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/24340 | [
"Bug"
] | BUG: GaussianProcessRegressor.predict inplace modifies input X, when passed via kernel
### Describe the bug
In line 425, 426 of /sklearn/gaussian_process/_gpr.py (inside the predict method) y_var is modified in place:
```
# Compute variance of predictive distribution
# Use einsum ... | 24,340 | [
0.015717322006821632,
0.02504741959273815,
0.007130498066544533,
0.0008761823992244899,
0.05681764334440231,
-0.0352056659758091,
0.017844870686531067,
0.0032567987218499184,
0.00026655790861696005,
0.03651934862136841,
0.03581644222140312,
0.07501363754272461,
0.04177936166524887,
0.02659... |
https://github.com/scikit-learn/scikit-learn/issues/24340 | [
"Bug"
] | BUG: GaussianProcessRegressor.predict inplace modifies input X, when passed via kernel
### Describe the bug
In line 425, 426 of /sklearn/gaussian_process/_gpr.py (inside the predict method) y_var is modified in place:
```
# Compute variance of predictive distribution
# Use einsum ... | 24,340 | [
0.015717322006821632,
0.02504741959273815,
0.007130498066544533,
0.0008761823992244899,
0.05681764334440231,
-0.0352056659758091,
0.017844870686531067,
0.0032567987218499184,
0.00026655790861696005,
0.03651934862136841,
0.03581644222140312,
0.07501363754272461,
0.04177936166524887,
0.02659... |
https://github.com/scikit-learn/scikit-learn/issues/24340 | [
"Bug"
] | BUG: GaussianProcessRegressor.predict inplace modifies input X, when passed via kernel
### Describe the bug
In line 425, 426 of /sklearn/gaussian_process/_gpr.py (inside the predict method) y_var is modified in place:
```
# Compute variance of predictive distribution
# Use einsum ... | 24,340 | [
0.015717322006821632,
0.02504741959273815,
0.007130498066544533,
0.0008761823992244899,
0.05681764334440231,
-0.0352056659758091,
0.017844870686531067,
0.0032567987218499184,
0.00026655790861696005,
0.03651934862136841,
0.03581644222140312,
0.07501363754272461,
0.04177936166524887,
0.02659... |
https://github.com/scikit-learn/scikit-learn/issues/24340 | [
"Bug"
] | BUG: GaussianProcessRegressor.predict inplace modifies input X, when passed via kernel
### Describe the bug
In line 425, 426 of /sklearn/gaussian_process/_gpr.py (inside the predict method) y_var is modified in place:
```
# Compute variance of predictive distribution
# Use einsum ... | 24,340 | [
0.015717322006821632,
0.02504741959273815,
0.007130498066544533,
0.0008761823992244899,
0.05681764334440231,
-0.0352056659758091,
0.017844870686531067,
0.0032567987218499184,
0.00026655790861696005,
0.03651934862136841,
0.03581644222140312,
0.07501363754272461,
0.04177936166524887,
0.02659... |
https://github.com/scikit-learn/scikit-learn/issues/24328 | [
"Documentation",
"Build / CI"
] | DOC deprecate is updating a docstring against numpydocs rules
### Describe the issue linked to the documentation
`sklearn.utils.deprecate` is updating the docstring within `_update_doc()`
This causes the numpydocs to fail during the test with the error:
`GL09: Deprecation warning should precede extended summary`
... | 24,328 | [
0.03103109635412693,
0.00822175107896328,
0.003297638613730669,
-0.040420159697532654,
0.04337291419506073,
0.018855318427085876,
0.0002988166525028646,
0.02842293120920658,
-0.04389650374650955,
-0.021730083972215652,
0.07620128244161606,
0.06553418189287186,
0.012928415089845657,
-0.0356... |
https://github.com/scikit-learn/scikit-learn/issues/24328 | [
"Documentation",
"Build / CI"
] | DOC deprecate is updating a docstring against numpydocs rules
### Describe the issue linked to the documentation
`sklearn.utils.deprecate` is updating the docstring within `_update_doc()`
This causes the numpydocs to fail during the test with the error:
`GL09: Deprecation warning should precede extended summary`
... | 24,328 | [
0.033986616879701614,
0.010105524212121964,
-0.0025286769960075617,
-0.037124473601579666,
0.046614035964012146,
0.019826561212539673,
0.0008585168397985399,
0.03050164505839348,
-0.042559877038002014,
-0.020814284682273865,
0.07927800714969635,
0.06541301310062408,
0.011212784796953201,
-... |
https://github.com/scikit-learn/scikit-learn/issues/24315 | [
"Bug",
"help wanted"
] | log_loss giving nan when input is np.float32 and eps is default
### Describe the bug
When input has values that are numpy array of np.float32, 1-eps (with default eps=1e-15) results in 1.0, and log_loss() when calculating log(1-p) with p=1.0 results in nan.
### Steps/Code to Reproduce
```py
from sklearn.metr... | 24,315 | [
-0.0236436165869236,
-0.06401517987251282,
0.020281856879591942,
0.014815342612564564,
0.06585405021905899,
-0.018032986670732498,
0.04333719611167908,
0.029511814936995506,
-0.054784830659627914,
-0.03435111790895462,
0.030268166214227676,
-0.034452538937330246,
0.005006821826100349,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24315 | [
"Bug",
"help wanted"
] | log_loss giving nan when input is np.float32 and eps is default
### Describe the bug
When input has values that are numpy array of np.float32, 1-eps (with default eps=1e-15) results in 1.0, and log_loss() when calculating log(1-p) with p=1.0 results in nan.
### Steps/Code to Reproduce
```py
from sklearn.metr... | 24,315 | [
-0.0236436165869236,
-0.06401517987251282,
0.020281856879591942,
0.014815342612564564,
0.06585405021905899,
-0.018032986670732498,
0.04333719611167908,
0.029511814936995506,
-0.054784830659627914,
-0.03435111790895462,
0.030268166214227676,
-0.034452538937330246,
0.005006821826100349,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24315 | [
"Bug",
"help wanted"
] | log_loss giving nan when input is np.float32 and eps is default
### Describe the bug
When input has values that are numpy array of np.float32, 1-eps (with default eps=1e-15) results in 1.0, and log_loss() when calculating log(1-p) with p=1.0 results in nan.
### Steps/Code to Reproduce
```py
from sklearn.metr... | 24,315 | [
-0.0236436165869236,
-0.06401517987251282,
0.020281856879591942,
0.014815342612564564,
0.06585405021905899,
-0.018032986670732498,
0.04333719611167908,
0.029511814936995506,
-0.054784830659627914,
-0.03435111790895462,
0.030268166214227676,
-0.034452538937330246,
0.005006821826100349,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24315 | [
"Bug",
"help wanted"
] | log_loss giving nan when input is np.float32 and eps is default
### Describe the bug
When input has values that are numpy array of np.float32, 1-eps (with default eps=1e-15) results in 1.0, and log_loss() when calculating log(1-p) with p=1.0 results in nan.
### Steps/Code to Reproduce
```py
from sklearn.metr... | 24,315 | [
-0.0236436165869236,
-0.06401517987251282,
0.020281856879591942,
0.014815342612564564,
0.06585405021905899,
-0.018032986670732498,
0.04333719611167908,
0.029511814936995506,
-0.054784830659627914,
-0.03435111790895462,
0.030268166214227676,
-0.034452538937330246,
0.005006821826100349,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24315 | [
"Bug",
"help wanted"
] | log_loss giving nan when input is np.float32 and eps is default
### Describe the bug
When input has values that are numpy array of np.float32, 1-eps (with default eps=1e-15) results in 1.0, and log_loss() when calculating log(1-p) with p=1.0 results in nan.
