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/23628 | [
"Question"
] | Running pipeline.fit automatically runs pipeline.transform
### Describe the bug
Currently, when I run:
`pipeline.fit(X, y)`
all of pipeline transformers transform functions are being executed.
I wanted to know if there is a way to run `pipeline.fit(X, y)` and only the fit functions, then later run `pipeline.trans... | 23,628 |
https://github.com/scikit-learn/scikit-learn/issues/23628 | [
"Question"
] | Running pipeline.fit automatically runs pipeline.transform
### Describe the bug
Currently, when I run:
`pipeline.fit(X, y)`
all of pipeline transformers transform functions are being executed.
I wanted to know if there is a way to run `pipeline.fit(X, y)` and only the fit functions, then later run `pipeline.trans... | 23,628 |
https://github.com/scikit-learn/scikit-learn/issues/23628 | [
"Question"
] | Running pipeline.fit automatically runs pipeline.transform
### Describe the bug
Currently, when I run:
`pipeline.fit(X, y)`
all of pipeline transformers transform functions are being executed.
I wanted to know if there is a way to run `pipeline.fit(X, y)` and only the fit functions, then later run `pipeline.trans... | 23,628 |
https://github.com/scikit-learn/scikit-learn/issues/23627 | [
"Question"
] | Anomalous result in PLS
### Describe the bug
In the [Cross Decomposition PLS Regression code](https://github.com/scikit-learn/scikit-learn/blob/80598905e/sklearn/cross_decomposition/_pls.py), I get an anomalous result.
Expected that the dot product would give a 0. But instead it gives a small negative number.
... | 23,627 |
https://github.com/scikit-learn/scikit-learn/issues/23627 | [
"Question"
] | Anomalous result in PLS
### Describe the bug
In the [Cross Decomposition PLS Regression code](https://github.com/scikit-learn/scikit-learn/blob/80598905e/sklearn/cross_decomposition/_pls.py), I get an anomalous result.
Expected that the dot product would give a 0. But instead it gives a small negative number.
... | 23,627 |
https://github.com/scikit-learn/scikit-learn/issues/23626 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=45637&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Aug 16, 2022)
- test_grid_search_failing_classifier
- test_searchcv_r... | 23,626 |
https://github.com/scikit-learn/scikit-learn/issues/23626 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=45637&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Aug 16, 2022)
- test_grid_search_failing_classifier
- test_searchcv_r... | 23,626 |
https://github.com/scikit-learn/scikit-learn/issues/23626 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=45637&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Aug 16, 2022)
- test_grid_search_failing_classifier
- test_searchcv_r... | 23,626 |
https://github.com/scikit-learn/scikit-learn/issues/23626 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=45637&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Aug 16, 2022)
- test_grid_search_failing_classifier
- test_searchcv_r... | 23,626 |
https://github.com/scikit-learn/scikit-learn/issues/23626 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=45637&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)** (Aug 16, 2022)
- test_grid_search_failing_classifier
- test_searchcv_r... | 23,626 |
https://github.com/scikit-learn/scikit-learn/issues/23614 | [
"Build / CI"
] | 800+ test failures in scipy-dev build
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=43215&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf&t=ef785ae2-496b-5b02-9f0e-07a6c3ab3081
This deserves further investigation ...
Looks like scipy changed from 1.9dev to 1.10dev so maybe the scipy whe... | 23,614 |
https://github.com/scikit-learn/scikit-learn/issues/23614 | [
"Build / CI"
] | 800+ test failures in scipy-dev build
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=43215&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf&t=ef785ae2-496b-5b02-9f0e-07a6c3ab3081
This deserves further investigation ...
Looks like scipy changed from 1.9dev to 1.10dev so maybe the scipy whe... | 23,614 |
https://github.com/scikit-learn/scikit-learn/issues/23612 | [
"Documentation",
"Needs Triage"
] | Mention factor x2 between MAE and mean pinball loss
### Describe the issue linked to the documentation
In the documentation, we mention that the mean pinball loss with `alpha=0.5` is equal to the mean absolute error.
