ENH Adds TargetEncoder #25334

thomasjpfan · 2023-01-08T21:24:57Z

Reference Issues/PRs

Closes #5853
Closes #9614
Supersedes #17323
Fixes or at least related to #24967

What does this implement/fix? Explain your changes.

This PR implements a target encoder which uses CV during fit_transform to prevent the target from leaking. transform uses the the target encoding from all the training data. This means that fit_transform() != fit().transform().

The implementation uses Cython to learn the encoding which provides a 10x speed up compared to using a pure Python+NumPy approach. Cython is required because many encodings are learn during cross validation in fit_transform.

Any other comments?

The implementation uses the same scheme as cuML's TargetEncoder, which they used to win Recsys2020.

glemaitre · 2023-01-09T09:09:43Z

Do we want to have Regressor in the naming since we could make the encoder work on both regression and classification problem?

sklearn/preprocessing/_target_encoder.py

examples/preprocessing/plot_target_regressor_encoder.py

betatim · 2023-01-09T11:08:17Z

Do we want to have Regressor in the naming since we could make the encoder work on both regression and classification problem?

To have a encoder that works for both classification and regression I think we'd have to detect the type of problem based on the vaules of y. How good is the automatic detection of "regression vs classification"? In addition it feels like the code for classification, in particular multi-class classification, would be more complicated than for the regression case (you can't use np.mean(y)).

I think the way this works is that when performing the change from "categories" to "encoded categories" _BaseEncoder._transform returns a version of X where a column representing a categorical feature is replaced by a column of integers. This is then translated to the actual encoded value in _transform_X_int. On top we have the complexity of fit().transform() and fit_transform() not doing exactly the same thing. It took a lot of going back and forth and working out what _BaseEncoder does to get to that understanding.

After reading the code and example once my big picture thoughts are:

nice!
this looks like it should work,
can we have more doc strings and comments that explain why something is the way it is
both doc string and naming stuff should be done after code structure, etc
right now sample weights aren't supported, would it be easy to add that?

What is your plan? Is it ready to go or does it need more tweaking?

glemaitre · 2023-01-09T13:25:43Z

How good is the automatic detection of "regression vs classification"?

type_of_target is quite good and would do the job.

In addition it feels like the code for classification, in particular multi-class classification, would be more complicated than for the regression case (you can't use np.mean(y)).

dirty_cat implements both but this is not the same branch of code: https://github.com/dirty-cat/dirty_cat/blob/main/dirty_cat/_target_encoder.py

betatim · 2023-01-09T15:37:54Z

What can the TargetEncoder from dirty cat not do/what is the advantage of making yet another implementation over taking it verbatim? For example from the constructor args of it, it seems it supports regression, binary and multi-class classification. It seems like an excellent starting point

glemaitre · 2023-01-09T15:56:36Z

From the dev meeting, we thought that could put aside the multiclass on the side for the moment and support binary classification and regression. We need to make sure that the name of the encoder reflects that but we don't have to support all possible classification and regression problems at first.

I will make a review having those points in mind.

glemaitre · 2023-01-09T15:59:31Z

What can the TargetEncoder from dirty cat not do/what is the advantage of making yet another implementation over taking it verbatim?

Since OneHotEncoder and OrdinalEncoder are working for all problems, it could come as a surprise to our user to have to select the right encoder type. There is also the precedent of categorical_encoder, cuml, dirty_cat (for the one that I am aware of) that are exposing a single encoder for all usage.

betatim · 2023-01-09T16:31:24Z

Since OneHotEncoder and OrdinalEncoder are working for all problems, it could come as a surprise to our user to have to select the right encoder type.

I agree. Having one encoder for all types of problems is nicer than having to choose.

My question was "Why not take the dirty_cat implementation of TargetEncoder (as a starting point) for the implementation in scikit-learn?"

thomasjpfan · 2023-01-09T22:50:30Z

Here are the key differences between this PR and dirty_cat's version:

This PR takes advantage of the existing code in _BaseEncoder, which greatly simplifies the implementation.
This PR does cross validation in fit_transform while dirty cat does not. The cross validation is required to prevent the target from leaking during training.
This PR's implementation is much faster because of the Cython, even with cross validation in fit_transform:

from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import TargetRegressorEncoder
from dirty_cat import TargetEncoder as DirtyCatTargetEncoder
import numpy as np

rng = np.random.default_rng()
n_samples, n_features = 500_000, 20
X = rng.integers(0, high=30, size=(n_samples, n_features))
y = rng.standard_normal(size=n_samples)

%%timeit
_ = TargetRegressorEncoder().fit_transform(X, y)
# 3.37 s ± 132 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
_ = DirtyCatTargetEncoder().fit_transform(X, y)
# 9.9 s ± 220 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

This PR is faster even when it's fit_transform learns 6 encodings: five for cv=5 and one more encoding over the whole dataset. dirty_cat only learns one encoding for the whole dataset because it does not do cross validation.

thomasjpfan · 2023-01-10T00:28:44Z

What is your plan? Is it ready to go or does it need more tweaking?