### Steps/Code to Reproduce
```py
from sklearn.metr... | 24,315 | [
-0.0236436165869236,
-0.06401517987251282,
0.020281856879591942,
0.014815342612564564,
0.06585405021905899,
-0.018032986670732498,
0.04333719611167908,
0.029511814936995506,
-0.054784830659627914,
-0.03435111790895462,
0.030268166214227676,
-0.034452538937330246,
0.005006821826100349,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24315 | [
"Bug",
"help wanted"
] | log_loss giving nan when input is np.float32 and eps is default
### Describe the bug
When input has values that are numpy array of np.float32, 1-eps (with default eps=1e-15) results in 1.0, and log_loss() when calculating log(1-p) with p=1.0 results in nan.
### Steps/Code to Reproduce
```py
from sklearn.metr... | 24,315 | [
-0.0236436165869236,
-0.06401517987251282,
0.020281856879591942,
0.014815342612564564,
0.06585405021905899,
-0.018032986670732498,
0.04333719611167908,
0.029511814936995506,
-0.054784830659627914,
-0.03435111790895462,
0.030268166214227676,
-0.034452538937330246,
0.005006821826100349,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24315 | [
"Bug",
"help wanted"
] | log_loss giving nan when input is np.float32 and eps is default
### Describe the bug
When input has values that are numpy array of np.float32, 1-eps (with default eps=1e-15) results in 1.0, and log_loss() when calculating log(1-p) with p=1.0 results in nan.
### Steps/Code to Reproduce
```py
from sklearn.metr... | 24,315 | [
-0.0236436165869236,
-0.06401517987251282,
0.020281856879591942,
0.014815342612564564,
0.06585405021905899,
-0.018032986670732498,
0.04333719611167908,
0.029511814936995506,
-0.054784830659627914,
-0.03435111790895462,
0.030268166214227676,
-0.034452538937330246,
0.005006821826100349,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24315 | [
"Bug",
"help wanted"
] | log_loss giving nan when input is np.float32 and eps is default
### Describe the bug
When input has values that are numpy array of np.float32, 1-eps (with default eps=1e-15) results in 1.0, and log_loss() when calculating log(1-p) with p=1.0 results in nan.
### Steps/Code to Reproduce
```py
from sklearn.metr... | 24,315 | [
-0.0236436165869236,
-0.06401517987251282,
0.020281856879591942,
0.014815342612564564,
0.06585405021905899,
-0.018032986670732498,
0.04333719611167908,
0.029511814936995506,
-0.054784830659627914,
-0.03435111790895462,
0.030268166214227676,
-0.034452538937330246,
0.005006821826100349,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24315 | [
"Bug",
"help wanted"
] | log_loss giving nan when input is np.float32 and eps is default
### Describe the bug
When input has values that are numpy array of np.float32, 1-eps (with default eps=1e-15) results in 1.0, and log_loss() when calculating log(1-p) with p=1.0 results in nan.
### Steps/Code to Reproduce
```py
from sklearn.metr... | 24,315 | [
-0.0236436165869236,
-0.06401517987251282,
0.020281856879591942,
0.014815342612564564,
0.06585405021905899,
-0.018032986670732498,
0.04333719611167908,
0.029511814936995506,
-0.054784830659627914,
-0.03435111790895462,
0.030268166214227676,
-0.034452538937330246,
0.005006821826100349,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24313 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | BayesianRidge prediction standard deviation affected by uniform sample weights
### Describe the bug
The standard deviation of predictions obtained by setting return_std=True on predict(), is clearly affected by uniform sample_weight vectors on fit(). A uniform sample_weight vector will act as a constant on the like... | 24,313 | [
-0.00919291004538536,
-0.040043026208877563,
0.04071143642067909,
-0.003984031733125448,
0.06835528463125229,
-0.028509272262454033,
0.043612755835056305,
0.012657401151955128,
-0.001546679763123393,
0.049734778702259064,
0.03050411492586136,
0.052968963980674744,
0.00556412898004055,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/24313 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | BayesianRidge prediction standard deviation affected by uniform sample weights
### Describe the bug
The standard deviation of predictions obtained by setting return_std=True on predict(), is clearly affected by uniform sample_weight vectors on fit(). A uniform sample_weight vector will act as a constant on the like... | 24,313 | [
-0.00919291004538536,
-0.040043026208877563,
0.04071143642067909,
-0.003984031733125448,
0.06835528463125229,
-0.028509272262454033,
0.043612755835056305,
0.012657401151955128,
-0.001546679763123393,
0.049734778702259064,
0.03050411492586136,
0.052968963980674744,
0.00556412898004055,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/24313 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | BayesianRidge prediction standard deviation affected by uniform sample weights
### Describe the bug
The standard deviation of predictions obtained by setting return_std=True on predict(), is clearly affected by uniform sample_weight vectors on fit(). A uniform sample_weight vector will act as a constant on the like... | 24,313 | [
-0.00919291004538536,
-0.040043026208877563,
0.04071143642067909,
-0.003984031733125448,
0.06835528463125229,
-0.028509272262454033,
0.043612755835056305,
0.012657401151955128,
-0.001546679763123393,
0.049734778702259064,
0.03050411492586136,
0.052968963980674744,
0.00556412898004055,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/24313 | [
"Bug",
"module:linear_model",
"Needs Investigation"
] | BayesianRidge prediction standard deviation affected by uniform sample weights
### Describe the bug
The standard deviation of predictions obtained by setting return_std=True on predict(), is clearly affected by uniform sample_weight vectors on fit(). A uniform sample_weight vector will act as a constant on the like... | 24,313 | [
-0.00919291004538536,
-0.040043026208877563,
0.04071143642067909,
-0.003984031733125448,
0.06835528463125229,
-0.028509272262454033,
0.043612755835056305,
0.012657401151955128,
-0.001546679763123393,
0.049734778702259064,
0.03050411492586136,
0.052968963980674744,
0.00556412898004055,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/24312 | [
"Build / CI",
"help wanted"
] | Coordinate with other scientific python projects to use shared tools and policy to upload nightly build wheels
See the motivation in this discussion: https://discuss.scientific-python.org/t/interest-in-github-action-for-scipy-wheels-nightly-uploads-and-removals/397
- Draft shared Github Actions config: https://gith... | 24,312 | [
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0.034061212092638016,
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0... |
https://github.com/scikit-learn/scikit-learn/issues/24312 | [
"Build / CI",
"help wanted"
] | Coordinate with other scientific python projects to use shared tools and policy to upload nightly build wheels
See the motivation in this discussion: https://discuss.scientific-python.org/t/interest-in-github-action-for-scipy-wheels-nightly-uploads-and-removals/397
- Draft shared Github Actions config: https://gith... | 24,312 | [
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https://github.com/scikit-learn/scikit-learn/issues/24312 | [
"Build / CI",
"help wanted"
] | Coordinate with other scientific python projects to use shared tools and policy to upload nightly build wheels
See the motivation in this discussion: https://discuss.scientific-python.org/t/interest-in-github-action-for-scipy-wheels-nightly-uploads-and-removals/397
- Draft shared Github Actions config: https://gith... | 24,312 | [
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https://github.com/scikit-learn/scikit-learn/issues/24312 | [
"Build / CI",
"help wanted"
] | Coordinate with other scientific python projects to use shared tools and policy to upload nightly build wheels
See the motivation in this discussion: https://discuss.scientific-python.org/t/interest-in-github-action-for-scipy-wheels-nightly-uploads-and-removals/397
- Draft shared Github Actions config: https://gith... | 24,312 | [
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https://github.com/scikit-learn/scikit-learn/issues/24312 | [
"Build / CI",
"help wanted"
] | Coordinate with other scientific python projects to use shared tools and policy to upload nightly build wheels
See the motivation in this discussion: https://discuss.scientific-python.org/t/interest-in-github-action-for-scipy-wheels-nightly-uploads-and-removals/397
- Draft shared Github Actions config: https://gith... | 24,312 | [
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https://github.com/scikit-learn/scikit-learn/issues/24310 | [
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'copy'
I am using natureInspiredSearchcv for tuning randomForest classifier, while tuning min_impurity_decrease I am getting following error, May I have some help please
```
grid_result = grid.fit(X_trainf32, y_train)
File "C:\Users\pg\anaconda3\envs\autoMLpy395T... | 24,310 | [
0.022487081587314606,
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-0.016216957941651344,
... |
https://github.com/scikit-learn/scikit-learn/issues/24305 | [
"New Feature",
"Needs Triage"
] | Progress bar for BaseSearchCV and its inheritors to track iterative progress more conveniently
### Describe the workflow you want to enable
When `verbose>0`, the fitting is shown as a progress bar including the average time/pace and last fit attempted time.