With a small reproducer, it seems that we have a factor x2:
```python
import numpy as np
from... | 23,612 |
https://github.com/scikit-learn/scikit-learn/issues/23610 | [
"Question"
] | .pyx and .pxd files are getting imported in my .py file
### Describe the bug
I was trying to contribute for one of the feature, while doing the debug, i encountered an import error. When i tried to analyse, it was .pyx and .pyd file. Could anyone help me how to fix this?(Note:- i didn't pip install the sklearn.)
##... | 23,610 |
https://github.com/scikit-learn/scikit-learn/issues/23610 | [
"Question"
] | .pyx and .pxd files are getting imported in my .py file
### Describe the bug
I was trying to contribute for one of the feature, while doing the debug, i encountered an import error. When i tried to analyse, it was .pyx and .pyd file. Could anyone help me how to fix this?(Note:- i didn't pip install the sklearn.)
##... | 23,610 |
https://github.com/scikit-learn/scikit-learn/issues/23610 | [
"Question"
] | .pyx and .pxd files are getting imported in my .py file
### Describe the bug
I was trying to contribute for one of the feature, while doing the debug, i encountered an import error. When i tried to analyse, it was .pyx and .pyd file. Could anyone help me how to fix this?(Note:- i didn't pip install the sklearn.)
##... | 23,610 |
https://github.com/scikit-learn/scikit-learn/issues/23605 | [
"Bug"
] | LogisticRegression fails in sklearn 1.1.1 with newton-cg solver when X only contains one predictor
### Describe the bug
I think this is related to https://github.com/scikit-learn/scikit-learn/pull/21808
As explained in the title, this bug appeared in version 1.1.1. `LogisticRegression(solver="newton-cg")` fails wh... | 23,605 |
https://github.com/scikit-learn/scikit-learn/issues/23599 | [
"Bug",
"Needs Triage"
] | sklearn.utils._param_validation._InstancesOf is insufficient for numpy data types
### Describe the bug
Numpy data types can be constructed in different ways. Although they result in the same data type, isinstance() yields different results based on the way the data type has been constructed.
```
>>> isinstance(np.f... | 23,599 |
https://github.com/scikit-learn/scikit-learn/issues/23599 | [
"Bug",
"Needs Triage"
] | sklearn.utils._param_validation._InstancesOf is insufficient for numpy data types
### Describe the bug
Numpy data types can be constructed in different ways. Although they result in the same data type, isinstance() yields different results based on the way the data type has been constructed.
```
>>> isinstance(np.f... | 23,599 |
https://github.com/scikit-learn/scikit-learn/issues/23596 | [
"Bug",
"module:test-suite"
] | sklearn.externals._lobpcg.lobpcg throws ValueError in test_function_docstring
### Describe the bug
I cloned and installed the current main branch from the repository and ran `pytest sklearn` out of curiosity and encountered a ValueError for the function `sklearn.externals._lobpcg.lobpcg`. Further examination reveal... | 23,596 |
https://github.com/scikit-learn/scikit-learn/issues/23596 | [
"Bug",
"module:test-suite"
] | sklearn.externals._lobpcg.lobpcg throws ValueError in test_function_docstring
### Describe the bug
I cloned and installed the current main branch from the repository and ran `pytest sklearn` out of curiosity and encountered a ValueError for the function `sklearn.externals._lobpcg.lobpcg`. Further examination reveal... | 23,596 |
https://github.com/scikit-learn/scikit-learn/issues/23596 | [
"Bug",
"module:test-suite"
] | sklearn.externals._lobpcg.lobpcg throws ValueError in test_function_docstring
### Describe the bug
I cloned and installed the current main branch from the repository and ran `pytest sklearn` out of curiosity and encountered a ValueError for the function `sklearn.externals._lobpcg.lobpcg`. Further examination reveal... | 23,596 |
https://github.com/scikit-learn/scikit-learn/issues/23596 | [
"Bug",
"module:test-suite"
] | sklearn.externals._lobpcg.lobpcg throws ValueError in test_function_docstring
