sklearn SVM fit() “ValueError: setting an array element with a sequence”

问题:

I am using sklearn to apply svm on my own set of images. The images are put in a data frame. I pass to the fit function a numpy array that has 2D lists, these 2D lists represents images and the second input I pass to the function is the list of targets (The targets are numbers). I always get this error "ValueError: setting an array element with a sequence".

trainingImages = images.ix[images.partID <=9] trainingTargets = images.clustNo.ix[images.partID<=9] trainingImages.reset_index(inplace=True,drop=True) trainingTargets.reset_index(inplace=True,drop=True)  classifier = svm.SVC(gamma=0.001) classifier.fit(trainingImages.image.values,trainingTargets.values.tolist()) 

The Error:

--------------------------------------------------------------------------- ValueError                                Traceback (most recent call last) <ipython-input-43-5336fbeca868> in <module>()       8 classifier = svm.SVC(gamma=0.001)       9  ---> 10 classifier.fit(trainingImages.image.values,trainingTargets.values.tolist())      11       12 #classifier.fit(t, list(range(0,2899)))  /Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/sklearn/svm/base.py in fit(self, X, y, sample_weight)     148         self._sparse = sparse and not callable(self.kernel)     149  --> 150         X = check_array(X, accept_sparse='csr', dtype=np.float64, order='C')     151         y = self._validate_targets(y)     152   /Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)     371                                       force_all_finite)     372     else: --> 373         array = np.array(array, dtype=dtype, order=order, copy=copy)     374      375         if ensure_2d:  ValueError: setting an array element with a sequence. 

回答1:

I had the same exact error, it's one of two possibilities:

1- Data and labels are not in the same length.

2- For a specific feature vector, the number of elements are not equal.

回答2:

It's probably because "trainingImages.image.values" does not have the same number of elements in all it's arrays. Check a similar question here in stackoverflow:

ValueError: setting an array element with a sequence. while using SVM in scikit-learn

回答3:

If you are sure the dimensions are correct, below's a piece of code/workflow that might help

import skimage.io as skio import matplotlib.pyplot as plt import numpy as np from sklearn import svm from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score %matplotlib inline  # Load the data trainingImages = skio.imread_collection('train/images/*.jpg',conserve_memory=True)  # cast to numpy arrays trainingImages = np.asarray(trainingImages)  # reshape img array to vector def reshape_image(img):     return np.reshape(img,len(img)*len(img[0]))  img_reshape = np.zeros((len(trainingImages),len(trainingImages[0])*len(trainingImages[0][0])))  for i in range(0,len(trainingImages)):     img_reshape[i] = reshape_image(trainingImages[i])  # SVM clf = svm.SVC(gamma=0.01,C=10,kernel='poly') clf.fit(img_reshape,trainingTargets.values.tolist()) 

文章来源: sklearn SVM fit() “ValueError: setting an array element with a sequence”