### Describe your proposed solution
Implement `tqdm`-like... | 24,305 | [
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-0.052677322179079056,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24303 | [
"Needs Triage"
] | Cannot import 'DistanceMetric' from 'sklearn.metrics'
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24302
<div type='discussions-op-text'>
<sup>Originally posted by **anubhavde** August 30, 2022</sup>
# **ImportError: cannot import name 'DistanceMetric' from 'sklearn.metrics' (/home/... | 24,303 | [
0.03686687350273132,
-0.006394913885742426,
0.010601569898426533,
-0.035713113844394684,
0.05875816568732262,
0.04784473031759262,
0.05331100896000862,
0.05440424010157585,
0.06295879930257797,
-0.0014408682473003864,
-0.019473841413855553,
0.025366133078932762,
-0.011760154739022255,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24303 | [
"Needs Triage"
] | Cannot import 'DistanceMetric' from 'sklearn.metrics'
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24302
<div type='discussions-op-text'>
<sup>Originally posted by **anubhavde** August 30, 2022</sup>
# **ImportError: cannot import name 'DistanceMetric' from 'sklearn.metrics' (/home/... | 24,303 | [
0.03686687350273132,
-0.006394913885742426,
0.010601569898426533,
-0.035713113844394684,
0.05875816568732262,
0.04784473031759262,
0.05331100896000862,
0.05440424010157585,
0.06295879930257797,
-0.0014408682473003864,
-0.019473841413855553,
0.025366133078932762,
-0.011760154739022255,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24303 | [
"Needs Triage"
] | Cannot import 'DistanceMetric' from 'sklearn.metrics'
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24302
<div type='discussions-op-text'>
<sup>Originally posted by **anubhavde** August 30, 2022</sup>
# **ImportError: cannot import name 'DistanceMetric' from 'sklearn.metrics' (/home/... | 24,303 | [
0.03686687350273132,
-0.006394913885742426,
0.010601569898426533,
-0.035713113844394684,
0.05875816568732262,
0.04784473031759262,
0.05331100896000862,
0.05440424010157585,
0.06295879930257797,
-0.0014408682473003864,
-0.019473841413855553,
0.025366133078932762,
-0.011760154739022255,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24303 | [
"Needs Triage"
] | Cannot import 'DistanceMetric' from 'sklearn.metrics'
### Discussed in https://github.com/scikit-learn/scikit-learn/discussions/24302
<div type='discussions-op-text'>
<sup>Originally posted by **anubhavde** August 30, 2022</sup>
# **ImportError: cannot import name 'DistanceMetric' from 'sklearn.metrics' (/home/... | 24,303 | [
0.03686687350273132,
-0.006394913885742426,
0.010601569898426533,
-0.035713113844394684,
0.05875816568732262,
0.04784473031759262,
0.05331100896000862,
0.05440424010157585,
0.06295879930257797,
-0.0014408682473003864,
-0.019473841413855553,
0.025366133078932762,
-0.011760154739022255,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24288 | [
"Documentation",
"module:metrics"
] | Add plotting AUC plotting tools to "plot_roc" example
### Describe the issue linked to the documentation
The following example:
https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
does not use sklearn.metrics.plot_roc_curve or any of the related plotting tools.
### Suggest a potential alter... | 24,288 | [
-0.03323927894234657,
-0.00476723350584507,
-0.0017009066650643945,
-0.010024083778262138,
0.009410693310201168,
0.03314496949315071,
0.04850194230675697,
0.0014835393521934748,
0.01859440468251705,
-0.01631295680999756,
0.025517713278532028,
0.05605355277657509,
-0.03649858757853508,
0.06... |
https://github.com/scikit-learn/scikit-learn/issues/24288 | [
"Documentation",
"module:metrics"
] | Add plotting AUC plotting tools to "plot_roc" example
### Describe the issue linked to the documentation
The following example:
https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
does not use sklearn.metrics.plot_roc_curve or any of the related plotting tools.
### Suggest a potential alter... | 24,288 | [
-0.034072402864694595,
-0.006329231895506382,
-0.004633613862097263,
-0.008256564848124981,
0.012735658325254917,
0.023152967914938927,
0.037331320345401764,
0.008284185081720352,
0.018811818212270737,
-0.010127348825335503,
0.025467460975050926,
0.05667976662516594,
-0.039269205182790756,
... |
https://github.com/scikit-learn/scikit-learn/issues/24288 | [
"Documentation",
"module:metrics"
] | Add plotting AUC plotting tools to "plot_roc" example
### Describe the issue linked to the documentation
The following example:
https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
does not use sklearn.metrics.plot_roc_curve or any of the related plotting tools.
### Suggest a potential alter... | 24,288 | [
-0.03261163458228111,
0.003408081131055951,
-0.011686339043080807,
0.004054848570376635,
0.008393840864300728,
0.028520159423351288,
0.039232056587934494,
0.007973205298185349,
0.011151771061122417,
-0.012766255997121334,
0.03171706199645996,
0.06081576645374298,
-0.03911133110523224,
0.05... |
https://github.com/scikit-learn/scikit-learn/issues/24288 | [
"Documentation",
"module:metrics"
] | Add plotting AUC plotting tools to "plot_roc" example
### Describe the issue linked to the documentation
The following example:
https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
does not use sklearn.metrics.plot_roc_curve or any of the related plotting tools.
### Suggest a potential alter... | 24,288 | [
-0.026414556428790092,
-0.01171313039958477,
-0.003358251880854368,
-0.00858008861541748,
0.009817543439567089,
0.032300639897584915,
0.04603470116853714,
-0.0036756759509444237,
0.015443451702594757,
-0.01727711409330368,
0.0278814397752285,
0.054452329874038696,
-0.03506297245621681,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24288 | [
"Documentation",
"module:metrics"
] | Add plotting AUC plotting tools to "plot_roc" example
### Describe the issue linked to the documentation
The following example:
https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
does not use sklearn.metrics.plot_roc_curve or any of the related plotting tools.
### Suggest a potential alter... | 24,288 | [
-0.029942769557237625,
-0.0010552805615589023,
-0.008954813703894615,
0.02578483335673809,
-0.009767490439116955,
0.04257666692137718,
0.046434689313173294,
-0.007659412454813719,
0.005771405063569546,
-0.028924187645316124,
0.031016940250992775,
0.062464501708745956,
-0.04239877685904503,
... |
https://github.com/scikit-learn/scikit-learn/issues/24281 | [
"New Feature",
"module:inspection",
"Needs Decision - Include Feature"
] | Feature Request - Parallel Coordinates Plot for GridSearch result analysis
### Describe the workflow you want to enable
GridSearch result are hard to analyze expecially when `param_grid` is very large.
