How can I normalize confusion matrix plot?

hello ,when i use the code as follow,the will be some errors, EasyEnsembleClassifier was used

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import balanced_accuracy_score
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from imbalanced_ensemble.ensemble import EasyEnsembleClassifier
from collections import Counter

X, y = make_classification(n_classes=2, class_sep=2,
weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
print('Original dataset shape %s' % Counter(y))

Original dataset shape Counter({{1: 900, 0: 100}})

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
bbc = EasyEnsembleClassifier(random_state=42)
bbc.fit(X_train, y_train)
EasyEnsembleClassifier(...)
y_pred = bbc.predict(X_test)
print(y_pred)

Traceback (most recent call last):
File "C:/Users/Administrator/PycharmProjects/pythonProject5/test-easy.py", line 16, in
bbc.fit(X_train, y_train)
File "C:\Users\Administrator\PycharmProjects\pythonProject5\venv\lib\site-packages\imbalanced_ensemble\utils_validation.py", line 602, in inner_f
return f(**kwargs)
File "C:\Users\Administrator\PycharmProjects\pythonProject5\venv\lib\site-packages\imbalanced_ensemble\ensemble\under_sampling\easy_ensemble.py", line 275, in fit
return self._fit(X, y,
File "C:\Users\Administrator\PycharmProjects\pythonProject5\venv\lib\site-packages\imbalanced_ensemble\utils_validation.py", line 602, in inner_f
return f(**kwargs)
File "C:\Users\Administrator\PycharmProjects\pythonProject5\venv\lib\site-packages\imbalanced_ensemble\ensemble_bagging.py", line 359, in fit
n_samples, self.n_features = X.shape
AttributeError: can't set attribute

You have a missing comma in the INSTALL_REQUIRES list which breaks conda env export.

Context

I'm trying to export my conda environment, but it fails.

conda env export > environment.yaml

InvalidVersionSpec: Invalid version '1.1.3joblib>=0.11': invalid character(s)

After some time digging around I find the cause using the command

# grep -rl "1.1.3joblib>=0.11" /opt/conda/envs/mt-base
/opt/conda/envs/mt-base/lib/python3.10/site-packages/imbalanced_ensemble-0.1.7.dist-info/METADATA

and I end up here :-)

Your package imbalanced-ensemble is a dependency of autoviml which is in my environment.yaml file.

import imbalanced_ensemble is lengthy, rebase the whole project to imbens to ease the use of this package.

This will cause incompatibility with imbalanced-ensemble<=0.1.7.

Hello, I must say that IMBENS is an exceptional machine learning library. I am currently utilizing it to handle classification tasks for an extensive dataset. However, it appears that IMBENS lacks support for model migration to the GPU 'SelfPacedEnsembleClassifier' object has no attribute 'to'.

您好，执行./examples/visualizer/plot_performance_curve.py，出现以下错误，应该怎么解决呢？

The early termination in sklearn.ensemble.AdaBoostClassifier may be too strict under certain scenarios (only 1 base classifier is trained), which greatly hinders the performance of boosting-based ensemble imbalanced learning methods.

It should make more sense to add a parameter that allows the user to decide whether to enable strict early termination.

Update needed in pipeline.py

Zhining,
Sorry to bother you, but when I use EasyEnsembleClassifier in imbens.ensemble, I got the error "AttributeError: 'Pipeline' object has no attribute '_check_fit_params'", I can't figure out why this happened, could u please help me with it, the code is as follows:

from imbens.ensemble import EasyEnsembleClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

X, y = make_classification(n_classes=2, class_sep=2, n_features=100,
weights=[0.3,0.7], n_informative=2, n_redundant=1, flip_y=0.01,
n_clusters_per_class=1, n_samples=10000, random_state=10)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.5, random_state=42)

init_kwargs1 = {'estimator': DecisionTreeClassifier(),}

easyens = EasyEnsembleClassifier(**init_kwargs1).fit(X_train, y_train)
pred = easyens.predict(X_valid)

