on [macOS.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=43822&view=logs&j=97641769-79fb-5590-9088-a30ce9b850b9)**
- test_fastica_eigh_low_rank_warning[69]
COMMENT:
## CI is no longer failing! ✅
[Successfu... | 23,754 |
https://github.com/scikit-learn/scikit-learn/issues/23754 | [
"Bug"
] | ⚠️ CI failed on macOS.pylatest_conda_forge_mkl ⚠️
**CI failed on [macOS.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=43822&view=logs&j=97641769-79fb-5590-9088-a30ce9b850b9)**
- test_fastica_eigh_low_rank_warning[69]
COMMENT:
The test initially failed in `global_rand... | 23,754 |
https://github.com/scikit-learn/scikit-learn/issues/23754 | [
"Bug"
] | ⚠️ CI failed on macOS.pylatest_conda_forge_mkl ⚠️
**CI failed on [macOS.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=43822&view=logs&j=97641769-79fb-5590-9088-a30ce9b850b9)**
- test_fastica_eigh_low_rank_warning[69]
COMMENT:
## CI is no longer failing! ✅
[Successfu... | 23,754 |
https://github.com/scikit-learn/scikit-learn/issues/23753 | [
"Bug",
"module:linear_model"
] | Erreneous Avg. Loss calculation
https://github.com/scikit-learn/scikit-learn/blob/80598905e517759b4696c74ecc35c6e2eb508cff/sklearn/linear_model/_sgd_fast.pyx#L633-L636
The avg. loss should be `sumloss/count` instead of `sumloss/n_samples`. This is because the validation data points are ignored during loss calculati... | 23,753 |
https://github.com/scikit-learn/scikit-learn/issues/23753 | [
"Bug",
"module:linear_model"
] | Erreneous Avg. Loss calculation
https://github.com/scikit-learn/scikit-learn/blob/80598905e517759b4696c74ecc35c6e2eb508cff/sklearn/linear_model/_sgd_fast.pyx#L633-L636
The avg. loss should be `sumloss/count` instead of `sumloss/n_samples`. This is because the validation data points are ignored during loss calculati... | 23,753 |
https://github.com/scikit-learn/scikit-learn/issues/23753 | [
"Bug",
"module:linear_model"
] | Erreneous Avg. Loss calculation
https://github.com/scikit-learn/scikit-learn/blob/80598905e517759b4696c74ecc35c6e2eb508cff/sklearn/linear_model/_sgd_fast.pyx#L633-L636
The avg. loss should be `sumloss/count` instead of `sumloss/n_samples`. This is because the validation data points are ignored during loss calculati... | 23,753 |
https://github.com/scikit-learn/scikit-learn/issues/23750 | [
"Bug",
"module:datasets",
"Needs Investigation"
] | fetch_lfw_pairs issue
### Describe the bug
On two different machines for exactly same code, I am getting different result.
### Steps/Code to Reproduce
```py
from sklearn.datasets import fetch_lfw_pairs
from sklearn.utils import resample
X = fetch_lfw_pairs(
subset="test",
# funneled=False,
... | 23,750 |
https://github.com/scikit-learn/scikit-learn/issues/23750 | [
"Bug",
"module:datasets",
"Needs Investigation"
] | fetch_lfw_pairs issue
### Describe the bug
On two different machines for exactly same code, I am getting different result.
### Steps/Code to Reproduce
```py
from sklearn.datasets import fetch_lfw_pairs
from sklearn.utils import resample
X = fetch_lfw_pairs(
subset="test",
# funneled=False,
... | 23,750 |
https://github.com/scikit-learn/scikit-learn/issues/23750 | [
"Bug",
"module:datasets",
"Needs Investigation"
] | fetch_lfw_pairs issue
### Describe the bug
On two different machines for exactly same code, I am getting different result.
### Steps/Code to Reproduce
```py
from sklearn.datasets import fetch_lfw_pairs
from sklearn.utils import resample
X = fetch_lfw_pairs(
subset="test",
# funneled=False,
... | 23,750 |
https://github.com/scikit-learn/scikit-learn/issues/23750 | [
"Bug",
"module:datasets",
"Needs Investigation"
] | fetch_lfw_pairs issue
### Describe the bug
On two different machines for exactly same code, I am getting different result.