The current documentation show usages of:
- [matrix_plot/pivot](https://scikit-learn.org/stable/_images/sphx_g... | 24,281 | [
-0.05515727400779724,
0.038846779614686966,
-0.005418846383690834,
-0.002458982402458787,
-0.0189514197409153,
-0.02379882149398327,
0.04789077118039131,
0.02647855319082737,
0.008472420275211334,
0.006020710337907076,
-0.03681795671582222,
0.05108218267560005,
-0.03704002872109413,
0.0134... |
https://github.com/scikit-learn/scikit-learn/issues/24281 | [
"New Feature",
"module:inspection",
"Needs Decision - Include Feature"
] | Feature Request - Parallel Coordinates Plot for GridSearch result analysis
### Describe the workflow you want to enable
GridSearch result are hard to analyze expecially when `param_grid` is very large.
The current documentation show usages of:
- [matrix_plot/pivot](https://scikit-learn.org/stable/_images/sphx_g... | 24,281 | [
-0.05515727400779724,
0.038846779614686966,
-0.005418846383690834,
-0.002458982402458787,
-0.0189514197409153,
-0.02379882149398327,
0.04789077118039131,
0.02647855319082737,
0.008472420275211334,
0.006020710337907076,
-0.03681795671582222,
0.05108218267560005,
-0.03704002872109413,
0.0134... |
https://github.com/scikit-learn/scikit-learn/issues/24281 | [
"New Feature",
"module:inspection",
"Needs Decision - Include Feature"
] | Feature Request - Parallel Coordinates Plot for GridSearch result analysis
### Describe the workflow you want to enable
GridSearch result are hard to analyze expecially when `param_grid` is very large.
The current documentation show usages of:
- [matrix_plot/pivot](https://scikit-learn.org/stable/_images/sphx_g... | 24,281 | [
-0.05515727400779724,
0.038846779614686966,
-0.005418846383690834,
-0.002458982402458787,
-0.0189514197409153,
-0.02379882149398327,
0.04789077118039131,
0.02647855319082737,
0.008472420275211334,
0.006020710337907076,
-0.03681795671582222,
0.05108218267560005,
-0.03704002872109413,
0.0134... |
https://github.com/scikit-learn/scikit-learn/issues/24281 | [
"New Feature",
"module:inspection",
"Needs Decision - Include Feature"
] | Feature Request - Parallel Coordinates Plot for GridSearch result analysis
### Describe the workflow you want to enable
GridSearch result are hard to analyze expecially when `param_grid` is very large.
The current documentation show usages of:
- [matrix_plot/pivot](https://scikit-learn.org/stable/_images/sphx_g... | 24,281 | [
-0.05515727400779724,
0.038846779614686966,
-0.005418846383690834,
-0.002458982402458787,
-0.0189514197409153,
-0.02379882149398327,
0.04789077118039131,
0.02647855319082737,
0.008472420275211334,
0.006020710337907076,
-0.03681795671582222,
0.05108218267560005,
-0.03704002872109413,
0.0134... |
https://github.com/scikit-learn/scikit-learn/issues/24275 | [
"Documentation",
"module:cluster"
] | small typo on webpage
### Describe the issue linked to the documentation
There is a typo in https://scikit-learn.org/stable/modules/clustering.html#bisect-k-means
_Please notice the bold font below._
> [BisectingKMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.BisectingKMeans.html#sklea... | 24,275 | [
0.03624299541115761,
-0.05883541703224182,
-0.054480694234371185,
0.01596062444150448,
-0.008154381066560745,
0.028968317434191704,
0.0763794481754303,
0.0032615792006254196,
0.017324009910225868,
-0.03270922228693962,
0.056000757962465286,
0.010565775446593761,
0.06834958493709564,
-0.008... |
https://github.com/scikit-learn/scikit-learn/issues/24275 | [
"Documentation",
"module:cluster"
] | small typo on webpage
### Describe the issue linked to the documentation
There is a typo in https://scikit-learn.org/stable/modules/clustering.html#bisect-k-means
_Please notice the bold font below._
> [BisectingKMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.BisectingKMeans.html#sklea... | 24,275 | [
0.02825823426246643,
-0.06646852940320969,
-0.053280383348464966,
0.013818052597343922,
-0.00343918870203197,
0.020373649895191193,
0.07203750312328339,
0.0051287333481013775,
0.019548961892724037,
-0.019693268463015556,
0.043829113245010376,
0.026690565049648285,
0.06001076474785805,
-0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24275 | [
"Documentation",
"module:cluster"
] | small typo on webpage
### Describe the issue linked to the documentation
There is a typo in https://scikit-learn.org/stable/modules/clustering.html#bisect-k-means
_Please notice the bold font below._
> [BisectingKMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.BisectingKMeans.html#sklea... | 24,275 | [
0.03936735913157463,
-0.054808251559734344,
-0.05081133916974068,
0.018473980948328972,
-0.0023895055055618286,
0.031324487179517746,
0.06887295842170715,
0.0003461243468336761,
0.014883150346577168,
-0.040729157626628876,
0.042814791202545166,
0.01243489421904087,
0.06619463860988617,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24274 | [
"Bug",
"module:ensemble"
] | Histogram GBDT can segfault if categorical contains negative categories
Histogram GBDT can segfault if they contain negative categories.
Indeed, we documented that they should all be positive in `[0, max_bins]` but segfaulting is probably not the best error message.
I triggered the problem on my Mac M1 with the ... | 24,274 | [
-0.02139981836080551,
-0.004820807836949825,
0.009511123411357403,
-0.027778787538409233,
0.09085321426391602,
0.023363061249256134,
-0.004804128780961037,
0.010949117131531239,
0.004059139639139175,
-0.02430688962340355,
-0.00499154906719923,
-0.005727554205805063,
-0.012667162343859673,
... |
https://github.com/scikit-learn/scikit-learn/issues/24269 | [
"Build / CI"
] | ⚠️ CI failed on macOS.pylatest_conda_forge_mkl ⚠️
**CI failed on [macOS.pylatest_conda_forge_mkl](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=45966&view=logs&j=97641769-79fb-5590-9088-a30ce9b850b9)** (Aug 26, 2022)
Unable to find junit file. Please see link for details.
COMMENT:
## CI is no... | 24,269 | [
-0.001070361235179007,
0.027337368577718735,
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0.013621284626424313,
0.013144015334546566,
0.05450676009058952,
-0.018684597685933113,
0.076... |
https://github.com/scikit-learn/scikit-learn/issues/24265 | [
"module:preprocessing"
] | Rename OneHotEncoder option sparse to sparse_output
### Task
Introduce new parameter `sparse_output` in `OneHotEncoder` and deprecate the then old `sparse` parameter.
### Background
Several estimators have an option to return sparse output.
- `RandomTreesEmbedding(sparse_output=True)`
- `LabelBinarizer(sparse_o... | 24,265 | [
-0.02098437212407589,
0.03180014342069626,
0.05039571225643158,
-0.005655735265463591,
0.06119859591126442,
-0.005848522298038006,
0.030036790296435356,
0.07600825279951096,
-0.05723605677485466,
-0.016055846586823463,
0.05170054733753204,
0.05147364363074303,
0.012653365731239319,
0.04099... |
https://github.com/scikit-learn/scikit-learn/issues/24265 | [
"module:preprocessing"
] | Rename OneHotEncoder option sparse to sparse_output
### Task
Introduce new parameter `sparse_output` in `OneHotEncoder` and deprecate the then old `sparse` parameter.
### Background
Several estimators have an option to return sparse output.