It's mostly to decide on how we want to extend the API for classification targets. Currently, this PR is the minimum requirement for regression targets. The core computation in this PR can be extended to classification without too much trouble.

type_of_target is quite good and would do the job.

I do not like how type_of_target will treats floats as classification targets. For example:

import numpy as np
from sklearn.utils.multiclass import type_of_target

type_of_target(np.asarray([1.0] * 10 + [2.0] * 30 + [4.0] * 10 + [5.0]))
# 'multiclass'

I prefer two more explicit options:

TargetRegressorEncoder and TargetClassificationEncoder
TargetEncoder with a target_type parameter to switch between regression and classification.

I went with option 1 in this PR, but I am okay with either option. For option 2, I am +0.5 on having a target_type="auto" option that will infer the type of the target.

thomasjpfan · 2023-01-10T01:03:25Z

After thinking about it a little more, I am okay with just inferring the target type with type_of_target. I updated this PR:

Infer the target type with type_of_target
Added a target_type="auto" option to allow the user to control the inference.
Support both binary classification and regression
The encoder is now called TargetEncoder

ogrisel

Looks good to me. Based on test_target_encoding_for_linear_regression I think we need to do the nested cross-val by default and break the usual fit_transform <=> fit + transform implicit equivalence of other scikit-learn estimators. In particular, I don't see how to compute the "real" training accuracy: to do so we would need a fit_score method on pipelines (which could be a good idea by the way to save some redundant computation, but this is a digression).

Anyways, I don't see any other way around, and to me the protection against catastrophic overfitting caused by noisy high-cardinality categorical features outweighs the potentially surprising (but well documented) behavior of fit_transform.

thomasjpfan · 2023-03-14T16:16:35Z

I am still no decided whether this should better be a pitfall-style example or a test.

I think both are useful. I have not seen an example similar to your test case that demonstrates why the internal validation is useful.

In a follow up PR, we can convert the test into a pitfall style example and link it in the docstring for cv. (I'm still unsure about adding cv=None)

ogrisel

Spotted another typo in the inline comment of the new test.

sklearn/preprocessing/tests/test_target_encoder.py

ogrisel · 2023-03-14T19:18:33Z

@lorentzenchr @betatim @glemaitre any more feedback?

@jovan-stojanovic you might be interested in the new test: I checked that dirty_cat's TargetEncoder can also lead to this kind of overfitting problems because of the lack of built-in CV during fit_transform.

ogrisel · 2023-03-15T13:45:43Z

@thomasjpfan I had a devil inspired idea at coffee: we could store a weakref to the traininset set at fit time to detect if the X passed to .transform is the same as the one passed to .fit/.fit_transform. That would make it possible to hide the fit_transform / fit + transform discrepancy like cuML does but without holding a strong ref to X_train that would be problematic as it would prevent early collection of otherwise GC-able data and would make model serialization inefficient without additional care.

Still the weakref hack could still lead to surprising behaviors. For instance, while the following would work:

X_train = load_dataset_from_disk()
X_trans_1 = target_transformer.fit_transform(X_train)
X_trans_2 = target_transformer.transform(X_train)
np.assert_allclose(X_trans_1, X_trans_2)

this seemingly innocuous variation would fail:

X_train = load_dataset_from_disk()
X_trans_1 = target_transformer.fit_transform(X_train)

X_train = load_dataset_from_disk()
X_trans_2 = target_transformer.transform(X_train)
np.assert_allclose(X_trans_1, X_trans_2)

so overall, I am not 100% sure the weakref hack would be a usability improvement or not.

Feel free to pretend that you haven't read this comment and not reply. I would perfectly understand.

ogrisel · 2023-03-15T17:23:48Z

Another pitfall I discovered when experimenting with this PR:

If you have a mix of informative and non-informative categorical features (e.g. f_i and f_u), and if you apply StandardScaler() after TargetEncoder() then you can run in big troubles: the variance of target_encoder.fit_tramsform(X_train[[f_u]]) can be very small but non-zero, so StandarScaler can rescale this massively which is probably a bad idea for a downstream distance based model (e.g. a kernel machine, nearest neighbors and so on).

However if you use the raw target encoded values of f_i and f_u in conjunction of true numerical features that have all been scaled (via a dedicated entry in a column transformers) and if y has a much larger standard deviation, then f_i will have a very large standard deviation compared to the independently scaled numerical features, which is problematic.

I see two possible solutions:

a) add an option to TargetEncoder to internally use a scaled version of y_train to compute the encodings;
b) make it possible for StandardScaler or those scalers to use shared mean and scale statistics for a group of columns:

preprocessor = ColumnTransformer(
    [
        (
            "categorical",
             make_pipeline(TargetEncoder(), StandardScaler(shared_mean=True, shared_scale=True),
            ["f_i", "f_u"],
        ),
    ],
    remainder=StandardScaler(),
)

Option b) is probably useful beyond post-processing target encoded categorical values. For instance @TomDLT and I encountered a similar problem a long time ago when running make_pipeline(StandardScaler(), LogisticRegression()) with the saga solver on MNIST: some pixel values are nearly constant 0 or 1 and having per-feature scaling is catastrophically hurting the condition number of X.T @ X if I recall correctly. At the time we just decided to use MinMaxScaler instead and move along. But this is a real problems when groups of features should be re-scaled together to preserve meaning (e.g. pixel intensities, EEG channels, MEL features for audio signal and so on).