### Describe the bug
I cloned and installed the current main branch from the repository and ran `pytest sklearn` out of curiosity and encountered a ValueError for the function `sklearn.externals._lobpcg.lobpcg`. Further examination reveal... | 23,596 |
https://github.com/scikit-learn/scikit-learn/issues/23596 | [
"Bug",
"module:test-suite"
] | sklearn.externals._lobpcg.lobpcg throws ValueError in test_function_docstring
### Describe the bug
I cloned and installed the current main branch from the repository and ran `pytest sklearn` out of curiosity and encountered a ValueError for the function `sklearn.externals._lobpcg.lobpcg`. Further examination reveal... | 23,596 |
https://github.com/scikit-learn/scikit-learn/issues/23596 | [
"Bug",
"module:test-suite"
] | sklearn.externals._lobpcg.lobpcg throws ValueError in test_function_docstring
### Describe the bug
I cloned and installed the current main branch from the repository and ran `pytest sklearn` out of curiosity and encountered a ValueError for the function `sklearn.externals._lobpcg.lobpcg`. Further examination reveal... | 23,596 |
https://github.com/scikit-learn/scikit-learn/issues/23596 | [
"Bug",
"module:test-suite"
] | sklearn.externals._lobpcg.lobpcg throws ValueError in test_function_docstring
### Describe the bug
I cloned and installed the current main branch from the repository and ran `pytest sklearn` out of curiosity and encountered a ValueError for the function `sklearn.externals._lobpcg.lobpcg`. Further examination reveal... | 23,596 |
https://github.com/scikit-learn/scikit-learn/issues/23596 | [
"Bug",
"module:test-suite"
] | sklearn.externals._lobpcg.lobpcg throws ValueError in test_function_docstring
### Describe the bug
I cloned and installed the current main branch from the repository and ran `pytest sklearn` out of curiosity and encountered a ValueError for the function `sklearn.externals._lobpcg.lobpcg`. Further examination reveal... | 23,596 |
https://github.com/scikit-learn/scikit-learn/issues/23584 | [
"New Feature",
"Needs Triage"
] | [dbscan] [enhancement] Raise warning when dealing with numbers susceptible to precision errors
### Describe the workflow you want to enable
You have some time series data that you wish to run through dbscan, only to find that your eps value is much lower than what produces expected results.
Consider
```
(Pdb) ... | 23,584 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23574 | [
"Bug",
"Documentation",
"help wanted",
"module:cluster",
"OS:macOS"
] | Segmentation Fault in KMeans on OSX
### Describe the bug
Hi when I run this code
```
import numpy as np
from sklearn.cluster import KMeans
X_train = np.random.RandomState(0).random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)
```
EDIT by @ogrisel: inserted `.RandomState(0)`... | 23,574 |
https://github.com/scikit-learn/scikit-learn/issues/23571 | [
"Bug",
"Needs Triage"
] | Yeo-Johnson Transformer
### Describe the bug
Yeo-Johnson Transformer converges to a transformer that produces a 0 array. I think this happens because the MLE of the Yeo-Johnson converges to a lambda which produces 0 variance transformations. The log-likelihood function is inf for this value of lambda, hence this la... | 23,571 |
https://github.com/scikit-learn/scikit-learn/issues/23571 | [
"Bug",
"Needs Triage"
] | Yeo-Johnson Transformer
### Describe the bug
Yeo-Johnson Transformer converges to a transformer that produces a 0 array. I think this happens because the MLE of the Yeo-Johnson converges to a lambda which produces 0 variance transformations. The log-likelihood function is inf for this value of lambda, hence this la... | 23,571 |
https://github.com/scikit-learn/scikit-learn/issues/23568 | [
"New Feature",
"module:utils"
] | Allow subclasses to pass through check_array()
### Describe the workflow you want to enable
I have a subclass of `numpy.ndarray` which keeps track of axis labels: which axes represent time, spatial directions, independent samples, and response variables. I have a `transform()` step in my pipeline that runs
`x = c... | 23,568 |
https://github.com/scikit-learn/scikit-learn/issues/23568 | [
"New Feature",
"module:utils"
] | Allow subclasses to pass through check_array()
