labels)# 识别错误标签的示例cl.fit(train_data,labels,label_issues=label_issues)preds=cl.predict(test_data)# 从通过自动清理的数据训练过后的模型进行预测fromcleanlab.filterimportfind_label_issuesranked_label_issues=find_label_issues(labels,pred_probs,return_indices_ranked...
When I use the from cleanlab.filter import find_label_issues I get the following ERROR TypeError: ufunc 'true_divide' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' Activity cgnorthcutt commented on...
# label issues are ordered by likelihood of being an error. First index is most likely error. from cleanlab.filter import find_label_issues ordered_label_issues = find_label_issues( # One line of code! labels=numpy_array_of_noisy_labels, pred_probs=numpy_array_of_predicted_probabilities, ...
Your addition so far has made it possible to run CleanLearning.find_label_issues() with larger dimensional data, however I believe CleanLearning.fit() will fail due to this line over here since the classifier is called again. Similarly, CleanLearning.predict() and related functions would likely...
from PIL import ImageStat, ImageFilter import numpy as np def check_brightness(img, **kwargs): def check_brightness(img): """ Scores the overall brightness for a given image to find ones that are too bright and too dark generates 'Brightness sorted z-scores' in images.misc_info Paramete...