EDIT: we should probably do StandardScaler(shared_mean_and_scale=True) instead because decoupling the two might be challenging from a code maintenance point of view, and not that useful to the end users.

Even if we decide would also be a) convenient UX improvement, we can delay this to a follow-up PR.

I just wanted to brain-dump this here so that we can think about it when we work on a pitfall example for TargetEncoder-based feature engineering.

/cc @jovan-stojanovic who might also be interested for dirty_cat.

thomasjpfan · 2023-03-15T19:11:10Z

so overall, I am not 100% sure the weakref hack would be a usability improvement or not.

I think it borders on being too magical. For example, if the data is sliced the same way or copied, the references are not the same:

import numpy as np
import weakref

X = np.random.randn(10, 10)

X1 = X[:4]
X2 = X[:4]
X3 = X1.copy()

X1_ref = weakref.ref(X1)

assert X1 is X1_ref()
assert X2 is not X1_ref()
assert X3 is not X1_ref()

For reference, cuML's TargetEncoder holds the training data and checks all the values.

a) add an option to TargetEncoder to internally use a scaled version of y_train to compute the encodings;

At one point, I had something similar implemented in #17323 as the default. I think it's reasonable to use a scaled version of the target for encoding purposes.

TomDLT · 2023-03-15T19:29:23Z

For instance @TomDLT and I encountered a similar problem a long time ago when running make_pipeline(StandardScaler(), LogisticRegression()) with the saga solver on MNIST: some pixel values are nearly constant 0 or 1 and having per-feature scaling is catastrophic.

Yes, some MNIST pixels reach a value of 250 after rescaling (see Details below), which is quite out of distribution for a unit-norm Normal distribution. We should add a warning when some outputs of StandardScaler are large (say above 100, or even 10).

This is a real problems when groups of features should be re-scaled together to preserve meaning (e.g. pixel intensities, EEG channels, MEL features for audio signal and so on).

Yes, this is a big limitation of StandardScaler. +1 for adding StandardScaler(shared_mean=True, shared_scale=True).

import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm

from sklearn.datasets import fetch_openml
from sklearn.preprocessing import StandardScaler

mnist = fetch_openml("mnist_784", as_frame=False, parser="pandas")
X, y = mnist.data, mnist.target

X_scaled = StandardScaler().fit_transform(X)
max_values = X_scaled.max(axis=0)

fig, ax = plt.subplots()
image = ax.imshow(max_values.reshape(28, 28), cmap=plt.get_cmap("viridis", 6),
                  norm=LogNorm())
ax.set(xticks=[], yticks=[], title="Maximum value of each scaled MNIST feature")
fig.colorbar(image)
plt.show()

ogrisel · 2023-03-16T09:06:57Z

a) add an option to TargetEncoder to internally use a scaled version of y_train to compute the encodings;

At one point, I had something similar implemented in #17323 as the default. I think it's reasonable to use a scaled version of the target for encoding purposes.

Let's keep that in mind for a follow-up PR. But it we want to make it the default (which would probably be helpful), we should probably do that before the 1.3 release.

ogrisel · 2023-03-16T12:30:12Z

For reference, cuML's TargetEncoder holds the training data and checks all the values.

Note that we could use a weakref + a concrete value check. But even that would feel to complex/magical. +0.5 for keeping the code as it is.

glemaitre

I have only one nitpick.

examples/preprocessing/plot_target_encoder.py

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

ogrisel · 2023-03-16T15:59:39Z

Merged! Thank you very much @thomasjpfan!

lorentzenchr · 2023-03-16T21:37:14Z

Just an idea: For detection of the training set during transform, we could store mean, variance and maybe 2 quantiles. This should be pretty unique.

ogrisel · 2023-03-17T07:52:55Z

The checks need to happen before encoding on the categorical variables. We could store the per feature category counts instead. Maybe with a few random probe records that contain several features with infrequent categories.

ogrisel · 2023-03-17T07:54:59Z

The checks need to happen before encoding on the categorical variables. We could store the per feature category counts instead. Maybe with a few random probe records.

But this would be quite catastrophic in case of false positives.

lorentzenchr · 2023-03-17T11:30:37Z

Now I see the difficulty. Maybe it is good enough as is. In principle, we would need to detect every single row of the training set and that’s the responsibility of the user, isn‘t it.

betatim · 2023-03-17T13:38:21Z

Whoop whoop! Nice work!

BenjaminBossan · 2023-03-20T10:56:54Z

Pretty nice addition, thanks for this.

