Python module to perform under sampling and over sampling with various techniques.
linked to the business side
Yep, this makes total sense. On the flipside, instance-level "complicatedness"/"hard-to-learn-ness" is usually appearing in oversampling techniques, which might serve as the basis of local costs. I found this so obvious that I just wondered if I am searching for wrong terms to find anything like this.
Also, I could imagine some local density estimation based costs. If the density is smaller, then the cost of misclassification should be higher.
In order to balance for differences in the densities of the classes, not only balancing the difference in the number of samples.
As global, class cardinality proportional weights do.
For reference I am using the SMOTE method for oversampling:
smoter = SMOTE(random_state=42, n_jobs=-1, sampling_strategy = 'not majority')
X_train_smote, y_train_smote = smoter.fit_resample(X_train, y_train)To be more specific, I am wondering whether it is possible to know the index for X_train in the X_train_smote dataset.
Has anyone used imblearn SMOTE together with Pipeline and some explainability framework? e.g. shap, lime, eli5
My problem is that I try to explain my predictions, but for that I need to first transform the data to the state of "just before fitting the last estimator" (because the transformers in the Pipeline like custom vectorizers create new columns) before running the explainability package together with the last step - the estimator. For that, I can in principle use
My problem is that I try to explain my predictions, but for that I need to first transform the data to the state of "just before fitting the last estimator" (because the transformers in the Pipeline like custom vectorizers create new columns) before running the explainability package together with the last step - the estimator. For that, I can in principle use
pipeline[:-1].transform(np.array(X_train)). However, I then get the error "AttributeError: 'SMOTE' object has no attribute 'transform'". I don't know how to proceed.
import numpy as np
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
x = np.array([['aaa'] * 100, ['bbb'] * 100]).T
y = np.array([0] * 10 + [1] * 90)
ros = RandomOverSampler()
x_res, y_res = ros.fit_sample(x, y)
smote = SMOTE()
x_res, y_res = smote.fit_sample(x, y)
while SMOTE interpolate and therefore apply numerical operation
thus you need to encode your data in some way
Hello team. I have a question about Borderline SMOTE:
The variant 2 is supposed to interpolate between the minority in danger and other neighbors from the minority, and the minority in danger and some neighbors from the majority.
In line https://github.com/scikit-learn-contrib/imbalanced-learn/blob/4162d2d/imblearn/over_sampling/_smote.py#L352
we train a KNN only on the minority class and then derive the neighbors nns from it, which we use for the interpolation.
Then we use that nns to obtain the neighbors from the majority class in the second part (https://github.com/scikit-learn-contrib/imbalanced-learn/blob/4162d2d/imblearn/over_sampling/_smote.py#L397) of the borderline-2 code. But would not nns contain only neighbours from the minority? as it is derived from a knn trained only in the minority class?
Random question, do you have any experience on how widely used MetaCost is? https://homes.cs.washington.edu/~pedrod/papers/kdd99.pdf, I mean to work with imbalanced data? Supposedly wraps any classifier. Do you know a good Python implementation or are you planing to make it part of imabalanced-learn?
Hello! Maybe a usage question... I am using SMOTE and trying to oversample the minority class to a specific ratio. So I am passing a float value between (0,1] to the sampling_strategy argument. No matter the value I set, even 1, I always get "ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio."
The distribution of my target variable is the following so anything above 0.1 roughly I was expecting to be fine. Am I missing something?
The distribution of my target variable is the following so anything above 0.1 roughly I was expecting to be fine. Am I missing something?
In any case, without an mre, I suppose that your target ratio is to low in order to generate new examples. In the oversampling case the sampling_strategy is proportionally to the majority class. Having said that, in the case of 90:10 you will need at least
0.13 in order to generate (and add) a single minority instance. So the new ratio will be 90:11.
Hey guys, can anyone here review my issue (scikit-learn-contrib/imbalanced-learn#781), it's about adding a new feature to Condensed Nearest Neighbour and I want to start working on it but first I want to hear your opinion. thank you
so we can stop after a certain number of iteration based on some criteria
which could be considered as an early stopping
CNN is only doing a single iteration
We have an inner iteration that go through all samples to decide whether to exclude or not some samples
but stopping there would be a bad idea
You might treat only a specific area of your data distribution then which would not be beneficial I think
at least this mis my intuition
Probably, tuning the hyperparameters would be better in this case.
but by experience, the recipes that work to handle balancing issue is
to train a
BalancedBaggingClassifier (that use a RandomUnderSampler with a strong learner as a HistGradientBoosting
and it usually beats any fancy resampling
and it is just much faster :)
whatever algorithm based on KNN does not scale properly
But this is only my 2 cents on the issues. I am happy to see application where this is indeed not the case :)
but this is slower
In classification the loss will still be affected by the balancing issue
that's why the making an ensemble of GBDT that see balanced bootstrap samples could be better