### Describe the workflow you want to enable
I have a subclass of `numpy.ndarray` which keeps track of axis labels: which axes represent time, spatial directions, independent samples, and response variables. I have a `transform()` step in my pipeline that runs
`x = c... | 23,568 |
https://github.com/scikit-learn/scikit-learn/issues/23567 | [
"New Feature",
"module:model_selection"
] | Include print counter for HalvingGridSearch in verbose=3 mode
### Describe the workflow you want to enable
Dear all,
when using HalvingGridSearch it is very likely, that (especially the first iteration) it contains 100's to 1000's of grid points. While monitoring in the verbose mode can be nice to get an idea abou... | 23,567 |
https://github.com/scikit-learn/scikit-learn/issues/23566 | [
"Bug",
"module:cluster"
] | `silhouette_score` fails whit `"k-means++"` init on very sparse data
### Describe the bug
Not sure this is a bug or something that just need to be better documented.
The `"k-means++"` init on very sparse data can select initial centroids than never get updated and this cause problems in the silhouette clustering... | 23,566 |
https://github.com/scikit-learn/scikit-learn/issues/23566 | [
"Bug",
"module:cluster"
] | `silhouette_score` fails whit `"k-means++"` init on very sparse data
### Describe the bug
Not sure this is a bug or something that just need to be better documented.
The `"k-means++"` init on very sparse data can select initial centroids than never get updated and this cause problems in the silhouette clustering... | 23,566 |
https://github.com/scikit-learn/scikit-learn/issues/23566 | [
"Bug",
"module:cluster"
] | `silhouette_score` fails whit `"k-means++"` init on very sparse data
### Describe the bug
Not sure this is a bug or something that just need to be better documented.
The `"k-means++"` init on very sparse data can select initial centroids than never get updated and this cause problems in the silhouette clustering... | 23,566 |
https://github.com/scikit-learn/scikit-learn/issues/23566 | [
"Bug",
"module:cluster"
] | `silhouette_score` fails whit `"k-means++"` init on very sparse data
### Describe the bug
Not sure this is a bug or something that just need to be better documented.
The `"k-means++"` init on very sparse data can select initial centroids than never get updated and this cause problems in the silhouette clustering... | 23,566 |
https://github.com/scikit-learn/scikit-learn/issues/23566 | [
"Bug",
"module:cluster"
] | `silhouette_score` fails whit `"k-means++"` init on very sparse data
### Describe the bug
Not sure this is a bug or something that just need to be better documented.
The `"k-means++"` init on very sparse data can select initial centroids than never get updated and this cause problems in the silhouette clustering... | 23,566 |
https://github.com/scikit-learn/scikit-learn/issues/23554 | [
"New Feature",
"module:model_selection",
"Needs Reproducible Code",
"Metadata Routing"
] | passing cross validation data to custom score function in GridSearchCV
### Describe the workflow you want to enable
Custom score function some time need the feature values also but there is no way we could do this .We can not the use same dataset which we have used for gridsearchcv ,reason is the data we sent will be... | 23,554 |
https://github.com/scikit-learn/scikit-learn/issues/23554 | [
"New Feature",
"module:model_selection",
"Needs Reproducible Code",
"Metadata Routing"
] | passing cross validation data to custom score function in GridSearchCV
### Describe the workflow you want to enable
Custom score function some time need the feature values also but there is no way we could do this .We can not the use same dataset which we have used for gridsearchcv ,reason is the data we sent will be... | 23,554 |
https://github.com/scikit-learn/scikit-learn/issues/23554 | [
"New Feature",
"module:model_selection",
"Needs Reproducible Code",
"Metadata Routing"
] | passing cross validation data to custom score function in GridSearchCV
### Describe the workflow you want to enable
Custom score function some time need the feature values also but there is no way we could do this .We can not the use same dataset which we have used for gridsearchcv ,reason is the data we sent will be... | 23,554 |