A small question: According to the tags, TargetEncoder does not require y, should this be changed?

>>> from sklearn.preprocessing import TargetEncoder
>>> from sklearn.utils._tags import _safe_tags
>>> _safe_tags(TargetEncoder())['requires_y']
False

Co-authored-by: Andreas Mueller <t3kcit@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Jovan Stojanovic <62058944+jovan-stojanovic@users.noreply.github.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

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Fan <thomasjpfan@gmail.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Extend message for large sparse matrices support (scikit-learn#25961) Co-authored-by: Meekail Zain <34613774+Micky774@users.noreply.github.com> * MAINT Parameters validation for datasets.make_gaussian_quantiles (scikit-learn#25959) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.metrics.d2_tweedie_score (scikit-learn#25975) * MAINT Parameters validation for datasets.make_hastie_10_2 (scikit-learn#25967) * MAINT Parameters validation for preprocessing.minmax_scale (scikit-learn#25962) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for datasets.make_checkerboard (scikit-learn#25955) * MAINT Parameters validation for datasets.make_biclusters (scikit-learn#25945) * MAINT Parameters validation for datasets.make_moons (scikit-learn#25971) * DOC replace deviance by loss in docstring of GradientBoosting (scikit-learn#25968) * MAINT Fix broken link in feature_selection/_univariate_selection.py (scikit-learn#25984) * DOC Update model_persistence.rst to fix skops example (scikit-learn#25993) Co-authored-by: adrinjalali <adrin.jalali@gmail.com> * DOC Specified meaning for max_patches=None in extract_patches_2d (scikit-learn#25996) * DOC document that last step is never cached in pipeline (scikit-learn#25995) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * FIX SequentialFeatureSelector throws IndexError when cv is a generator (scikit-learn#25973) * ENH Adds infrequent categories support to OrdinalEncoder (scikit-learn#25677) Co-authored-by: Tim Head <betatim@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Andreas Mueller <t3kcit@gmail.com> * MAINT make plot_digits_denoising deterministic by fixing random state (scikit-learn#26004) * DOC improve example of PatchExtractor (scikit-learn#26002) * MAINT Parameters validation for datasets.make_friedman2 (scikit-learn#25986) * MAINT Parameters validation for datasets.make_friedman3 (scikit-learn#25989) * MAINT Parameters validation for datasets.make_sparse_uncorrelated (scikit-learn#26001) * MAINT Parameters validation for datasets.make_spd_matrix (scikit-learn#26003) * MAINT Parameters validation for datasets.make_sparse_spd_matrix (scikit-learn#26009) * DOC Added the meanings of default=None for PatchExtractor parameters (scikit-learn#26005) * MAINT remove unecessary check covered by parameter validation framework (scikit-learn#26014) * MAINT Consistent cython types from _typedefs (scikit-learn#25942) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> * MAINT Parameters validation for datasets.make_swiss_roll (scikit-learn#26020) * MAINT Parameters validation for datasets.make_s_curve (scikit-learn#26022) * MAINT Parameters validation for datasets.make_blobs (scikit-learn#25983) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * DOC fix SplineTransformer include_bias docstring (scikit-learn#26018) * ENH RocCurveDisplay add option to plot chance level (scikit-learn#25987) * DOC show from_estimator and from_predictions for Displays (scikit-learn#25994) * EXA Fix rst in plot_partial_dependence (scikit-learn#26028) * CI Adds coverage to docker jobs on Azure (scikit-learn#26027) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * API Replace `n_iter` in `Bayesian Ridge` and `ARDRegression` (scikit-learn#25697) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * CLN Make _NumPyAPIWrapper naming consistent to _ArrayAPIWrapper (scikit-learn#26039) * CI disable coverage on Windows to keep CI times reasonable (scikit-learn#26052) * DOC Use Scientific Python Plausible instance for analytics (scikit-learn#25547) * MAINT Parameters validation for sklearn.preprocessing.scale (scikit-learn#26036) * MAINT Parameters validation for sklearn.metrics.pairwise.haversine_distances (scikit-learn#26047) * MAINT Parameters validation for sklearn.metrics.pairwise.laplacian_kernel (scikit-learn#26048) * MAINT Parameters validation for sklearn.metrics.pairwise.linear_kernel (scikit-learn#26049) * MAINT Parameters validation for sklearn.metrics.silhouette_samples (scikit-learn#26053) * MAINT Parameters validation for sklearn.preprocessing.add_dummy_feature (scikit-learn#26058) * Added Parameter Validation for metrics.cluster.normalized_mutual_info_score() (scikit-learn#26060) * DOC Typos in HistGradientBoosting documentation (scikit-learn#26057) * TST add global_random_seed fixture to sklearn/datasets/tests/test_rcv1.py (scikit-learn#26043) * MAINT Parameters validation for sklearn.metrics.pairwise.cosine_similarity (scikit-learn#26006) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * ENH Adds isdtype to Array API wrapper (scikit-learn#26029) * MAINT Parameters validation for sklearn.metrics.silhouette_score (scikit-learn#26054) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * FIX fix spelling mistake in _NumPyAPIWrapper (scikit-learn#26064) * CI ignore more non-library Python files in codecov (scikit-learn#26059) * MAINT Parameters validation for sklearn.metrics.pairwise.cosine_distances (scikit-learn#26046) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Introduce BinaryClassifierCurveDisplayMixin (scikit-learn#25969) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * ENH Forces shape to be tuple when using Array API's reshape (scikit-learn#26030) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Tim