TypeError: 'tuple' object is not callable Python : <p>Currently coding for some machine learning in which I am using sklearn, numpy and scipy. I am able to parse my database and prepare data-sets. However when coming to prediction and outputting results, I am getting the following error:</p>

<p><em>Type Error: 'tuple' object is not callable</em></p>

<p>My code is below:</p>

<pre><code>from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import SGDClassifier
from sklearn import tree
from sklearn import gaussian_process
from sklearn import neural_network
from sklearn import preprocessing
from time import time
import numpy as np

t0 = time()

classifier = int(input(
"""
Enter number corresponding to classifier you would like to use:
1. Support Vector Machines
2. Gaussian Naive Bayes
3. Multinomial Naive Bayes
4. Stochastic Gradient Descent with Logistic Regression loss function
"""))

dataset = int(input(
"""
Enter number corresponding to data set you would like to use:
1. First half and second half
2. Alternating
3. Alternating with attack type
4. Alternating with attack type and target type
"""))

# Assign appropriate datasets
input_files = ['half', 'alternating', 'alternating-with-attacktype', 'alternating-all']
filename = input_files[dataset-1]

# Instantiate arrays for inputs and outputs
train_inputs = []
train_outputs = np.array([])

test_inputs = []
test_expected_outputs = np.array([])
test_actual_outputs = []
X = np.array([])
# Read training file
print ('Reading training file')
t = time()

for line in open('datasets/%s-train.txt' % filename):
    inputs = line.split(' ')
    outputs = inputs.pop()
    train_outputs = np.append(train_outputs, int(outputs))
print ('Done. Time taken: %f secs.\n' % (time()-t))

# for line in open('datasets/%s-train.txt' % filename):
#   inputs = line.split(' ')
#   output = inputs.pop()
#   train_outputs = np.append(train_outputs, int(output))
# print ('Done. Time taken: %f secs.\n' % (time()-t))

print ('Create classifier')
t = time()
clf = None

# No preprocessing for SVMs
# Otherwise, scale inputs (preprocessing to make more amenable for machine learning)
if classifier == 1: # Support vector machines
  clf = SVC()
elif classifier == 2: # Gaussian Naive Bayes
  train_inputs = preprocessing.scale(np.array(train_inputs))
  clf = GaussianNB()
elif classifier == 3: # Multinomial Naive Bayes
  clf = MultinomialNB()
elif classifier == 4: # Stochastic gradient descent with logistic regression
  train_inputs = preprocessing.scale(np.array(train_inputs))
  clf = SGDClassifier(loss='log')
print ('Done. Time taken: %f secs.\n' % (time()-t))

print ('Fit classifier')
t = time()
X.shape(1 -1)
clf.fit(train_inputs, train_outputs)
print ('Done. Time taken: %f secs.\n' % (time()-t))

# Read test file and scale inputs
print ('Reading test file')
t = time()
for line in open('datasets/%s-test.txt' % filename):
  inputs = line.split(' ')
  output = inputs.pop()
  test_expected_outputs = np.append(test_expected_outputs, int(output))
  test_inputs.append(map(float, inputs))

# Same here: no preprocessing for SVMs
# Otherwise, scale inputs (preprocessing to make more amenable for machine learning)
if classifier != 1:
  test_inputs = preprocessing.scale(np.array(test_inputs))
print ('Done. Time taken: %f secs.\n' % (time()-t))

print ('Predict for test file')
t = time()
test_actual_outputs = [clf.predict(i)[0] for i in test_inputs]
print ('Done. Time taken: %f secs.\n' % (time()-t))

print ('Compare outputs')
t = time()
right = sum(test_actual_outputs == test_expected_outputs)
wrong = len(test_actual_outputs) - right
print ('Done. Time taken: %f secs.\n' % (time()-t))

print ('Number right: %d\nNumber wrong: %d' % (right, wrong))
print ('Prediction rate: %.2f%%' % (100.0 * right/len(test_actual_outputs)))
print ('Total time taken: %f secs.\n' % (time()-t0))
</code></pre>

<p>I know that I need to add array.reshape(-1 1) or array.reshape(1 -1) but not sure what this will do.</p>

<p>Any advice on how to solve this would be welcome. </p>