https://github.com/scikit-learn/scikit-learn/issues/23554 | [
"New Feature",
"module:model_selection",
"Needs Reproducible Code",
"Metadata Routing"
] | passing cross validation data to custom score function in GridSearchCV
### Describe the workflow you want to enable
Custom score function some time need the feature values also but there is no way we could do this .We can not the use same dataset which we have used for gridsearchcv ,reason is the data we sent will be... | 23,554 |
https://github.com/scikit-learn/scikit-learn/issues/23554 | [
"New Feature",
"module:model_selection",
"Needs Reproducible Code",
"Metadata Routing"
] | passing cross validation data to custom score function in GridSearchCV
### Describe the workflow you want to enable
Custom score function some time need the feature values also but there is no way we could do this .We can not the use same dataset which we have used for gridsearchcv ,reason is the data we sent will be... | 23,554 |
https://github.com/scikit-learn/scikit-learn/issues/23550 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | `distance_threshold` not respected in `AgglomerativeClustering` with sparse `connectivity`
### Describe the bug
When passing a sparse `connectivity` to `AgglomerativeClustering` constrained by a given `distance_threshold`, the current implementation may return a clustering not respecting this constraint.
This is... | 23,550 |
https://github.com/scikit-learn/scikit-learn/issues/23550 | [
"Bug",
"module:cluster",
"Needs Investigation"
] | `distance_threshold` not respected in `AgglomerativeClustering` with sparse `connectivity`
### Describe the bug
When passing a sparse `connectivity` to `AgglomerativeClustering` constrained by a given `distance_threshold`, the current implementation may return a clustering not respecting this constraint.
This is... | 23,550 |
https://github.com/scikit-learn/scikit-learn/issues/23547 | [
"Documentation",
"module:ensemble",
"module:tree"
] | Tree Based model max_features when setting float values is not from rounded number.
### Describe the bug
In the documentation, for the tree based model such as ExtraTrees, it says for the float max_features:
- If float, then max_features is a fraction and round(max_features * n_features) features are considered... | 23,547 |
https://github.com/scikit-learn/scikit-learn/issues/23547 | [
"Documentation",
"module:ensemble",
"module:tree"
] | Tree Based model max_features when setting float values is not from rounded number.
### Describe the bug
In the documentation, for the tree based model such as ExtraTrees, it says for the float max_features:
- If float, then max_features is a fraction and round(max_features * n_features) features are considered... | 23,547 |
https://github.com/scikit-learn/scikit-learn/issues/23547 | [
"Documentation",
"module:ensemble",
"module:tree"
] | Tree Based model max_features when setting float values is not from rounded number.
### Describe the bug
In the documentation, for the tree based model such as ExtraTrees, it says for the float max_features:
- If float, then max_features is a fraction and round(max_features * n_features) features are considered... | 23,547 |
https://github.com/scikit-learn/scikit-learn/issues/23537 | [
"Bug",
"module:feature_extraction"
] | SelectFromModel threshold for ElasticNet is wrong
### Describe the bug
As an estimator with an L1 penalty, ElasticNet should default to 1e-5 as the selection threshold. SelectFromModel code does not account for this, since ElasticNet has no penalty parameter, and is not called *LASSO*.
https://github.com/scikit-le... | 23,537 |
https://github.com/scikit-learn/scikit-learn/issues/23537 | [
"Bug",
"module:feature_extraction"
] | SelectFromModel threshold for ElasticNet is wrong
### Describe the bug
As an estimator with an L1 penalty, ElasticNet should default to 1e-5 as the selection threshold. SelectFromModel code does not account for this, since ElasticNet has no penalty parameter, and is not called *LASSO*.