- `RandomTreesEmbedding(sparse_output=True)`
- `LabelBinarizer(sparse_o... | 24,265 | [
-0.02627013623714447,
0.04482128471136093,
0.026498720049858093,
-0.028925662860274315,
0.020768681541085243,
-0.0002757208130788058,
0.036453209817409515,
0.05296638607978821,
-0.10194896161556244,
-0.016766376793384552,
0.06864246726036072,
0.03824407979846001,
0.015310402028262615,
0.02... |
https://github.com/scikit-learn/scikit-learn/issues/24265 | [
"module:preprocessing"
] | Rename OneHotEncoder option sparse to sparse_output
### Task
Introduce new parameter `sparse_output` in `OneHotEncoder` and deprecate the then old `sparse` parameter.
### Background
Several estimators have an option to return sparse output.
- `RandomTreesEmbedding(sparse_output=True)`
- `LabelBinarizer(sparse_o... | 24,265 | [
-0.026040231809020042,
0.04567910358309746,
0.031159954145550728,
-0.027045147493481636,
0.03725212439894676,
-0.001227645087055862,
0.031330935657024384,
0.060556139796972275,
-0.09659402817487717,
-0.014278133399784565,
0.06431163847446442,
0.0435032993555069,
0.016998812556266785,
0.031... |
https://github.com/scikit-learn/scikit-learn/issues/24265 | [
"module:preprocessing"
] | Rename OneHotEncoder option sparse to sparse_output
### Task
Introduce new parameter `sparse_output` in `OneHotEncoder` and deprecate the then old `sparse` parameter.
### Background
Several estimators have an option to return sparse output.
- `RandomTreesEmbedding(sparse_output=True)`
- `LabelBinarizer(sparse_o... | 24,265 | [
-0.002758794231340289,
0.0365113690495491,
0.029532216489315033,
-0.03936738520860672,
0.03547639027237892,
0.012071714736521244,
0.04313839226961136,
0.07704243808984756,
-0.06587501615285873,
-0.02772338315844536,
0.0760061964392662,
0.031773846596479416,
-0.0022964070085436106,
0.027316... |
https://github.com/scikit-learn/scikit-learn/issues/24265 | [
"module:preprocessing"
] | Rename OneHotEncoder option sparse to sparse_output
### Task
Introduce new parameter `sparse_output` in `OneHotEncoder` and deprecate the then old `sparse` parameter.
### Background
Several estimators have an option to return sparse output.
- `RandomTreesEmbedding(sparse_output=True)`
- `LabelBinarizer(sparse_o... | 24,265 | [
-0.013757673092186451,
0.02664872445166111,
0.04496125131845474,
-0.02090032771229744,
0.04533051326870918,
-0.004270200617611408,
0.03734061121940613,
0.07816480845212936,
-0.054889895021915436,
-0.01733909174799919,
0.06867055594921112,
0.04989131912589073,
0.0121773025020957,
0.04684033... |
https://github.com/scikit-learn/scikit-learn/issues/24265 | [
"module:preprocessing"
] | Rename OneHotEncoder option sparse to sparse_output
### Task
Introduce new parameter `sparse_output` in `OneHotEncoder` and deprecate the then old `sparse` parameter.
### Background
Several estimators have an option to return sparse output.
- `RandomTreesEmbedding(sparse_output=True)`
- `LabelBinarizer(sparse_o... | 24,265 | [
-0.013989088125526905,
0.054734617471694946,
0.02873564139008522,
-0.03494071587920189,
0.02554880455136299,
0.004953420255333185,
0.06369814276695251,
0.043021898716688156,
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-0.01613030955195427,
0.08101681619882584,
0.04674727842211723,
0.02310820110142231,
0.0225180... |
https://github.com/scikit-learn/scikit-learn/issues/24265 | [
"module:preprocessing"
] | Rename OneHotEncoder option sparse to sparse_output
### Task
Introduce new parameter `sparse_output` in `OneHotEncoder` and deprecate the then old `sparse` parameter.
### Background
Several estimators have an option to return sparse output.
- `RandomTreesEmbedding(sparse_output=True)`
- `LabelBinarizer(sparse_o... | 24,265 | [
-0.026082202792167664,
0.04197319597005844,
0.027483517304062843,
-0.022964784875512123,
0.03391501307487488,
-0.0029741376638412476,
0.034113559871912,
0.05300749093294144,
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-0.015759747475385666,
0.06427092105150223,
0.04172903671860695,
0.024607744067907333,
0.02131... |
https://github.com/scikit-learn/scikit-learn/issues/24263 | [
"Needs Triage"
] | Problem with ransac predictions.
I think I have encountered a bug in sklearn.linear_model.RANSACRegressor.
I was using it to simplify OpenCV contours, on the occupancy grid map. Unfortunately, RANSAC was pretty bad at detecting almost straight points perpendicular to the x-axis. I have checked it on a simpler train... | 24,263 | [
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-0.04186321794986725,
0.01641962118446827,
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0.03493626415729523,
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0.05825383588671684,
-0.001507589709945023,
0.058867... |
https://github.com/scikit-learn/scikit-learn/issues/24262 | [
"Easy",
"cython"
] | MAINT Simplify some `KMeans` routines' signatures
Looking at the [scikit-learn's `_kmeans_single_lloyd`'s routines](https://github.com/scikit-learn/scikit-learn/blob/7bdd426704fc49316b3273fb33de7f6931c22692/sklearn/cluster/_k_means_lloyd.pyx), some of their signatures can be simplified.
For instance:
- `x_squared... | 24,262 | [
-0.017348190769553185,
-0.005269866902381182,
-0.002410612301900983,
0.01823277398943901,
0.022491727024316788,
0.02304181270301342,
0.02377437986433506,
0.0164240300655365,
0.01449543982744217,
-0.015209706500172615,
0.052586887031793594,
0.0455089770257473,
-0.014105894602835178,
0.04092... |
https://github.com/scikit-learn/scikit-learn/issues/24262 | [
"Easy",
"cython"
] | MAINT Simplify some `KMeans` routines' signatures
Looking at the [scikit-learn's `_kmeans_single_lloyd`'s routines](https://github.com/scikit-learn/scikit-learn/blob/7bdd426704fc49316b3273fb33de7f6931c22692/sklearn/cluster/_k_means_lloyd.pyx), some of their signatures can be simplified.
