Scikit Learn digits datasets

Question

i don't understand what is going on

this is my code

from sklearn import datasets
from sklearn import svm
import matplotlib.pyplot as plt

# Load digits dataset
digits = datasets.load_digits()

# Create support vector machine classifier
clf = svm.SVC(gamma=0.001, C=100.)
# fit the classifier
X, y = digits.data[:-1], digits.target[:-1]
clf.fit(X, y)
pred = clf.predict(digits.data[-1]) # error goes at this line
plt.imshow(digits.images[-1], cmap=plt.cm.gray_r, interpolation='nearest')
plt.show()

this code is showing the sklearn digits image and show its prediction

when i execute this code it show me this error

Traceback (most recent call last):
  File "detect.py", line 15, in <module>
    pred = clf.predict(digits.data[-1])
  File "/usr/local/lib/python2.7/dist-packages/sklearn/svm/base.py", line 548, in predict
    y = super(BaseSVC, self).predict(X)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/svm/base.py", line 308, in predict
    X = self._validate_for_predict(X)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/svm/base.py", line 439, in _validate_for_predict
    X = check_array(X, accept_sparse='csr', dtype=np.float64, order="C")
  File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 410, in check_array
    "if it contains a single sample.".format(array))
ValueError: Expected 2D array, got 1D array instead:
array=[  0.   0.  10.  14.   8.   1.   0.   0.   0.   2.  16.  14.   6.   1.   0.
   0.   0.   0.  15.  15.   8.  15.   0.   0.   0.   0.   5.  16.  16.  10.
   0.   0.   0.   0.  12.  15.  15.  12.   0.   0.   0.   4.  16.   6.   4.
  16.   6.   0.   0.   8.  16.  10.   8.  16.   8.   0.   0.   1.   8.  12.
  14.  12.   1.   0.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

Answer 1

The error messsage says what's wrong: You're feeding the classifier's predict method a single column and it's expecting as many columns as was used when fitting. If you change you're code to

pred = clf.predict(digits.data[:-1])

it works. Of course that makes no sense because now you're predicting the same data you fitted with (And you're omitting one column from the features). A more reasonable thing to do is to split the dataset to train and test datasets and the fit with train and predict the test. Like this:

from sklearn import datasets
from sklearn import svm
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

# Load digits dataset
digits = datasets.load_digits()

# Create support vector machine classifier
clf = svm.SVC(gamma=0.001, C=100.)
# split the data to train and test sets
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=2017)
# fit the classifier with train data
clf.fit(X_train, y_train)
pred = clf.predict(X_test)