The errors are as follows:

Traceback (most recent call last):
File "f:\MYT\EEGP\smote_compare.py", line 176, in
easyens = EasyEnsembleClassifier(**init_kwargs1).fit(X_train, y_train)
File "D:\ProgramData\anaconda3\envs\EEG\lib\site-packages\imbens\utils_validation.py", line 604, in inner_f
return f(**kwargs)
File "D:\ProgramData\anaconda3\envs\EEG\lib\site-packages\imbens\ensemble_under_sampling\easy_ensemble.py", line 285, in fit
return self._fit(
File "D:\ProgramData\anaconda3\envs\EEG\lib\site-packages\imbens\utils_validation.py", line 604, in inner_f
return f(**kwargs)
File "D:\ProgramData\anaconda3\envs\EEG\lib\site-packages\imbens\ensemble_bagging.py", line 480, in _fit
all_results = Parallel(
File "D:\ProgramData\anaconda3\envs\EEG\lib\site-packages\sklearn\utils\parallel.py", line 67, in call
return super().call(iterable_with_config)
File "D:\ProgramData\anaconda3\envs\EEG\lib\site-packages\joblib\parallel.py", line 1863, in call
return output if self.return_generator else list(output)
File "D:\ProgramData\anaconda3\envs\EEG\lib\site-packages\joblib\parallel.py", line 1792, in _get_sequential_output
res = func(*args, **kwargs)
File "D:\ProgramData\anaconda3\envs\EEG\lib\site-packages\sklearn\utils\parallel.py", line 129, in call
return self.function(*args, **kwargs)
File "D:\ProgramData\anaconda3\envs\EEG\lib\site-packages\imbens\ensemble_bagging.py", line 163, in _parallel_build_estimators
estimator.fit((X[indices])[:, features], y[indices])
File "D:\ProgramData\anaconda3\envs\EEG\lib\site-packages\imbens\pipeline.py", line 273, in fit
fit_params_steps = self._check_fit_params(**fit_params)
AttributeError: 'Pipeline' object has no attribute '_check_fit_params'

packages version:
scikit-learn 1.4.0
imbalanced-ensemble 0.2.1

Hi, IMBENS is a great machine learning library, but I have a problem now, I want to deploy self_paced_ensemble algorithm as online services to production environment, how to generate PMML files successfully. I have tried Nyoka and sklearn2pmml so far, but all failed.

access all samplers directly from imbens.sampler module.

• rename imbens.sampler.over_sampling -> imbens.sampler._over_sampling
• rename imbens.sampler.under_sampling -> imbens.sampler._under_sampling

This will cause incompatibility with imbalanced-ensemble<=0.1.7.

Getting the error in the code provided in examples

Coming from this line: clf.fit(X_train, y_train) from this notebook https://github.com/ZhiningLiu1998/imbalanced-ensemble/blob/main/examples/classification/plot_classifier_comparison.py

from https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b6c772dc3b8292bcffb990ce27515a8/sklearn/utils/metaestimators.py#L144

    .. deprecated:: 1.3
        `if_delegate_has_method` is deprecated in version 1.1 and will be removed in
        version 1.3. Use `available_if` instead.

Need a fix to cooperate with sklearn > 1.3.0.

Resampling + Bagging clf (e.g., OverBagging) raises error when used with base estimators that do not support sample_weight (e.g., sklearn.KNeighborsClassifier).

根据你的在这儿https://imbalanced-ensemble.readthedocs.io/en/latest/auto_examples/classification/plot_digits.html 的代码，将分类器改成EasyEnsembleClassifier可以复现这个问题，会出现：

AttributeError: can't set attribute这个问题。

Hi，if i wanna realize my new undersampling idea in Bagging classifiers on the package,where should I add my undersampling strategy? thanks !

ZeroDivisionError may occur when some bins are empty when performing self-paced under-sampling.