我正在尝试使用Scikit-learn为我的4类分类集打印多类ROC曲线。我在Google Colab中使用它。我一直在关注this tutorial from Scikit。当我尝试执行此操作时-
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
我知道我需要对输出/测试数据进行二值化。所以，我做到了-
from google.colab import drive
drive.mount('/content/drive')
data = "/content/drive/My Drive/AMD_new"
import torch
import helper
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms, models
import torchvision.models as models
from torchvision import datasets ,transforms
#Changning the transform of the data-
transform_train = transforms.Compose([transforms.RandomHorizontalFlip(),
transforms.RandomResizedCrop(224),
# transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([transforms.RandomHorizontalFlip(),
transforms.RandomResizedCrop(224),
# transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# choose the training and test datasets
train_data = datasets.ImageFolder(data+"/train", transform=transform_train)
test_data = datasets.ImageFolder(data+"/val", transform = transform_test)
dataloader_train = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True, num_workers=2)
dataloader_test = torch.utils.data.DataLoader(test_data, batch_size=32, num_workers=2)
# Binarize the output
dataloader_test = label_binarize(dataloader_test, classes=[0, 1, 2, 3])
nb_classes = dataloader_test.shape[1]
但是有一个错误，我无法解决。
E: Package 'python-software-properties' has no installation candidate
··········
fuse: mountpoint is not empty
fuse: if you are sure this is safe, use the 'nonempty' mount option
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-f3ce2af1dde4> in <module>()
56 dataloader_test = torch.utils.data.DataLoader(test_data, batch_size=32, num_workers=2)
57 # Binarize the output
---> 58 dataloader_test = label_binarize(dataloader_test, classes=[0, 1, 2, 3])
59 nb_classes = dataloader_test.shape[1]
/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/label.py in label_binarize(y, classes, neg_label, pos_label, sparse_output)
579 # XXX Workaround that will be removed when list of list format is
580 # dropped
--> 581 y = check_array(y, accept_sparse='csr', ensure_2d=False, dtype=None)
582 else:
583 if _num_samples(y) == 0:
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
575 shape_repr = _shape_repr(array.shape)
576 if ensure_min_samples > 0:
--> 577 n_samples = _num_samples(array)
578 if n_samples < ensure_min_samples:
579 raise ValueError("Found array with %d sample(s) (shape=%s) while a"
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in _num_samples(x)
140 if len(x.shape) == 0:
141 raise TypeError("Singleton array %r cannot be considered"
--> 142 " a valid collection." % x)
143 # Check that shape is returning an integer or default to len
144 # Dask dataframes may not return numeric shape[0] value
TypeError: Singleton array array(<torch.utils.data.dataloader.DataLoader object at 0x7f47667cda20>,
dtype=object) cannot be considered a valid collection.
请帮助我克服这个问题。我已关注此帖子-Singleton array array(<function train at 0x7f3a311320d0>, dtype=object) cannot be considered a valid collection 谢谢。