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.
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.
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
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())
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