Head <betatim@gmail.com> * MAINT Parameters validation for sklearn.metrics.pairwise.paired_euclidean_distances (scikit-learn#26073) * MAINT Parameters validation for sklearn.metrics.pairwise.paired_manhattan_distances (scikit-learn#26074) * MAINT Parameters validation for sklearn.metrics.pairwise.paired_cosine_distances (scikit-learn#26075) * MAINT Parameters validation for sklearn.preprocessing.binarize (scikit-learn#26076) * MAINT Parameters validation for metrics.explained_variance_score (scikit-learn#26079) * DOC use correct template name for displays (scikit-learn#26081) * MAINT Parameters validation for sklearn.preprocessing.maxabs_scale (scikit-learn#26077) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.preprocessing.label_binarize (scikit-learn#26078) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT parameter validation for d2_absolute_error_score (scikit-learn#26066) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT Parameter validation for roc_auc_score (scikit-learn#26007) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT Parameters validation for sklearn.preprocessing.normalize (scikit-learn#26069) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT Parameter validation for metrics.cluster.fowlkes_mallows_score (scikit-learn#26080) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT Parameters validation for compose.make_column_transformer (scikit-learn#25897) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT Parameters validation for sklearn.metrics.pairwise.polynomial_kernel (scikit-learn#26070) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.metrics.pairwise.rbf_kernel (scikit-learn#26071) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.metrics.pairwise.sigmoid_kernel (scikit-learn#26072) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Param validation: constraint for numeric missing values (scikit-learn#26085) * FIX Adds support for negative values in categorical features in gradient boosting (scikit-learn#25629) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Tim Head <betatim@gmail.com> * MAINT Fix C warning in Cython module splitting.pyx (scikit-learn#26051) * MNT Updates _isotonic.pyx to use memoryviews instead of `cnp.ndarray` (scikit-learn#26068) * FIX Fixes memory regression for inspecting extension arrays (scikit-learn#26106) * PERF set openmp to use only physical cores by default (scikit-learn#26082) * MNT Update black to 23.3.0 (scikit-learn#26110) * MNT Adds black commit to git-blame-ignore-revs (scikit-learn#26111) * MAINT Parameters validation for sklearn.metrics.pair_confusion_matrix (scikit-learn#26107) * MAINT Parameters validation for sklearn.metrics.mean_poisson_deviance (scikit-learn#26104) * DOC Use notebook style in plot_lof_outlier_detection.py (scikit-learn#26017) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * MAINT utils._fast_dict uses types from utils._typedefs (scikit-learn#26025) * DOC remove sparse-matrix for `y` in ElasticNet (scikit-learn#26127) * ENH add exponential loss (scikit-learn#25965) * MAINT Parameters validation for sklearn.preprocessing.robust_scale (scikit-learn#26086) * MAINT Parameters validation for sklearn.datasets.fetch_rcv1 (scikit-learn#26126) * MAINT Parameters validation for sklearn.metrics.adjusted_rand_score (scikit-learn#26134) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.metrics.calinski_harabasz_score (scikit-learn#26135) * MAINT Parameters validation for sklearn.metrics.davies_bouldin_score (scikit-learn#26136) * MAINT: remove `from numpy.math cimport` statements (scikit-learn#26143) * MAINT Parameters validation for sklearn.inspection.permutation_importance (scikit-learn#26145) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.metrics.cluster.homogeneity_completeness_v_measure (scikit-learn#26137) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.metrics.rand_score (scikit-learn#26138) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * DOC update comment in metrics/tests/test_classification.py (scikit-learn#26150) * CI small cleanup of Cirrus CI test script (scikit-learn#26168) * MAINT remove deprecated is_categorical_dtype (scikit-learn#26156) * DOC Add skforecast to related projects page (scikit-learn#26133) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * FIX Keeps namedtuple's class when transform returns a tuple (scikit-learn#26121) * DOC corrected letter case for better readability in sklearn/metrics/_classification.py / (scikit-learn#26169) * MAINT Parameters validation for sklearn.preprocessing.power_transform (scikit-learn#26142) * FIX `roc_auc_score` now uses `y_prob` instead of `y_pred` (scikit-learn#26155) * MAINT Parameters validation for sklearn.datasets.load_iris (scikit-learn#26177) * MAINT Parameters validation for sklearn.datasets.load_diabetes (scikit-learn#26166) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.datasets.load_breast_cancer (scikit-learn#26165) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.metrics.cluster.entropy (scikit-learn#26162) * MAINT Parameters validation for sklearn.datasets.fetch_species_distributions (scikit-learn#26161) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * ASV Fix tol in SGDRegressorBenchmark (scikit-learn#26146) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * MNT use api.openml.org URLs for fetch_openml (scikit-learn#26171) * MAINT Parameters validation for sklearn.utils.resample (scikit-learn#26139) * MAINT make it explicit that additive_chi2_kernel does not accept sparse matrix (scikit-learn#26178) * MNT fix circleci link in README.rst (scikit-learn#26183) * CI Fix circleci artifact redirector action (scikit-learn#26181) * GOV introduce rights for groups as discussed in SLEP019 (scikit-learn#25753) Co-authored-by: Julien <git@jjerphan.xyz> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * MAINT