### Steps/Code to Reproduce
```py
from sklearn.datasets import fetch_lfw_pairs
from sklearn.utils import resample
X = fetch_lfw_pairs(
subset="test",
# funneled=False,
... | 23,750 |
https://github.com/scikit-learn/scikit-learn/issues/23749 | [
"API"
] | Make `penalty` parameter consistent for `None`
We are inconsistent with the `penalty` parameter for the linear models.
For the SGD and Perceptron, we will accept `penalty=None` while this is not the case in `LogisticRegression`. In `LogisticRegression`, we must provide the string `'none'`.
I assume we could make e... | 23,749 |
https://github.com/scikit-learn/scikit-learn/issues/23749 | [
"API"
] | Make `penalty` parameter consistent for `None`
We are inconsistent with the `penalty` parameter for the linear models.
For the SGD and Perceptron, we will accept `penalty=None` while this is not the case in `LogisticRegression`. In `LogisticRegression`, we must provide the string `'none'`.
I assume we could make e... | 23,749 |
https://github.com/scikit-learn/scikit-learn/issues/23749 | [
"API"
] | Make `penalty` parameter consistent for `None`
We are inconsistent with the `penalty` parameter for the linear models.
For the SGD and Perceptron, we will accept `penalty=None` while this is not the case in `LogisticRegression`. In `LogisticRegression`, we must provide the string `'none'`.
I assume we could make e... | 23,749 |
https://github.com/scikit-learn/scikit-learn/issues/23749 | [
"API"
] | Make `penalty` parameter consistent for `None`
We are inconsistent with the `penalty` parameter for the linear models.
For the SGD and Perceptron, we will accept `penalty=None` while this is not the case in `LogisticRegression`. In `LogisticRegression`, we must provide the string `'none'`.
I assume we could make e... | 23,749 |
https://github.com/scikit-learn/scikit-learn/issues/23744 | [
"New Feature"
] | Add set difference to `validate_parameter_constraints` `ValueError`
### Describe the workflow you want to enable
Currently the `ValueError` protuced by `validate_parameter_constraints` can get very long and unruly when there are a lot of validation parameters in either the passed constraints, or in the params to be v... | 23,744 |
https://github.com/scikit-learn/scikit-learn/issues/23744 | [
"New Feature"
] | Add set difference to `validate_parameter_constraints` `ValueError`
### Describe the workflow you want to enable
Currently the `ValueError` protuced by `validate_parameter_constraints` can get very long and unruly when there are a lot of validation parameters in either the passed constraints, or in the params to be v... | 23,744 |
https://github.com/scikit-learn/scikit-learn/issues/23735 | [
"Bug",
"Needs Triage"
] | Error after fitting pipeline: "This OneHotEncoder instance is not fitted yet"
### Describe the bug
I've trained a simple pipeline with a OneHotEncoder and a RandomForestClassifier model. **After fitting the pipeline**, I try to access the encoder's categories but the following error messages appears:
```pytb
NotF... | 23,735 |
https://github.com/scikit-learn/scikit-learn/issues/23733 | [
"New Feature",
"Needs Triage"
] | SimpleImputer does not implement `get_feature_names_out`
### Describe the workflow you want to enable
I noticed that [SimpleImputer](https://github.com/scikit-learn/scikit-learn/blob/159cb46c5672b0ba87d0ba80c6b80ec1aa5fda32/sklearn/impute/_base.py#L132) is still not supporting `get_feature_names_out`. In looking at ... | 23,733 |
https://github.com/scikit-learn/scikit-learn/issues/23732 | [
"New Feature",
"API",
"Needs Decision"
] | Add `n_jobs` and `random_state` to global config
### Describe the workflow you want to enable
I would like to be able to set `n_jobs=-1` in one place and have this take effect in any function with an `n_jobs` parameter. Same for `random_state`.
Perhaps there are other parameters that fit the theme: "if a user sets... | 23,732 |
https://github.com/scikit-learn/scikit-learn/issues/23732 | [
"New Feature",
"API",
"Needs Decision"
] | Add `n_jobs` and `random_state` to global config
### Describe the workflow you want to enable
I would like to be able to set `n_jobs=-1` in one place and have this take effect in any function with an `n_jobs` parameter. Same for `random_state`.
