html_url stringlengths 57 57 | labels listlengths 1 6 | text stringlengths 32 258k | issue_number int64 22.4k 33k | embedding listlengths 768 768 |
|---|---|---|---|---|
https://github.com/scikit-learn/scikit-learn/issues/30213 | [
"New Feature",
"module:gaussian_process",
"Needs Investigation"
] | Tuning `alpha` in `GaussianProcessRegressor`
### Describe the workflow you want to enable
In the [GaussianProcessRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html), `alpha` stands for the likelihood variance of the targets given the inputs: $Y = f(X)... | 30,213 | [
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0.0011288727400824428,
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0.0023624738678336143,
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0.00858269538730383,
0.029518477618694305,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30213 | [
"New Feature",
"module:gaussian_process",
"Needs Investigation"
] | Tuning `alpha` in `GaussianProcessRegressor`
### Describe the workflow you want to enable
In the [GaussianProcessRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html), `alpha` stands for the likelihood variance of the targets given the inputs: $Y = f(X)... | 30,213 | [
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0.008524788543581963,
0.05244232714176178,
... |
https://github.com/scikit-learn/scikit-learn/issues/30213 | [
"New Feature",
"module:gaussian_process",
"Needs Investigation"
] | Tuning `alpha` in `GaussianProcessRegressor`
### Describe the workflow you want to enable
In the [GaussianProcessRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html), `alpha` stands for the likelihood variance of the targets given the inputs: $Y = f(X)... | 30,213 | [
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0.007089703343808651,
0.0029520075768232346,
0.00837868545204401,
0.036658234894275665,
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https://github.com/scikit-learn/scikit-learn/issues/30212 | [
"Documentation",
"Needs Triage"
] | Missing documentation on ConvergenceWarning?
### Describe the issue linked to the documentation
Hi!
I was looking to know more about the convergence warning, I found [this link](https://scikit-learn.org/1.5/modules/generated/sklearn.exceptions.ConvergenceWarning.html), which redirects towards sklearn.utils. However,... | 30,212 | [
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0.010813356377184391,
0.024205287918448448,
-... |
https://github.com/scikit-learn/scikit-learn/issues/30212 | [
"Documentation",
"Needs Triage"
] | Missing documentation on ConvergenceWarning?
### Describe the issue linked to the documentation
Hi!
I was looking to know more about the convergence warning, I found [this link](https://scikit-learn.org/1.5/modules/generated/sklearn.exceptions.ConvergenceWarning.html), which redirects towards sklearn.utils. However,... | 30,212 | [
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0.0225137360394001,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30199 | [
"New Feature",
"Needs Triage"
] | Add "mish" activation function to sklearn.neural_network.MLPClassifier and make it the default
### Describe the workflow you want to enable
Currently, the default activation function for `sklearn.neural_network.MLPClassifier` is "relu". However, there are several papers that demonstrate better results with "mish" =... | 30,199 | [
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0.03064228966832161,
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0.01495702937245369,
-0.009407991543412209,
0.068... |
https://github.com/scikit-learn/scikit-learn/issues/30197 | [
"Bug"
] | Exception on rendering html empty pipeline
### Describe the bug
Rendering empty pipeline to html fails, and just simply displaying an empty pipeline fails on IPython/Jupyter.
See upstream IPython issue:
https://github.com/ipython/ipython/issues/14568
### Steps/Code to Reproduce
```python
>>> from sklea... | 30,197 | [
0.004704207181930542,
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0.012975074350833893,
-0.028297778218984604,
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0.010971768759191036,
0.017477696761488914,
0.06708887964487076,
0.05767185613512993,
-0.04002654179930687,
0.008217869326472282,
0.016174163669347763,
0.022822948172688484,
0.030... |
https://github.com/scikit-learn/scikit-learn/issues/30197 | [
"Bug"
] | Exception on rendering html empty pipeline
### Describe the bug
Rendering empty pipeline to html fails, and just simply displaying an empty pipeline fails on IPython/Jupyter.
