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

Question

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.

What's the shape of trainingImages.image.values? X values passed to the fit function should be of shape (n_samples, n_features). If you have (n_samples, width, height), try X.reshape(-1, width*height). — dukebody
– dukebody, Commented Mar 20, 2016 at 17:49

Community · Accepted Answer · 2020-06-20 09:12:55Z

8

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.

edited Jun 20, 2020 at 9:12

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answered May 9, 2018 at 21:50

Minions

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Community · Accepted Answer · 2017-05-23 12:02:20Z

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

edited May 23, 2017 at 12:02

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answered Mar 20, 2016 at 15:23

armatita

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2 Comments

Perihan Gad Over a year ago

Thank you for your reply. I made sure that all its elements have the same size and I am still getting the same error.

armatita Over a year ago

Could you post a print of trainingImages.image.values and trainingTargets.values.tolist()? (as well as shape)

Vivek Kalyanarangan · Accepted Answer · 2016-04-04 09:29:07Z

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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