Perhaps there are other parameters that fit the theme: "if a user sets... | 23,732 |
https://github.com/scikit-learn/scikit-learn/issues/23732 | [
"New Feature",
"API",
"Needs Decision"
] | Add `n_jobs` and `random_state` to global config
### Describe the workflow you want to enable
I would like to be able to set `n_jobs=-1` in one place and have this take effect in any function with an `n_jobs` parameter. Same for `random_state`.
Perhaps there are other parameters that fit the theme: "if a user sets... | 23,732 |
https://github.com/scikit-learn/scikit-learn/issues/23728 | [
"Needs Decision"
] | [Meta] consistently break ties randomly in scikit-learn estimators (with random_state) in an unbiased way
Some estimators have arbitrary ways to break ties:
- k-nearest neighbors with data points lying on a uniform 1d grid (see #23667 for the BallTree for instance);
- tied splits on different features in histogram gr... | 23,728 |
https://github.com/scikit-learn/scikit-learn/issues/23728 | [
"Needs Decision"
] | [Meta] consistently break ties randomly in scikit-learn estimators (with random_state) in an unbiased way
Some estimators have arbitrary ways to break ties:
- k-nearest neighbors with data points lying on a uniform 1d grid (see #23667 for the BallTree for instance);
- tied splits on different features in histogram gr... | 23,728 |
https://github.com/scikit-learn/scikit-learn/issues/23728 | [
"Needs Decision"
] | [Meta] consistently break ties randomly in scikit-learn estimators (with random_state) in an unbiased way
Some estimators have arbitrary ways to break ties:
- k-nearest neighbors with data points lying on a uniform 1d grid (see #23667 for the BallTree for instance);
- tied splits on different features in histogram gr... | 23,728 |
https://github.com/scikit-learn/scikit-learn/issues/23728 | [
"Needs Decision"
] | [Meta] consistently break ties randomly in scikit-learn estimators (with random_state) in an unbiased way
Some estimators have arbitrary ways to break ties:
- k-nearest neighbors with data points lying on a uniform 1d grid (see #23667 for the BallTree for instance);
- tied splits on different features in histogram gr... | 23,728 |
https://github.com/scikit-learn/scikit-learn/issues/23728 | [
"Needs Decision"
] | [Meta] consistently break ties randomly in scikit-learn estimators (with random_state) in an unbiased way
Some estimators have arbitrary ways to break ties:
- k-nearest neighbors with data points lying on a uniform 1d grid (see #23667 for the BallTree for instance);
- tied splits on different features in histogram gr... | 23,728 |
https://github.com/scikit-learn/scikit-learn/issues/23727 | [
"RFC",
"Needs Investigation"
] | [RFC] WASM / pyodide as a (somewhat) officially supported platform for scikit-learn
We started having bug reports (at least one indirect, in real life report at a conference: #23707) from users of scikit-learn in WASM environment (e.g. pyodide / jupyterlite, pyscript...).
Shall we invest effort in setting CI toolin... | 23,727 |
https://github.com/scikit-learn/scikit-learn/issues/23727 | [
"RFC",
"Needs Investigation"
] | [RFC] WASM / pyodide as a (somewhat) officially supported platform for scikit-learn
We started having bug reports (at least one indirect, in real life report at a conference: #23707) from users of scikit-learn in WASM environment (e.g. pyodide / jupyterlite, pyscript...).
Shall we invest effort in setting CI toolin... | 23,727 |
https://github.com/scikit-learn/scikit-learn/issues/23727 | [
"RFC",
"Needs Investigation"
] | [RFC] WASM / pyodide as a (somewhat) officially supported platform for scikit-learn
We started having bug reports (at least one indirect, in real life report at a conference: #23707) from users of scikit-learn in WASM environment (e.g. pyodide / jupyterlite, pyscript...).