See upstream IPython issue:
https://github.com/ipython/ipython/issues/14568
### Steps/Code to Reproduce
```python
>>> from sklea... | 30,197 | [
0.004704207181930542,
-0.013208183459937572,
0.012975074350833893,
-0.028297778218984604,
0.09306937456130981,
0.010971768759191036,
0.017477696761488914,
0.06708887964487076,
0.05767185613512993,
-0.04002654179930687,
0.008217869326472282,
0.016174163669347763,
0.022822948172688484,
0.030... |
https://github.com/scikit-learn/scikit-learn/issues/30197 | [
"Bug"
] | Exception on rendering html empty pipeline
### Describe the bug
Rendering empty pipeline to html fails, and just simply displaying an empty pipeline fails on IPython/Jupyter.
See upstream IPython issue:
https://github.com/ipython/ipython/issues/14568
### Steps/Code to Reproduce
```python
>>> from sklea... | 30,197 | [
0.004704207181930542,
-0.013208183459937572,
0.012975074350833893,
-0.028297778218984604,
0.09306937456130981,
0.010971768759191036,
0.017477696761488914,
0.06708887964487076,
0.05767185613512993,
-0.04002654179930687,
0.008217869326472282,
0.016174163669347763,
0.022822948172688484,
0.030... |
https://github.com/scikit-learn/scikit-learn/issues/30195 | [
"Documentation",
"Build / CI"
] | issue in building from source with Windows64 Python 3.12.7
### Describe the bug
I am currently following the guide on [building from source](https://scikit-learn.org/dev/developers/advanced_installation.html) to create an editable build of scikit-learn. However, I encountered some errors during the process. Any hel... | 30,195 | [
0.01904744654893875,
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0.07115557044744492,
0.020001403987407684,
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0.0019300823332741857,
0.0990668311715126,
0.0006763365236110985,... |
https://github.com/scikit-learn/scikit-learn/issues/30195 | [
"Documentation",
"Build / CI"
] | issue in building from source with Windows64 Python 3.12.7
### Describe the bug
I am currently following the guide on [building from source](https://scikit-learn.org/dev/developers/advanced_installation.html) to create an editable build of scikit-learn. However, I encountered some errors during the process. Any hel... | 30,195 | [
0.01904744654893875,
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0.0015712192980572581,
0.0019300823332741857,
0.0990668311715126,
0.0006763365236110985,... |
https://github.com/scikit-learn/scikit-learn/issues/30195 | [
"Documentation",
"Build / CI"
] | issue in building from source with Windows64 Python 3.12.7
### Describe the bug
I am currently following the guide on [building from source](https://scikit-learn.org/dev/developers/advanced_installation.html) to create an editable build of scikit-learn. However, I encountered some errors during the process. Any hel... | 30,195 | [
0.01904744654893875,
-0.03251666948199272,
-0.0018636466702446342,
-0.026270471513271332,
0.07115557044744492,
0.020001403987407684,
-0.00020264546037651598,
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-0.07714169472455978,
0.0015712192980572581,
0.0019300823332741857,
0.0990668311715126,
0.0006763365236110985,... |
https://github.com/scikit-learn/scikit-learn/issues/30195 | [
"Documentation",
"Build / CI"
] | issue in building from source with Windows64 Python 3.12.7
### Describe the bug
I am currently following the guide on [building from source](https://scikit-learn.org/dev/developers/advanced_installation.html) to create an editable build of scikit-learn. However, I encountered some errors during the process. Any hel... | 30,195 | [
0.01904744654893875,
-0.03251666948199272,
-0.0018636466702446342,
-0.026270471513271332,
0.07115557044744492,
0.020001403987407684,
-0.00020264546037651598,
-0.0012845692690461874,
-0.07714169472455978,
0.0015712192980572581,
0.0019300823332741857,
0.0990668311715126,
0.0006763365236110985,... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
+1
On Nov 1, 2024, 21:50, at ... | 30,194 | [
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0.02916002832353115,
0.05050324276089668,
-0.0015490135410800576,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I'm +0.5
On the (-) side, I... | 30,194 | [
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0.056010447442531586,
0.010520153678953648,
0... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I would say I'm +0.5.
