Clash Royale CLAN TAG#URR8PPP

ValueError in calculating KNN accuracy

I have two sparse matrices Timesort_Y and Timesort_X

Timesort_Y

Timesort_X

type(Timesort_Y) and also type(Timesort_X) are
scipy.sparse.csr.csr_matrix

type(Timesort_Y)

type(Timesort_X)

scipy.sparse.csr.csr_matrix

# split the data set into train and test
X_1, X_test, y_1, y_test = cross_validation.train_test_split(Timesort_X,
Timesort_Y, test_size=0.3, random_state=0)

# split the train data set into cross validation train and cross validation
test

X_tr, X_cv, y_tr, y_cv = cross_validation.train_test_split(X_1, y_1,
test_size=0.3)

for i in range(1,10,2):
# instantiate learning model (k = 30)
knn = KNeighborsClassifier(n_neighbors=i)

# fitting the model on crossvalidation train
knn.fit(X_tr, y_tr)

# predict the response on the crossvalidation train
pred = knn.predict(X_cv)

# evaluate CV accuracy
acc = accuracy_score(y_cv , pred, normalize=True) * float(100)———>>>> ERROR
print(‘nCV accuracy for k = %d is %d%%’ % (i, acc))


print(pred) is as below.

[[<1427x1 sparse matrix of type '<class 'numpy.int64'>'
with 215 stored elements in Compressed Sparse Row format>
<1427x1 sparse matrix of type '<class 'numpy.int64'>'
with 1212 stored elements in Compressed Sparse Row format>]
[<1427x1 sparse matrix of type '<class 'numpy.int64'>'
with 215 stored elements in Compressed Sparse Row format>
<1427x1 sparse matrix of type '<class 'numpy.int64'>'
with 1212 stored elements in Compressed Sparse Row format>]
[<1427x1 sparse matrix of type '<class 'numpy.int64'>'
with 215 stored elements in Compressed Sparse Row format>
<1427x1 sparse matrix of type '<class 'numpy.int64'>'
with 1212 stored elements in Compressed Sparse Row format>]
...
[<1427x1 sparse matrix of type '<class 'numpy.int64'>'
with 215 stored elements in Compressed Sparse Row format>
<1427x1 sparse matrix of type '<class 'numpy.int64'>'
with 1212 stored elements in Compressed Sparse Row format>]
[<1427x1 sparse matrix of type '<class 'numpy.int64'>'
with 215 stored elements in Compressed Sparse Row format>


print(type(y_cv)) is a follows.

<class 'scipy.sparse.csr.csr_matrix'>


ERROR is at accuracy_score:

accuracy_score

ValueError Traceback (most recent call last)
<ipython-input-46-5e158b0a4f95> in <module>()
16
17 # evaluate CV accuracy
---> 18 acc = accuracy_score(y_cv , pred, normalize=True) * float(100)
19 print('nCV accuracy for k = %d is %d%%' % (i, acc))
20

~Anaconda3libsite-packagessklearnmetricsclassification.py in
accuracy_score(y_true, y_pred, normalize, sample_weight)
174
175 # Compute accuracy for each possible representation
--> 176 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
177 if y_type.startswith('multilabel'):
178 differing_labels = count_nonzero(y_true - y_pred, axis=1)

~Anaconda3libsite-packagessklearnmetricsclassification.py in
_check_targets(y_true, y_pred)
71 check_consistent_length(y_true, y_pred)
72 type_true = type_of_target(y_true)
---> 73 type_pred = type_of_target(y_pred)
74
75 y_type = set([type_true, type_pred])

~Anaconda3libsite-packagessklearnutilsmulticlass.py in
type_of_target(y)
248 raise ValueError("y cannot be class 'SparseSeries'.")
249
--> 250 if is_multilabel(y):
251 return 'multilabel-indicator'
252

~Anaconda3libsite-packagessklearnutilsmulticlass.py in
is_multilabel(y)
150 _is_integral_float(np.unique(y.data))))
151 else:
--> 152 labels = np.unique(y)
153
154 return len(labels) < 3 and (y.dtype.kind in 'biu' or # bool,
int, uint

~Anaconda3libsite-packagesnumpylibarraysetops.py in unique(ar,
return_index, return_inverse, return_counts, axis)
221 ar = np.asanyarray(ar)
222 if axis is None:
--> 223 return _unique1d(ar, return_index, return_inverse,
return_counts)
224 if not (-ar.ndim <= axis < ar.ndim):
225 raise ValueError('Invalid axis kwarg specified for unique')

~Anaconda3libsite-packagesnumpylibarraysetops.py in _unique1d(ar,
return_index, return_inverse, return_counts)
281 aux = ar[perm]
282 else:
--> 283 ar.sort()
284 aux = ar
285 flag = np.concatenate(([True], aux[1:] != aux[:-1]))

~Anaconda3libsite-packagesscipysparsebase.py in __bool__(self)
286 return self.nnz != 0
287 else:
--> 288 raise ValueError("The truth value of an array with more
than one "
289 "element is ambiguous. Use a.any() or
a.all().")
290 __nonzero__ = __bool__

ValueError: The truth value of an array with more than one element is
ambiguous. Use a.any() or a.all().


please correct me where i am doing wrong.

pred

y_cv

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