Shall we invest effort in setting CI toolin... | 23,727 |
https://github.com/scikit-learn/scikit-learn/issues/23727 | [
"RFC",
"Needs Investigation"
] | [RFC] WASM / pyodide as a (somewhat) officially supported platform for scikit-learn
We started having bug reports (at least one indirect, in real life report at a conference: #23707) from users of scikit-learn in WASM environment (e.g. pyodide / jupyterlite, pyscript...).
Shall we invest effort in setting CI toolin... | 23,727 |
https://github.com/scikit-learn/scikit-learn/issues/23727 | [
"RFC",
"Needs Investigation"
] | [RFC] WASM / pyodide as a (somewhat) officially supported platform for scikit-learn
We started having bug reports (at least one indirect, in real life report at a conference: #23707) from users of scikit-learn in WASM environment (e.g. pyodide / jupyterlite, pyscript...).
Shall we invest effort in setting CI toolin... | 23,727 |
https://github.com/scikit-learn/scikit-learn/issues/23727 | [
"RFC",
"Needs Investigation"
] | [RFC] WASM / pyodide as a (somewhat) officially supported platform for scikit-learn
We started having bug reports (at least one indirect, in real life report at a conference: #23707) from users of scikit-learn in WASM environment (e.g. pyodide / jupyterlite, pyscript...).
Shall we invest effort in setting CI toolin... | 23,727 |
https://github.com/scikit-learn/scikit-learn/issues/23727 | [
"RFC",
"Needs Investigation"
] | [RFC] WASM / pyodide as a (somewhat) officially supported platform for scikit-learn
We started having bug reports (at least one indirect, in real life report at a conference: #23707) from users of scikit-learn in WASM environment (e.g. pyodide / jupyterlite, pyscript...).
Shall we invest effort in setting CI toolin... | 23,727 |
https://github.com/scikit-learn/scikit-learn/issues/23720 | [
"Bug",
"module:feature_selection"
] | Symmetry of `mutual_info_classif` and `mutual_info_regression`.
### Describe the bug
Suppose we have realizations of a continuous variable `c` and discrete variable `d` from a joint distribution `p(c, d)`. I expected that `mutual_info_classif(c, d, discrete_features=[False])` and `mutual_info_regression(d, c, discret... | 23,720 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23709 | [
"New Feature",
"module:calibration"
] | Enhance calibration plots
### Describe the workflow you want to enable
I would like to enhance calibration plots by adding these new aspects:
1. Plot bins as vertical lines in the background.
2. Equal aspect on both axis to have a square figure and enable easier visual comparisons.
3. Changing xlabel from "Mean ... | 23,709 |
https://github.com/scikit-learn/scikit-learn/issues/23707 | [
"Bug"
] | TSNE is broken in pyodide
While testing `pyscript` and thus `pyodide`, it seems that `TSNE` is broken.
The reason is that we are specifically using 32 bits internally that do not work with `pyodide`.
I think that we should make sure that `TSNE` preserve dtype in 64 bits and 32 bits without silently reducing the nume... | 23,707 |
https://github.com/scikit-learn/scikit-learn/issues/23707 | [
"Bug"
] | TSNE is broken in pyodide
While testing `pyscript` and thus `pyodide`, it seems that `TSNE` is broken.
The reason is that we are specifically using 32 bits internally that do not work with `pyodide`.
I think that we should make sure that `TSNE` preserve dtype in 64 bits and 32 bits without silently reducing the nume... | 23,707 |
https://github.com/scikit-learn/scikit-learn/issues/23707 | [
"Bug"
] | TSNE is broken in pyodide
While testing `pyscript` and thus `pyodide`, it seems that `TSNE` is broken.
The reason is that we are specifically using 32 bits internally that do not work with `pyodide`.
I think that we should make sure that `TSNE` preserve dtype in 64 bits and 32 bits without silently reducing the nume... | 23,707 |
https://github.com/scikit-learn/scikit-learn/issues/23707 | [
"Bug"
] | TSNE is broken in pyodide
While testing `pyscript` and thus `pyodide`, it seems that `TSNE` is broken.
The reason is that we are specifically using 32 bits internally that do not work with `pyodide`.
I think that we should make sure that `TSNE` preserve dtype in 64 bits and 32 bits without silently reducing the nume... | 23,707 |
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