Parameters validation for sklearn.neighbors.sort_graph_by_row_values (scikit-learn#26173) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * FIX improve convergence criterion for LogisticRegression(penalty="l1", solver='liblinear') (scikit-learn#25214) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * MAINT Fix several typos in src and doc files (scikit-learn#26187) * PERF fix overhead of _rescale_data in LinearRegression (scikit-learn#26207) * ENH add Huber loss (scikit-learn#25966) * MAINT Refactor GraphicalLasso and graphical_lasso (scikit-learn#26033) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Cython linting (scikit-learn#25861) * DOC Add JupyterLite button in example gallery (scikit-learn#25887) * MAINT Parameters validation for sklearn.covariance.ledoit_wolf_shrinkage (scikit-learn#26200) * MAINT Parameters validation for sklearn.datasets.load_linnerud (scikit-learn#26199) * MAINT Parameters validation for sklearn.datasets.load_wine (scikit-learn#26196) * DOC Added redirect to Provost paper + minor refactor (scikit-learn#26223) * MAINT Parameter Validation for `covariance.graphical_lasso` (scikit-learn#25053) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.datasets.load_digits (scikit-learn#26195) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.preprocessing.quantile_transform (scikit-learn#26144) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.model_selection.cross_validate (scikit-learn#26129) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * DOC Adds TargetEncoder example explaining the internal CV (scikit-learn#26185) Co-authored-by: Tim Head <betatim@gmail.com> * spelling mistake corrected in documentation for script `plot_document_clustering.py` (scikit-learn#26228) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * FIX possible UnboundLocalError in fetch_openml (scikit-learn#26236) * ENH Adds PyTorch support to LinearDiscriminantAnalysis (scikit-learn#25956) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Tim Head <betatim@gmail.com> * MNT Use fixed version of Pyodide (scikit-learn#26247) * MNT Reset transform_output default in example to fix doc build build (scikit-learn#26269) * DOC Update example plot_nearest_centroid.py (scikit-learn#26263) * MNT reduce JupyterLite build size (scikit-learn#26246) * DOC term -> meth in GradientBoosting (scikit-learn#26225) * MNT speed-up html-noplot build (scikit-learn#26245) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * MNT Use copy=False when creating DataFrames (scikit-learn#26272) * MAINT Parameters validation for sklearn.model_selection.permutation_test_score (scikit-learn#26230) * MAINT Parameters validation for sklearn.datasets.clear_data_home (scikit-learn#26259) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.datasets.load_files (scikit-learn#26203) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.datasets.get_data_home (scikit-learn#26260) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * DOC Fix y-axis plot labels in permutation test score example (scikit-learn#26240) * MAINT cython-lint ignores asv_benchmarks (scikit-learn#26282) * MAINT Parameter validation for metrics.cluster._supervised (scikit-learn#26258) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * DOC Improve docstring for tol in SequentialFeatureSelector (scikit-learn#26271) * MAINT Parameters validation for sklearn.datasets.load_sample_image (scikit-learn#26226) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * DOC Consistent param type for pos_label (scikit-learn#26237) * DOC Minor grammar fix to imputation docs (scikit-learn#26283) * MAINT Parameters validation for sklearn.calibration.calibration_curve (scikit-learn#26198) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * MAINT Parameters validation for sklearn.inspection.partial_dependence (scikit-learn#26209) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * MAINT Parameters validation for sklearn.model_selection.validation_curve (scikit-learn#26229) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.model_selection.learning_curve (scikit-learn#26227) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * MNT Remove deprecated pandas.api.types.is_sparse (scikit-learn#26287) * CI Use Trusted Publishers for uploading wheels to PyPI (scikit-learn#26249) * MAINT Parameters validation for sklearn.metrics.pairwise.manhattan_distances (scikit-learn#26122) * PERF revert openmp use in csr_row_norms (scikit-learn#26275) * MAINT Parameters validation for metrics.check_scoring (scikit-learn#26041) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MNT Improve error message when checking classification target is of a non-regression type (scikit-learn#26281) Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * DOC fix link to User Guide encoder_infrequent_categories (scikit-learn#26309) * MNT remove unused args in _predict_regression_tree_inplace_fast_dense (scikit-learn#26314) * ENH Adds missing value support for trees (scikit-learn#23595) Co-authored-by: Tim Head <betatim@gmail.com> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> * CLN Clean up logic in validate_data and cast_to_ndarray (scikit-learn#26300) * MAINT refactor scorer using _get_response_values (scikit-learn#26037) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> * DOC Add HGBDT to "see also" section of random forests (scikit-learn#26319) Co-authored-by: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu> Co-authored-by: Tim Head <betatim@gmail.com> * MNT Bump Github Action labeler version to use newer Node (scikit-learn#26302) * FIX thresholds should not exceed 1.0 with probabilities in `roc_curve` (scikit-learn#26194) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * ENH Allow for appropriate dtype us in `preprocessing.PolynomialFeatures` for sparse matrices (scikit-learn#23731) Co-authored-by: Aleksandr Kokhaniukov <alexander.kohanyukov@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * DOC Fix minor typo (scikit-learn#26327) * MAINT bump minimum version for pytest (scikit-learn#26184) Co-authored-by: Loïc Estève <loic.esteve@ymail.com> Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * DOC fix return type in isotonic_regression (scikit-learn#26332) * FIX fix available_if for MultiOutputRegressor.partial_fit (scikit-learn#26333) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * FIX make pipeline pass check_estimator (scikit-learn#26325) * FEA Add multiclass support to `average_precision_score` (scikit-learn#24769) Co-authored-by: Geoffrey <geoffrey.bolmier@gmail.com> Co-authored-by: gbolmier <geoffrey.bolmier@volvocars.