Froze... | 30,194 | [
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0.040112968534231186,
0.006291827652603388,
-0.002957... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
My argument is similar to what... | 30,194 | [
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... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
How about `sklearn.frozen.Free... | 30,194 | [
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0.009539085440337658,
-0.0001... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
`FrozenModel`? Everything's a ... | 30,194 | [
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0.0116055... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I agree with @adrinjalali's in... | 30,194 | [
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0.01978117786347866,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I would actually prefer `Freez... | 30,194 | [
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0.008503887802362442,
0.0059956274926662445,
0.048251066356897354,
0.011126479133963585,
0.00969... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
https://github.com/scikit-lear... | 30,194 | [
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0.02415076456964016,
0.016724616289138794,
0.048102691769599915,
0.010846861638128757,
-0.00013... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
First reaction wise I like `Fr... | 30,194 | [
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0.021784937009215355,
0.028627362102270126,
-0.014761805534362793,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
> Also Frozen(my_random_forest... | 30,194 | [
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0.017677994444966316,
-0.002939704805612564,
-0.00... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I also prefer `Frozen(Estimato... | 30,194 | [
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0.04468632489442825,
0.00846114382147789,
0.00896... |
https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
https://github.com/scikit-lear... | 30,194 | [
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0.010554551146924496,
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https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
I'm okay with the current `Fro... | 30,194 | [
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https://github.com/scikit-learn/scikit-learn/issues/30194 | [
"API",
"Blocker",
"RFC"
] | Rename `frozen.FrozenEstimator` to `frozen.Frozen`
Looking through all our estimators, none of them have the word "Estimator" besides `BaseEstimator` and `MetaEstimatorMixin`. I think we can shorten the meta-estimator name to `Frozen`.
CC @adrinjalali @scikit-learn/core-devs
COMMENT:
Ok then, I guess we're settled... | 30,194 | [
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https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 | [
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0.02214512787759304,
0.06358... |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 | [
0.04880962148308754,
0.04039140045642853,
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0.01595156453549862,
0.051879... |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 | [
0.046856362372636795,
0.04017390310764313,
-0.028943177312612534,
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0.024383263662457466,
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0.052237335592508316,
0.02502468414604664,
0.016865503042936325,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 | [
0.05092807114124298,
0.038420651108026505,
-0.019046103581786156,
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0.019423216581344604,
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0.017812004312872887,
0.013617039658129215,
0.04... |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 | [
0.04905221611261368,
0.03541059419512749,
-0.02266860194504261,
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0.025013701990246773,
0.015108246356248856,
0.038930... |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 | [
0.048787783831357956,
0.04469858109951019,
-0.027493035420775414,
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0.02336409129202366,
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0.008366814814507961,
0.02764485590159893,
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0.05061386153101921,
0.027493253350257874,
0.012667159549891949,
0.054... |
https://github.com/scikit-learn/scikit-learn/issues/30190 | [
"Documentation"
] | Towncrier categories overlap
### Describe the issue linked to the documentation
I had first [commented](https://github.com/scikit-learn/scikit-learn/pull/30046#issuecomment-2451761128) this on an issue, but I think maybe it is worth its own issue:
These categories that are listed in the [changelog instructions](... | 30,190 | [
0.04821823537349701,
0.04266299307346344,
-0.025831326842308044,
-0.004377265460789204,
0.027817873284220695,
0.012326776050031185,
0.016605878248810768,
0.030598139390349388,
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0.047004904597997665,
0.0257769376039505,
0.013137380592525005,
0.052... |
https://github.com/scikit-learn/scikit-learn/issues/30189 | [
"Bug"
] | `SimpleImputer().transform` on empty array raises `ValueError: Found array with 0 sample(s)`
### Describe the bug
I understand that the imputer requires at least one sample to fit. There is no reason for it not to return an empty array on `transform` though.