For instance:
- `x_squared... | 24,262 | [
-0.015930840745568275,
-0.011555200442671776,
0.0008498032111674547,
0.013322644867002964,
0.0365162193775177,
0.028921639546751976,
0.02648688293993473,
0.017330052331089973,
0.015266393311321735,
-0.015628863126039505,
0.05622832477092743,
0.06025676801800728,
0.004025349859148264,
0.032... |
https://github.com/scikit-learn/scikit-learn/issues/24257 | [
"Needs Triage"
] | How can I perform multiple transformations of columns with some columns being same across the transformations
For my usecase, I wanted to perform target encoding for some columns (say c1, c2,c3) and I also want to perform imputation for a column (c4) and I now wanted to perform standardscalar (once the previous target... | 24,257 | [
-0.020125148817896843,
0.057539649307727814,
0.008171195164322853,
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0.03509580343961716,
0.042989734560251236,
0.04405103251338005,
0.007312725763767958,
-0.013226109556853771,
-0.027044782415032387,
-0.01956298016011715,
0.011382460594177246,
0.0302859079092741,
0.015... |
https://github.com/scikit-learn/scikit-learn/issues/24255 | [
"New Feature"
] | Random Forest Neighbors
### Describe the workflow you want to enable
Random Regression forests allow for an interpretation where the values at a new point `x` is a weighted linear combination of the values observed in the dataset
<img width="188" alt="image" src="https://user-images.githubusercontent.com/61052993... | 24,255 | [
0.036049552261829376,
0.04513644427061081,
0.02606804110109806,
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0.023366261273622513,
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0.014605074189603329,
0.021979186683893204,
0.027849696576595306,
-0.0209891926497221,
0.02387... |
https://github.com/scikit-learn/scikit-learn/issues/24255 | [
"New Feature"
] | Random Forest Neighbors
### Describe the workflow you want to enable
Random Regression forests allow for an interpretation where the values at a new point `x` is a weighted linear combination of the values observed in the dataset
<img width="188" alt="image" src="https://user-images.githubusercontent.com/61052993... | 24,255 | [
0.026310322806239128,
0.08274782449007034,
0.017978450283408165,
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0.03272134065628052,
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0.0409122072160244,
0.024700727313756943,
0.0047414060682058334,
0.04206182435154915,
-0.02914462424814701,
0.010... |
https://github.com/scikit-learn/scikit-learn/issues/24255 | [
"New Feature"
] | Random Forest Neighbors
### Describe the workflow you want to enable
Random Regression forests allow for an interpretation where the values at a new point `x` is a weighted linear combination of the values observed in the dataset
<img width="188" alt="image" src="https://user-images.githubusercontent.com/61052993... | 24,255 | [
0.015007061883807182,
0.0544053316116333,
0.010045276954770088,
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0.019045453518629074,
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0.06783539056777954,
-0.027768967673182487,
0.008... |
https://github.com/scikit-learn/scikit-learn/issues/24255 | [
"New Feature"
] | Random Forest Neighbors
### Describe the workflow you want to enable
Random Regression forests allow for an interpretation where the values at a new point `x` is a weighted linear combination of the values observed in the dataset
<img width="188" alt="image" src="https://user-images.githubusercontent.com/61052993... | 24,255 | [
0.01079933624714613,
0.05134546011686325,
0.012022691778838634,
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0.03131437301635742,
0.00355477724224329,
0.06108520179986954,
-0.024523340165615082,
0.0157... |
https://github.com/scikit-learn/scikit-learn/issues/24255 | [
"New Feature"
] | Random Forest Neighbors
### Describe the workflow you want to enable
Random Regression forests allow for an interpretation where the values at a new point `x` is a weighted linear combination of the values observed in the dataset
<img width="188" alt="image" src="https://user-images.githubusercontent.com/61052993... | 24,255 | [
0.02581562101840973,
0.05221782624721527,
0.02626713179051876,
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0.014507165178656578,
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0.014998207800090313,
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0.04734804481267929,
-0.021585630252957344,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24255 | [
"New Feature"
] | Random Forest Neighbors
### Describe the workflow you want to enable
Random Regression forests allow for an interpretation where the values at a new point `x` is a weighted linear combination of the values observed in the dataset
<img width="188" alt="image" src="https://user-images.githubusercontent.com/61052993... | 24,255 | [
-0.0003485386841930449,
0.04154988005757332,
0.00993467215448618,
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0.004941301420331001,
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0.013180891051888466,
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0.03266932815313339,
0.012867835350334644,
-0.010476477444171906,
0.05985018238425255,
-0.05715814232826233,
0.01... |
https://github.com/scikit-learn/scikit-learn/issues/24255 | [
"New Feature"
] | Random Forest Neighbors
### Describe the workflow you want to enable
Random Regression forests allow for an interpretation where the values at a new point `x` is a weighted linear combination of the values observed in the dataset
<img width="188" alt="image" src="https://user-images.githubusercontent.com/61052993... | 24,255 | [
0.036563172936439514,
0.057244837284088135,
0.02071620337665081,
-0.030918147414922714,
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0.0011996442917734385,
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0.03692147508263588,
0.0102261146530509,
0.009582764469087124,
0.018220864236354828,
-0.009724298492074013,
0.00... |
https://github.com/scikit-learn/scikit-learn/issues/24255 | [
"New Feature"
] | Random Forest Neighbors
### Describe the workflow you want to enable
Random Regression forests allow for an interpretation where the values at a new point `x` is a weighted linear combination of the values observed in the dataset
<img width="188" alt="image" src="https://user-images.githubusercontent.com/61052993... | 24,255 | [
0.012995949946343899,
0.05335355922579765,
0.01731518656015396,
-0.030342429876327515,
0.00014626045594923198,
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0.03070363588631153,
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0.0698857232928276,
0.030682308599352837,
-0.002219964051619172,
0.058086708188056946,
-0.024492323398590088,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24254 | [
"module:metrics",
"Needs Investigation"
] | adjusted_mutual_info_score takes a long time with lists containing many unique values
### Describe the bug
The runtime of `adjusted_mutual_info_score` jumps significantly when we have large amounts of unique values in the two lists. Hovering around 6k total unique values (ie 2 columns of 3k unique values) keeps the r... | 24,254 | [
-0.01811455935239792,
-0.021208520978689194,
0.009649980813264847,
-0.015758445486426353,
0.06124822050333023,
0.011905652470886707,
-0.014858518727123737,
0.04825940355658531,
-0.014286944642663002,
-0.033111322671175,
-0.006311334669589996,
0.054703451693058014,
0.0027584400959312916,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/24254 | [
"module:metrics",
"Needs Investigation"
] | adjusted_mutual_info_score takes a long time with lists containing many unique values
### Describe the bug
The runtime of `adjusted_mutual_info_score` jumps significantly when we have large amounts of unique values in the two lists. Hovering around 6k total unique values (ie 2 columns of 3k unique values) keeps the r... | 24,254 | [
-0.01811455935239792,
-0.021208520978689194,
0.009649980813264847,
-0.015758445486426353,
0.06124822050333023,
0.011905652470886707,
-0.014858518727123737,
0.04825940355658531,
-0.014286944642663002,
-0.033111322671175,
-0.006311334669589996,
0.054703451693058014,
0.0027584400959312916,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/24254 | [
"module:metrics",
"Needs Investigation"
] | adjusted_mutual_info_score takes a long time with lists containing many unique values
### Describe the bug
The runtime of `adjusted_mutual_info_score` jumps significantly when we have large amounts of unique values in the two lists. Hovering around 6k total unique values (ie 2 columns of 3k unique values) keeps the r... | 24,254 | [
-0.01811455935239792,
-0.021208520978689194,
0.009649980813264847,
-0.015758445486426353,
0.06124822050333023,
0.011905652470886707,
-0.014858518727123737,
0.04825940355658531,
-0.014286944642663002,
-0.033111322671175,
-0.006311334669589996,
0.054703451693058014,
0.0027584400959312916,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/24254 | [
"module:metrics",
"Needs Investigation"
] | adjusted_mutual_info_score takes a long time with lists containing many unique values
### Describe the bug
The runtime of `adjusted_mutual_info_score` jumps significantly when we have large amounts of unique values in the two lists. Hovering around 6k total unique values (ie 2 columns of 3k unique values) keeps the r... | 24,254 | [
-0.01811455935239792,
-0.021208520978689194,
0.009649980813264847,
-0.015758445486426353,
0.06124822050333023,
0.011905652470886707,
-0.014858518727123737,
0.04825940355658531,
-0.014286944642663002,
-0.033111322671175,
-0.006311334669589996,
0.054703451693058014,
0.0027584400959312916,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/24253 | [
"Bug",
"module:cluster"
] | KMeans `predict` method fails when run in ray remote function due to in-place modification of `cluster_centers_` attribute
### Describe the bug
When KMeans object is passed to a ray remote function, calls to method `predict` fail with the following error:
```
------------------------------------------------------... | 24,253 | [
0.010853784158825874,
-0.04127835854887962,
-0.013187014497816563,
-0.022969761863350868,
0.0781182199716568,
-0.021387601271271706,
0.047766029834747314,
0.005812344141304493,
-0.060590628534555435,
0.04435763508081436,
-0.016454439610242844,
0.10357736051082611,
0.008921450935304165,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24253 | [
"Bug",
"module:cluster"
] | KMeans `predict` method fails when run in ray remote function due to in-place modification of `cluster_centers_` attribute
### Describe the bug
When KMeans object is passed to a ray remote function, calls to method `predict` fail with the following error:
```
------------------------------------------------------... | 24,253 | [
0.010853784158825874,
-0.04127835854887962,
-0.013187014497816563,
-0.022969761863350868,
0.0781182199716568,
-0.021387601271271706,
0.047766029834747314,
0.005812344141304493,
-0.060590628534555435,
0.04435763508081436,
-0.016454439610242844,
0.10357736051082611,
0.008921450935304165,
0.0... |
https://github.com/scikit-learn/scikit-learn/issues/24249 | [
"Documentation"
] | Docstring typo 📜
https://github.com/scikit-learn/scikit-learn/blob/36958fb240fbe435673a9e3c52e769f01f36bec0/sklearn/feature_extraction/text.py#L421
Typo makes docstring unclear and ambiguous:
```py
def build_analyzer(self):
"""Return a callable to process input data.