https://github.com/scikit-le... | 23,537 |
https://github.com/scikit-learn/scikit-learn/issues/23537 | [
"Bug",
"module:feature_extraction"
] | SelectFromModel threshold for ElasticNet is wrong
### Describe the bug
As an estimator with an L1 penalty, ElasticNet should default to 1e-5 as the selection threshold. SelectFromModel code does not account for this, since ElasticNet has no penalty parameter, and is not called *LASSO*.
https://github.com/scikit-le... | 23,537 |
https://github.com/scikit-learn/scikit-learn/issues/23537 | [
"Bug",
"module:feature_extraction"
] | SelectFromModel threshold for ElasticNet is wrong
### Describe the bug
As an estimator with an L1 penalty, ElasticNet should default to 1e-5 as the selection threshold. SelectFromModel code does not account for this, since ElasticNet has no penalty parameter, and is not called *LASSO*.
https://github.com/scikit-le... | 23,537 |
https://github.com/scikit-learn/scikit-learn/issues/23537 | [
"Bug",
"module:feature_extraction"
] | SelectFromModel threshold for ElasticNet is wrong
### Describe the bug
As an estimator with an L1 penalty, ElasticNet should default to 1e-5 as the selection threshold. SelectFromModel code does not account for this, since ElasticNet has no penalty parameter, and is not called *LASSO*.
https://github.com/scikit-le... | 23,537 |
https://github.com/scikit-learn/scikit-learn/issues/23533 | [
"Bug",
"module:feature_selection"
] | RFECV race condition on estimator
In RFECV, at
https://github.com/scikit-learn/scikit-learn/blob/fb3ed90fb501a755ce2938fb566bd0f6e2235054/sklearn/feature_selection/_rfe.py#L723-L726
the estimator is passed as-is to the fit function. Since `fit` modifies the object without copying, this is prone to race conditions (s... | 23,533 |
https://github.com/scikit-learn/scikit-learn/issues/23533 | [
"Bug",
"module:feature_selection"
] | RFECV race condition on estimator
In RFECV, at
https://github.com/scikit-learn/scikit-learn/blob/fb3ed90fb501a755ce2938fb566bd0f6e2235054/sklearn/feature_selection/_rfe.py#L723-L726
the estimator is passed as-is to the fit function. Since `fit` modifies the object without copying, this is prone to race conditions (s... | 23,533 |
https://github.com/scikit-learn/scikit-learn/issues/23533 | [
"Bug",
"module:feature_selection"
] | RFECV race condition on estimator
In RFECV, at
https://github.com/scikit-learn/scikit-learn/blob/fb3ed90fb501a755ce2938fb566bd0f6e2235054/sklearn/feature_selection/_rfe.py#L723-L726
the estimator is passed as-is to the fit function. Since `fit` modifies the object without copying, this is prone to race conditions (s... | 23,533 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23531 | [
"Needs Triage"
] | ⚠️ CI failed on Linux_Nightly.pylatest_pip_scipy_dev ⚠️
**CI is still failing on [Linux_Nightly.pylatest_pip_scipy_dev](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=42983&view=logs&j=dfe99b15-50db-5d7b-b1e9-4105c42527cf)**
- test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs... | 23,531 |
https://github.com/scikit-learn/scikit-learn/issues/23527 | [
"Documentation",
"Needs Triage"
] | Confusion regarding BinomialDeviance
### Describe the issue linked to the documentation
The [BinomialDeviance](https://github.com/scikit-learn/scikit-learn/blob/80598905e517759b4696c74ecc35c6e2eb508cff/sklearn/ensemble/_gb_losses.py#L633) states that it computes the `Compute the deviance (= 2 * negative log-likelih... | 23,527 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
https://github.com/scikit-learn/scikit-learn/issues/23525 | [
"RFC"
] | RFC classifiers trained by minimizing the Brier loss
At the moment, our probablistic classifiers (e.g. logistic regression and gradient boosted trees) optimize the log loss, typically after taking a sigmoid or softmax inverse link function (typically part of the Cython loss).
However the log-loss is not the only pr... | 23,525 |
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