### Steps/Code to Reproduce
```python
import numpy as np... | 30,189 | [
-0.002601576503366232,
-0.06473006308078766,
0.012450325302779675,
-0.01870514638721943,
0.06877356767654419,
-0.027501078322529793,
0.1023884117603302,
0.04246997460722923,
0.05688655376434326,
0.0062335338443517685,
0.021132731810212135,
0.06644957512617111,
0.019686419516801834,
0.00531... |
https://github.com/scikit-learn/scikit-learn/issues/30189 | [
"Bug"
] | `SimpleImputer().transform` on empty array raises `ValueError: Found array with 0 sample(s)`
### Describe the bug
I understand that the imputer requires at least one sample to fit. There is no reason for it not to return an empty array on `transform` though.
### Steps/Code to Reproduce
```python
import numpy as np... | 30,189 | [
-0.002601576503366232,
-0.06473006308078766,
0.012450325302779675,
-0.01870514638721943,
0.06877356767654419,
-0.027501078322529793,
0.1023884117603302,
0.04246997460722923,
0.05688655376434326,
0.0062335338443517685,
0.021132731810212135,
0.06644957512617111,
0.019686419516801834,
0.00531... |
https://github.com/scikit-learn/scikit-learn/issues/30189 | [
"Bug"
] | `SimpleImputer().transform` on empty array raises `ValueError: Found array with 0 sample(s)`
### Describe the bug
I understand that the imputer requires at least one sample to fit. There is no reason for it not to return an empty array on `transform` though.
### Steps/Code to Reproduce
```python
import numpy as np... | 30,189 | [
-0.002601576503366232,
-0.06473006308078766,
0.012450325302779675,
-0.01870514638721943,
0.06877356767654419,
-0.027501078322529793,
0.1023884117603302,
0.04246997460722923,
0.05688655376434326,
0.0062335338443517685,
0.021132731810212135,
0.06644957512617111,
0.019686419516801834,
0.00531... |
https://github.com/scikit-learn/scikit-learn/issues/30189 | [
"Bug"
] | `SimpleImputer().transform` on empty array raises `ValueError: Found array with 0 sample(s)`
### Describe the bug
I understand that the imputer requires at least one sample to fit. There is no reason for it not to return an empty array on `transform` though.
### Steps/Code to Reproduce
```python
import numpy as np... | 30,189 | [
-0.002601576503366232,
-0.06473006308078766,
0.012450325302779675,
-0.01870514638721943,
0.06877356767654419,
-0.027501078322529793,
0.1023884117603302,
0.04246997460722923,
0.05688655376434326,
0.0062335338443517685,
0.021132731810212135,
0.06644957512617111,
0.019686419516801834,
0.00531... |
https://github.com/scikit-learn/scikit-learn/issues/30189 | [
"Bug"
] | `SimpleImputer().transform` on empty array raises `ValueError: Found array with 0 sample(s)`
### Describe the bug
I understand that the imputer requires at least one sample to fit. There is no reason for it not to return an empty array on `transform` though.
### Steps/Code to Reproduce
```python
import numpy as np... | 30,189 | [
-0.002601576503366232,
-0.06473006308078766,
0.012450325302779675,
-0.01870514638721943,
0.06877356767654419,
-0.027501078322529793,
0.1023884117603302,
0.04246997460722923,
0.05688655376434326,
0.0062335338443517685,
0.021132731810212135,
0.06644957512617111,
0.019686419516801834,
0.00531... |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 | [
-0.01776629127562046,
0.07483655959367752,
0.035134684294462204,
-0.06630351394414902,
0.027945540845394135,
-0.023621300235390663,
0.013122443109750748,
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-0.04049495607614517,
0.08917609602212906,
-0.04071924090385437,
0.008866356685757637,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 | [
-0.01784096658229828,
0.07430776208639145,
0.03523985669016838,
-0.06566757708787918,
0.028949100524187088,
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0.012286071665585041,
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-0.040317192673683167,
0.08953747898340225,
-0.04044228792190552,
0.009864648804068565,
0.08... |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 | [
-0.011502918787300587,
0.07424260675907135,
0.03512735292315483,
-0.05823168903589249,
0.02806287445127964,
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0.015110787935554981,
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-0.04762941226363182,
0.0901300311088562,
-0.04508962854743004,
0.009990046732127666,
0.0872532... |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 | [
-0.011020216159522533,
0.06897516548633575,
0.037449147552251816,
-0.05694868043065071,
0.03022705763578415,
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0.008782408200204372,
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0.08482547104358673,
-0.0421450138092041,
0.012623879127204418,
0.085... |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 | [
-0.010584365576505661,
0.07029202580451965,
0.0383363701403141,
-0.05926162376999855,
0.029596492648124695,
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0.0093673225492239,