The callable handles that han... | 24,249 | [
0.07361113280057907,
-0.024220777675509453,
-0.015170336700975895,
-0.007827912457287312,
0.02908407896757126,
0.02024339698255062,
0.06688951700925827,
-0.0172501802444458,
0.021399961784482002,
-0.055010002106428146,
0.05658426880836487,
0.035078395158052444,
0.033222489058971405,
0.0774... |
https://github.com/scikit-learn/scikit-learn/issues/24249 | [
"Documentation"
] | Docstring typo 📜
https://github.com/scikit-learn/scikit-learn/blob/36958fb240fbe435673a9e3c52e769f01f36bec0/sklearn/feature_extraction/text.py#L421
Typo makes docstring unclear and ambiguous:
```py
def build_analyzer(self):
"""Return a callable to process input data.
The callable handles that han... | 24,249 | [
0.07291952520608902,
-0.025503767654299736,
-0.01814054511487484,
-0.007159742061048746,
0.02384212426841259,
0.016300808638334274,
0.06431811302900314,
-0.028471793979406357,
0.024782545864582062,
-0.0450407937169075,
0.04486159607768059,
0.03400013595819473,
0.04324668273329735,
0.061406... |
https://github.com/scikit-learn/scikit-learn/issues/24249 | [
"Documentation"
] | Docstring typo 📜
https://github.com/scikit-learn/scikit-learn/blob/36958fb240fbe435673a9e3c52e769f01f36bec0/sklearn/feature_extraction/text.py#L421
Typo makes docstring unclear and ambiguous:
```py
def build_analyzer(self):
"""Return a callable to process input data.
The callable handles that han... | 24,249 | [
0.07477967441082001,
-0.0009813661454245448,
-0.009101753123104572,
-0.013660628348588943,
-0.009008431807160378,
0.013979626819491386,
0.053735800087451935,
-0.029127061367034912,
0.012097899802029133,
-0.05732771009206772,
0.08341892063617706,
0.03116467408835888,
0.03892863169312477,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24249 | [
"Documentation"
] | Docstring typo 📜
https://github.com/scikit-learn/scikit-learn/blob/36958fb240fbe435673a9e3c52e769f01f36bec0/sklearn/feature_extraction/text.py#L421
Typo makes docstring unclear and ambiguous:
```py
def build_analyzer(self):
"""Return a callable to process input data.
The callable handles that han... | 24,249 | [
0.07362538576126099,
-0.01567751169204712,
-0.013772197999060154,
-0.007498409133404493,
0.00854938942939043,
0.008331384509801865,
0.06511354446411133,
-0.02440107986330986,
0.010329141281545162,
-0.05265524610877037,
0.06291535496711731,
0.032885052263736725,
0.03385486081242561,
0.07898... |
https://github.com/scikit-learn/scikit-learn/issues/24249 | [
"Documentation"
] | Docstring typo 📜
https://github.com/scikit-learn/scikit-learn/blob/36958fb240fbe435673a9e3c52e769f01f36bec0/sklearn/feature_extraction/text.py#L421
Typo makes docstring unclear and ambiguous:
```py
def build_analyzer(self):
"""Return a callable to process input data.
The callable handles that han... | 24,249 | [
0.07194273918867111,
-0.027691086754202843,
-0.015366628766059875,
-0.01414827536791563,
0.024572229012846947,
0.01754477620124817,
0.07051707059144974,
-0.03549036756157875,
0.02271917834877968,
-0.045910514891147614,
0.047990214079618454,
0.032168686389923096,
0.04153028875589371,
0.0644... |
https://github.com/scikit-learn/scikit-learn/issues/24248 | [
"Documentation",
"Needs Triage"
] | Issue with class template with 2 semi columns instead of a single one
### Describe the issue linked to the documentation
In the documentation, it appears that the section has 2 semi-columns instead of a single one:
![image](https://user-images.githubusercontent.com/7454015/186420230-489d6e13-9355-4e36-b89a-600bb68... | 24,248 | [
0.06131494417786598,
-0.023272810503840446,
0.0066230408847332,
0.063203364610672,
0.0614376924932003,
0.04922435060143471,
0.033976055681705475,
0.04599704593420029,
-0.0622955746948719,
-0.024931278079748154,
0.0025002621114253998,
-0.035112787038087845,
0.01634049415588379,
-0.043227396... |
https://github.com/scikit-learn/scikit-learn/issues/24248 | [
"Documentation",
"Needs Triage"
] | Issue with class template with 2 semi columns instead of a single one
### Describe the issue linked to the documentation
In the documentation, it appears that the section has 2 semi-columns instead of a single one:
![image](https://user-images.githubusercontent.com/7454015/186420230-489d6e13-9355-4e36-b89a-600bb68... | 24,248 | [
0.04236655682325363,
-0.054585009813308716,
0.0018998231971636415,
0.041327446699142456,
0.08007016777992249,
0.050217241048812866,
0.06933674961328506,
0.025575177744030952,
-0.02535216696560383,
-0.005898614414036274,
0.018584206700325012,
-0.0067497300915420055,
0.020242387428879738,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/24248 | [
"Documentation",
"Needs Triage"
] | Issue with class template with 2 semi columns instead of a single one
### Describe the issue linked to the documentation
In the documentation, it appears that the section has 2 semi-columns instead of a single one:
![image](https://user-images.githubusercontent.com/7454015/186420230-489d6e13-9355-4e36-b89a-600bb68... | 24,248 | [
0.030238715931773186,
-0.015215477906167507,
-0.0031584594398736954,
0.019460076466202736,
0.05327926576137543,
0.029254352673888206,
0.04737285524606705,
0.06466278433799744,
-0.06477919965982437,
0.00669008819386363,
-0.0077120778150856495,
-0.049127012491226196,
0.026044413447380066,
-0... |
https://github.com/scikit-learn/scikit-learn/issues/24248 | [
"Documentation",
"Needs Triage"
] | Issue with class template with 2 semi columns instead of a single one
### Describe the issue linked to the documentation
In the documentation, it appears that the section has 2 semi-columns instead of a single one:
![image](https://user-images.githubusercontent.com/7454015/186420230-489d6e13-9355-4e36-b89a-600bb68... | 24,248 | [
0.05742795392870903,
-0.020670056343078613,
0.019793221727013588,
0.04658934846520424,
0.05390224978327751,
0.05153713375329971,
0.03609484061598778,
0.04880908504128456,
-0.05938525125384331,
-0.0313916839659214,
0.0003996910818386823,
-0.03676103428006172,
0.012752150185406208,
-0.058642... |
https://github.com/scikit-learn/scikit-learn/issues/24247 | [
"New Feature",
"module:model_selection"
] | Add RepeatedStratifiedGroupKFold
### Describe the workflow you want to enable
Building off conversation #13621 and work already done in #18649, I'd like to add an implementation of `RepeatedStratifiedGroupKFold`.