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> --------- Signed-off-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Meekail Zain <34613774+Micky774@users.noreply.github.com> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: zeeshan lone <56621467+still-learning-ev@users.noreply.github.com> Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> Co-authored-by: Shiva chauhan <103742975+Shivachauhan17@users.noreply.github.com> Co-authored-by: AymericBasset <45051041+AymericBasset@users.noreply.github.com> Co-authored-by: Maren Westermann <maren.westermann@gmail.com> Co-authored-by: Nishu Choudhary <51842539+choudharynishu@users.noreply.github.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Loïc Estève <loic.esteve@ymail.com> Co-authored-by: Benedek Harsanyi <80836204+hbenedek@users.noreply.github.com> Co-authored-by: Pooja Subramaniam <poojas2086@gmail.com> Co-authored-by: Rushil Desai <rushildesai01@gmail.com> Co-authored-by: Xiao Yuan <yuanx749@gmail.com> Co-authored-by: Omar Salman <omar.salman@arbisoft.com> Co-authored-by: 2357juan <29247195+2357juan@users.noreply.github.com> Co-authored-by: Théophile Baranger <39696928+tbaranger@users.noreply.github.com> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Andreas Mueller <t3kcit@gmail.com> Co-authored-by: Jovan Stojanovic <62058944+jovan-stojanovic@users.noreply.github.com> Co-authored-by: Rahil Parikh <75483881+rprkh@users.noreply.github.com> Co-authored-by: Bharat Raghunathan <bharatraghunthan9767@gmail.com> Co-authored-by: Sortofamudkip <wishyutp0328@gmail.com> Co-authored-by: Gleb Levitski <36483986+glevv@users.noreply.github.com> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com> Co-authored-by: Ashwin Mathur <97467100+awinml@users.noreply.github.com> Co-authored-by: Sahil Gupta <sahil@Sahils-MBP.lan> Co-authored-by: Veghit <itay.vegh@gmail.com> Co-authored-by: Itay <itayvegh@gmail.com> Co-authored-by: precondition <57645186+precondition@users.noreply.github.com> Co-authored-by: Marc Torrellas Socastro <marc.torsoc@gmail.com> Co-authored-by: Dominic Fox <dominicjfox2@gmail.com> Co-authored-by: futurewarning <36329275+futurewarning@users.noreply.github.com> Co-authored-by: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Co-authored-by: Joey Ortiz <orangesherbet0@gmail.com> Co-authored-by: Tim Head <betatim@gmail.com> Co-authored-by: Christian Veenhuis <veenhuis@gmail.com> Co-authored-by: adienes <51664769+adienes@users.noreply.github.com> Co-authored-by: Dave Berenbaum <dave.berenbaum@gmail.com> Co-authored-by: Lene Preuss <lene.preuss@gmail.com> Co-authored-by: A.H.Mansouri <83764851+A-H-Mansoury@users.noreply.github.com> Co-authored-by: Boris Feld <lothiraldan@gmail.com> Co-authored-by: Carla J <ca.jancik@gmail.com> Co-authored-by: windiana42 <61181806+windiana42@users.noreply.github.com> Co-authored-by: mdarii <dariimaxim@gmail.com> Co-authored-by: murezzda <47388020+murezzda@users.noreply.github.com> Co-authored-by: Peter Piontek <piontek0@gmail.com> Co-authored-by: John Pangas <swiftyxswaggy@outlook.com> Co-authored-by: Dmitry Nesterov <76070534+dmitrylala@users.noreply.github.com> Co-authored-by: Yuchen Zhou <72342196+ROMEEZHOU@users.noreply.github.com> Co-authored-by: Ekaterina Butyugina <102963496+ekaterinabutyugina@users.noreply.github.com> Co-authored-by: Jiawei Zhang <jiawei.zhang@nyu.edu> Co-authored-by: Ansam Zedan <86729068+ansamz@users.noreply.github.com> Co-authored-by: genvalen <genvalen@protonmail.com> Co-authored-by: farhan khan <86480450+BabaYaga1221@users.noreply.github.com> Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Co-authored-by: Jiawei Zhang <jz4721@nyu.edu> Co-authored-by: Ralf Gommers <ralf.gommers@gmail.com> Co-authored-by: Jessicakk0711 <106110789+Jessicakk0711@users.noreply.github.com> Co-authored-by: Ankur Singh <singankur28@gmail.com> Co-authored-by: Seoeun(Sun☀️) Hong <75988952+seoeunHong@users.noreply.github.com> Co-authored-by: Nightwalkx <74856680+xi-jiajun@users.noreply.github.com> Co-authored-by: VIGNESH D <35656793+dvignesh1995@users.noreply.github.com> Co-authored-by: Vincent-violet <130581473+Vincent-violet@users.noreply.github.com> Co-authored-by: Elabonga Atuo <elabongaatuo@gmail.com> Co-authored-by: Tom Dupré la Tour <tom.dupre-la-tour@m4x.org> Co-authored-by: André Pedersen <andrped94@gmail.com> Co-authored-by: Ashish Dutt <ashish.dutt8@gmail.com> Co-authored-by: Phil <philsupertramp@users.noreply.github.com> Co-authored-by: Stanislav (Stanley) Modrak <44023416+smith558@users.noreply.github.com> Co-authored-by: hujiahong726 <52920842+hujiahong726@users.noreply.github.com> Co-authored-by: James Dean <24254612+AcylSilane@users.noreply.github.com> Co-authored-by: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu> Co-authored-by: Aleksandr Kokhaniukov <alexander.kohanyukov@gmail.com> Co-authored-by: c-git <43485962+c-git@users.noreply.github.com> Co-authored-by: annegnx <64203599+annegnx@users.noreply.github.com> Co-authored-by: Geoffrey <geoffrey.bolmier@gmail.com> Co-authored-by: gbolmier <geoffrey.bolmier@volvocars.com>