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0.08659441024065018,
-0.04532190039753914,
0.011958236806094646,
0.083... |
https://github.com/scikit-learn/scikit-learn/issues/30188 | [
"New Feature",
"Needs Triage"
] | Fallback value for NaN feature during classification
### Describe the workflow you want to enable
In code like this:
```python
probabilities = model.predict_proba(df)
```
where I need to predict classification probabilities from the features in the dataframe `df`, I could have NaNs. The way things are right n... | 30,188 | [
-0.017731206491589546,
0.07471606135368347,
0.02898251824080944,
-0.07288599759340286,
0.025332627817988396,
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0.019685791805386543,
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-0.04106573015451431,
0.09189026802778244,
-0.052229199558496475,
0.0019654908683151007,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30183 | [
"Documentation",
"Needs Investigation"
] | The Affinity Matrix Is NON-BINARY with`affinity="precomputed_nearest_neighbors"`
### Describe the issue linked to the documentation
## Issue Source:
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/sklearn/cluster/_spectral.py#L452-L454
## Issue Description
The Aff... | 30,183 | [
0.002503747120499611,
-0.1421656757593155,
0.0038820896297693253,
0.010701589286327362,
0.03431359678506851,
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0.0024905686732381582,
0.00013064865197520703,
0.05771593376994133,
0.009777044877409935,
-0.031174341216683388,
0.030038591474294662,
0.029699940234422684,
... |
https://github.com/scikit-learn/scikit-learn/issues/30183 | [
"Documentation",
"Needs Investigation"
] | The Affinity Matrix Is NON-BINARY with`affinity="precomputed_nearest_neighbors"`
### Describe the issue linked to the documentation
## Issue Source:
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/sklearn/cluster/_spectral.py#L452-L454
## Issue Description
The Aff... | 30,183 | [
0.002503747120499611,
-0.1421656757593155,
0.0038820896297693253,
0.010701589286327362,
0.03431359678506851,
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0.0024905686732381582,
0.00013064865197520703,
0.05771593376994133,
0.009777044877409935,
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0.030038591474294662,
0.029699940234422684,
... |
https://github.com/scikit-learn/scikit-learn/issues/30181 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other reference... | 30,181 | [
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0.04497953876852989,
0.022444913163781166,
0.03597238287329674,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30181 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other reference... | 30,181 | [
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0.0006943715270608664,
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0.04479411244392395,
0.022168593481183052,
0.03537427634000778,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30181 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other reference... | 30,181 | [
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0.046935390681028366,
0.022314034402370453,
0.037013549357652664,
-0.03... |
https://github.com/scikit-learn/scikit-learn/issues/30181 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other reference... | 30,181 | [
0.07809807360172272,
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0.0339592806994915,
-0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30181 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line
https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L70
"GitHub" is referred to as `github`
However, in the other reference... | 30,181 | [
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0.02386130578815937,
0.03606507182121277,
-0.031... |
https://github.com/scikit-learn/scikit-learn/issues/30180 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line: https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L161
there is a reference attached to "Enhancement proposals (SLEPs)." ... | 30,180 | [
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0.029558198526501656,
-0.01... |
https://github.com/scikit-learn/scikit-learn/issues/30180 | [
"Documentation"
] | DOC grammar issue in the governance page
### Describe the issue linked to the documentation
In the governance page at line: https://github.com/scikit-learn/scikit-learn/blob/59dd128d4d26fff2ff197b8c1e801647a22e0158/doc/governance.rst?plain=1#L161
there is a reference attached to "Enhancement proposals (SLEPs)." ... | 30,180 | [
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0.02640911564230919,
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https://github.com/scikit-learn/scikit-learn/issues/30166 | [
"Easy",
"Documentation"
] | The best model and final model in RANSAC are not same.