### Describe your proposed solution
See the implementation in #24227. Then `RepeatedStratifiedGroupKFol... | 24,247 | [
-0.037183698266744614,
0.0894777774810791,
-0.003208042122423649,
0.03600315749645233,
0.009451772086322308,
0.01578729785978794,
0.10383851826190948,
0.026605704799294472,
0.01111490186303854,
-0.04566819220781326,
0.011488123796880245,
0.029774000868201256,
-0.03510858118534088,
0.036082... |
https://github.com/scikit-learn/scikit-learn/issues/24243 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit add skip parameter
### Describe the workflow you want to enable
Dear sklearn community, I want to make an hour ahead forecast on a timeseries of at least a year with an interval of 5 minutes. To do CV, I can use TimeSeriesSplit with test_size = 12. I want to do this for many different times of the d... | 24,243 | [
-0.059653379023075104,
0.048504896461963654,
-0.018595635890960693,
-0.014617483131587505,
-0.002830627840012312,
0.01608089916408062,
0.08434174209833145,
0.023513546213507652,
0.040538009256124496,
0.011141224764287472,
0.07077596336603165,
-0.019768457859754562,
-0.05584844574332237,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24243 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit add skip parameter
### Describe the workflow you want to enable
Dear sklearn community, I want to make an hour ahead forecast on a timeseries of at least a year with an interval of 5 minutes. To do CV, I can use TimeSeriesSplit with test_size = 12. I want to do this for many different times of the d... | 24,243 | [
-0.059653379023075104,
0.048504896461963654,
-0.018595635890960693,
-0.014617483131587505,
-0.002830627840012312,
0.01608089916408062,
0.08434174209833145,
0.023513546213507652,
0.040538009256124496,
0.011141224764287472,
0.07077596336603165,
-0.019768457859754562,
-0.05584844574332237,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24243 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit add skip parameter
### Describe the workflow you want to enable
Dear sklearn community, I want to make an hour ahead forecast on a timeseries of at least a year with an interval of 5 minutes. To do CV, I can use TimeSeriesSplit with test_size = 12. I want to do this for many different times of the d... | 24,243 | [
-0.059653379023075104,
0.048504896461963654,
-0.018595635890960693,
-0.014617483131587505,
-0.002830627840012312,
0.01608089916408062,
0.08434174209833145,
0.023513546213507652,
0.040538009256124496,
0.011141224764287472,
0.07077596336603165,
-0.019768457859754562,
-0.05584844574332237,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24243 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit add skip parameter
### Describe the workflow you want to enable
Dear sklearn community, I want to make an hour ahead forecast on a timeseries of at least a year with an interval of 5 minutes. To do CV, I can use TimeSeriesSplit with test_size = 12. I want to do this for many different times of the d... | 24,243 | [
-0.059653379023075104,
0.048504896461963654,
-0.018595635890960693,
-0.014617483131587505,
-0.002830627840012312,
0.01608089916408062,
0.08434174209833145,
0.023513546213507652,
0.040538009256124496,
0.011141224764287472,
0.07077596336603165,
-0.019768457859754562,
-0.05584844574332237,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24243 | [
"New Feature",
"module:model_selection",
"Needs Decision - Include Feature"
] | TimeSeriesSplit add skip parameter
### Describe the workflow you want to enable
Dear sklearn community, I want to make an hour ahead forecast on a timeseries of at least a year with an interval of 5 minutes. To do CV, I can use TimeSeriesSplit with test_size = 12. I want to do this for many different times of the d... | 24,243 | [
-0.059653379023075104,
0.048504896461963654,
-0.018595635890960693,
-0.014617483131587505,
-0.002830627840012312,
0.01608089916408062,
0.08434174209833145,
0.023513546213507652,
0.040538009256124496,
0.011141224764287472,
0.07077596336603165,
-0.019768457859754562,
-0.05584844574332237,
0.... |
https://github.com/scikit-learn/scikit-learn/issues/24240 | [
"Build / CI"
] | ⚠️ CI failed on Linux_Docker.debian_atlas_32bit ⚠️
**CI is still failing on [Linux_Docker.debian_atlas_32bit](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=45919&view=logs&j=aabdcdc3-bb64-5414-b357-ed024fe8659e)** (Aug 25, 2022)
- test_fastica_simple[27-float32-True]
- test_fastica_simple[27-f... | 24,240 | [
-0.02597183920443058,
-0.023510724306106567,
-0.03242238238453865,
-0.04374735802412033,
0.028516193851828575,
0.03127912059426308,
0.02738766558468342,
0.03989230841398239,
0.011268340982496738,
0.04739587754011154,
0.028329215943813324,
0.020498141646385193,
0.02336871065199375,
0.043792... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24238 | [
"Bug",
"Needs Triage"
] | AttributeError: 'NoneType' object has no attribute 'split'
### Describe the bug
I am using SMOTE to sample a binary classification dataset (churn or not). For multi-label, it works, but when I use the same function on a binary dataset it fails with the following error:
```
AttributeError: 'NoneType' object has no... | 24,238 | [
-0.017901303246617317,
-0.04451756924390793,
0.02618256025016308,
-0.026963448151946068,
0.07537317276000977,
-0.0008382516680285335,
0.09328123182058334,
0.027417046949267387,
0.018694769591093063,
-0.029558081179857254,
0.017568377777934074,
0.04624723270535469,
-0.0029900309164077044,
0... |
https://github.com/scikit-learn/scikit-learn/issues/24228 | [
"module:neighbors",
"Needs Investigation"
] | Balltree's node_data radii seems to be incorrect
### Describe the bug
The node data from constructing a BallTree seems to be incorrect. Each parent node's radius should always be greater than or equal to both its child's radiuses, since the radius is that of the smallest hypersphere containing all the points within... | 24,228 | [
-0.00919004250317812,
-0.09733309596776962,
-0.022538861259818077,
0.031611811369657516,
0.010466407053172588,
-0.01708388142287731,
-0.03195609152317047,
-0.009588389657437801,
-0.06405659765005112,
0.002828695345669985,
0.03399921953678131,
-0.022908397018909454,
0.03774338215589523,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24228 | [
"module:neighbors",
"Needs Investigation"
] | Balltree's node_data radii seems to be incorrect
### Describe the bug
The node data from constructing a BallTree seems to be incorrect. Each parent node's radius should always be greater than or equal to both its child's radiuses, since the radius is that of the smallest hypersphere containing all the points within... | 24,228 | [
-0.00919004250317812,
-0.09733309596776962,
-0.022538861259818077,
0.031611811369657516,
0.010466407053172588,
-0.01708388142287731,
-0.03195609152317047,
-0.009588389657437801,
-0.06405659765005112,
0.002828695345669985,
0.03399921953678131,
-0.022908397018909454,
0.03774338215589523,
-0.... |
https://github.com/scikit-learn/scikit-learn/issues/24228 | [
"module:neighbors",
"Needs Investigation"
] | Balltree's node_data radii seems to be incorrect
### Describe the bug
The node data from constructing a BallTree seems to be incorrect. Each parent node's radius should always be greater than or equal to both its child's radiuses, since the radius is that of the smallest hypersphere containing all the points within... | 24,228 | [
-0.00919004250317812,
-0.09733309596776962,
-0.022538861259818077,
0.031611811369657516,
0.010466407053172588,
-0.01708388142287731,
-0.03195609152317047,
-0.009588389657437801,
-0.06405659765005112,
0.002828695345669985,
0.03399921953678131,
-0.022908397018909454,
0.03774338215589523,
-0.... |
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