thomasjpfan added 2 commits January 8, 2023 16:13

ENH Adds Target Regression Encoder

f29c2d8

DOC Adds pr number

336b17d

github-actions bot added cython module:preprocessing labels Jan 8, 2023

thomasjpfan mentioned this pull request Jan 8, 2023

ENH Adds Target Regression Encoder (Impact Encoder) #17323

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DOC Adds pr number

c52ce42

thomasjpfan changed the title ~~ENH Adds Target Regression Encoder~~ ENH Adds Target Regressor Encoder Jan 8, 2023

thomasjpfan added 4 commits January 8, 2023 20:32

DOC Adds example

1863be9

DOC Fixes example link

22af6a6

FIX Use fancy indexing

f535f54

FIX Fix issue with 32bit

c6d1fc4

glemaitre reviewed Jan 9, 2023

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betatim reviewed Jan 9, 2023

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thomasjpfan added 2 commits January 9, 2023 20:01

ENH Adds support for binary classification

2868b3c

TST Adds test to check target_type_

aa6e545

thomasjpfan changed the title ~~ENH Adds Target Regressor Encoder~~ ENH Adds TargetEncoder Jan 10, 2023

thomasjpfan added 4 commits January 9, 2023 20:05

DOC Update whats new with better wording

871ea45

DOC Update docs about target encoder itself

71e6bad

TST Update names of tests

fdd39d0

DOC better names for variables

c8d5546

TST Use RNG for permutation

05997bb

ogrisel approved these changes Mar 14, 2023

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ogrisel reviewed Mar 14, 2023

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More fixes in inline comments for the new test

6377419

glemaitre self-requested a review March 16, 2023 12:53

glemaitre approved these changes Mar 16, 2023

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examples/preprocessing/plot_target_encoder.py Outdated Show resolved Hide resolved

Use pandas to parse the fetched the example dataset.

180ee0e

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

ogrisel merged commit 392fdee into scikit-learn:main Mar 16, 2023

beckernick mentioned this pull request Mar 17, 2023

[QST] Evaluate TargetEncoder implementation vs. scikit-learn rapidsai/cuml#5280

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This was referenced Mar 20, 2023

FIX Adds requires_y tag to TargetEncoder #25917

Merged

API Set random_state=0 for TargetEncoder #25927

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thomasjpfan mentioned this pull request Apr 14, 2023

DOC Adds TargetEncoder example explaining the internal CV #26185

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ArturoAmorQ mentioned this pull request Apr 19, 2024

DOC Enhanced example visualization to RFE #28862

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ENH Adds TargetEncoder #25334

ENH Adds TargetEncoder #25334

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ENH Adds TargetEncoder #25334

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