### Describe the bug
The best model and final model in RANSAC are not same. Therefore, the final model inliers may not be same as the best model inliers.
In `_ransac.py`, the following code snippet computes the final model using all inliers so the final mod... | 30,166 | [
0.007635410409420729,
-0.03368695080280304,
0.028321167454123497,
0.0824243426322937,
0.0412079356610775,
0.008849294856190681,
0.024370159953832626,
0.02707873098552227,
0.00872092042118311,
0.011307294480502605,
-0.017288565635681152,
0.051642343401908875,
0.024028794839978218,
0.0166237... |
https://github.com/scikit-learn/scikit-learn/issues/30166 | [
"Easy",
"Documentation"
] | The best model and final model in RANSAC are not same.
### Describe the bug
The best model and final model in RANSAC are not same. Therefore, the final model inliers may not be same as the best model inliers.
In `_ransac.py`, the following code snippet computes the final model using all inliers so the final mod... | 30,166 | [
0.007635410409420729,
-0.03368695080280304,
0.028321167454123497,
0.0824243426322937,
0.0412079356610775,
0.008849294856190681,
0.024370159953832626,
0.02707873098552227,
0.00872092042118311,
0.011307294480502605,
-0.017288565635681152,
0.051642343401908875,
0.024028794839978218,
0.0166237... |
https://github.com/scikit-learn/scikit-learn/issues/30166 | [
"Easy",
"Documentation"
] | The best model and final model in RANSAC are not same.
### Describe the bug
The best model and final model in RANSAC are not same. Therefore, the final model inliers may not be same as the best model inliers.
In `_ransac.py`, the following code snippet computes the final model using all inliers so the final mod... | 30,166 | [
0.007635410409420729,
-0.03368695080280304,
0.028321167454123497,
0.0824243426322937,
0.0412079356610775,
0.008849294856190681,
0.024370159953832626,
0.02707873098552227,
0.00872092042118311,
0.011307294480502605,
-0.017288565635681152,
0.051642343401908875,
0.024028794839978218,
0.0166237... |
https://github.com/scikit-learn/scikit-learn/issues/30166 | [
"Easy",
"Documentation"
] | The best model and final model in RANSAC are not same.
### Describe the bug
The best model and final model in RANSAC are not same. Therefore, the final model inliers may not be same as the best model inliers.
In `_ransac.py`, the following code snippet computes the final model using all inliers so the final mod... | 30,166 | [
0.007635410409420729,
-0.03368695080280304,
0.028321167454123497,
0.0824243426322937,
0.0412079356610775,
0.008849294856190681,
0.024370159953832626,
0.02707873098552227,
0.00872092042118311,
0.011307294480502605,
-0.017288565635681152,
0.051642343401908875,
0.024028794839978218,
0.0166237... |
https://github.com/scikit-learn/scikit-learn/issues/30161 | [
"Needs Info"
] | Refactor _check_partial_fit_first_call to separate validation from state modification
### Describe the workflow you want to enable
This change aims to improve the architectural design of `partial_fit` classes validation by separating the validation logic from state modification. This will make the code more maintaina... | 30,161 | [
0.0013873920543119311,
0.05795150250196457,
0.018791144713759422,
0.010283183306455612,
0.05400409922003746,
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-0.009509905241429806,
0.038346707820892334,
0.010949385352432728,
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0.019921209663152695,
0.026236657053232193,
-0.02990591712296009,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30161 | [
"Needs Info"
] | Refactor _check_partial_fit_first_call to separate validation from state modification
### Describe the workflow you want to enable
This change aims to improve the architectural design of `partial_fit` classes validation by separating the validation logic from state modification. This will make the code more maintaina... | 30,161 | [
0.0013873920543119311,
0.05795150250196457,
0.018791144713759422,
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0.019921209663152695,
0.026236657053232193,
-0.02990591712296009,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/30160 | [
"New Feature",
"Performance"
] | Change forcing sequence in newton-cg solver of LogisticRegression
### Describe the workflow you want to enable
I'd like to have faster convergence of the `"newton-cg"` solver of `LogisticRegression` based on scientific publications with empirical studies as done in [A Study on Truncated Newton Methods for Linear Cl... | 30,160 | [
-0.002049884758889675,
0.05846437066793442,
0.0057979002594947815,
-0.017480265349149704,
0.024224966764450073,
-0.04480515792965889,
-0.06487135589122772,
0.033559322357177734,
-0.04852250963449478,
0.0013736238470301032,
0.06760948896408081,
-0.01299325656145811,
-0.035569433122873306,
-... |
https://github.com/scikit-learn/scikit-learn/issues/30160 | [
"New Feature",
"Performance"
] | Change forcing sequence in newton-cg solver of LogisticRegression
### Describe the workflow you want to enable
I'd like to have faster convergence of the `"newton-cg"` solver of `LogisticRegression` based on scientific publications with empirical studies as done in [A Study on Truncated Newton Methods for Linear Cl... | 30,160 | [
-0.002049884758889675,
0.05846437066793442,
0.0057979002594947815,
-0.017480265349149704,
0.024224966764450073,
-0.04480515792965889,
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0.033559322357177734,
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0.0013736238470301032,
0.06760948896408081,
-0.01299325656145811,
-0.035569433122873306,
-... |
https://github.com/scikit-learn/scikit-learn/issues/30160 | [
"New Feature",
"Performance"
] | Change forcing sequence in newton-cg solver of LogisticRegression
### Describe the workflow you want to enable
I'd like to have faster convergence of the `"newton-cg"` solver of `LogisticRegression` based on scientific publications with empirical studies as done in [A Study on Truncated Newton Methods for Linear Cl... | 30,160 | [
-0.002049884758889675,
0.05846437066793442,
0.0057979002594947815,
-0.017480265349149704,
0.024224966764450073,
-0.04480515792965889,
-0.06487135589122772,
0.033559322357177734,
-0.04852250963449478,
0.0013736238470301032,
0.06760948896408081,
-0.01299325656145811,
-0.035569433122873306,
-... |
https://github.com/scikit-learn/scikit-learn/issues/30159 | [
"Needs Triage"
] | ⚠️ CI failed on Wheel builder (last failure: Oct 27, 2024) ⚠️
**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/11537349026)** (Oct 27, 2024)
COMMENT:
## CI is no longer failing! ✅
[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/11546977899) on Oct 28... | 30,159 | [
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0.03762390464544296,
0.08539672940969467,
0.02882259525358677,
-0.015172121115028858,
0.08080... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30151 | [
"Bug"
] | Segmentation fault in sklearn.metrics.pairwise_distances with OpenBLAS 0.3.28 (only pthreads variant)
```
mamba create -n testenv scikit-learn python=3.12 libopenblas=0.3.28 -y
conda activate testenv
PYTHONFAULTHANDLER=1 python /tmp/test_openblas.py
```
```py
# /tmp/test_openblas.py
import numpy as np
from... | 30,151 | [
-0.01722785271704197,
-0.04042676091194153,
-0.017545001581311226,
0.024993455037474632,
0.012183836661279202,
0.016780424863100052,
0.04918358102440834,
0.049074895679950714,
-0.029629714787006378,
-0.03446294367313385,
0.021204467862844467,
0.03312781825661659,
-0.019897758960723877,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
https://github.com/scikit-learn/scikit-learn/issues/30147 | [
"Bug"
] | average_precision_score not working as expected
### Describe the bug
When compute AP with average_precision_score, I get unexpected results. The y_scores (output from the models) are very low for positive samples, so my AP should be very low. Instead I get a perfect 1.0 AP score.
### Steps/Code to Reproduce
```pyth... | 30,147 | [
-0.03054129146039486,
-0.0754200667142868,
0.016684597358107567,
0.03297929838299751,
0.0751592367887497,
-0.05127815902233124,
-0.009839809499680996,
-0.04266556724905968,
0.0006414995295926929,
0.013879990205168724,
0.0063989185728132725,
0.017736468464136124,
0.06785564869642258,
0.0338... |
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