Python 类型错误：'；元组'；对象不可调用

目前正在为一些机器学习编写代码，我正在使用sklearn、numpy和scipy。我能够解析我的数据库并准备数据集。然而，在预测和输出结果时，我得到以下错误：

类型错误：“tuple”对象不可调用

我的代码如下：

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))

我知道我需要添加array.reforme（-1）或array.reforme（1-1），但不确定这会做什么

---

欢迎就如何解决这一问题提供任何建议

此错误来自以下行：

X.shape(1 -1)

改用

X.shape

这是这期杂志的复制品：

X = pd.Series()
X.shape(1 -1)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-105-1bb3332dc7d5> in <module>()
      1 X = pd.Series()
----> 2 X.shape(1 -1)

TypeError: 'tuple' object is not callable

X=pd.Series（）
X.形状（1-1）
---------------------------------------------------------------------------
TypeError回溯（最近一次调用上次）
在（）
1 X=pd.系列（）
---->2 X形状（1-1）
TypeError:“tuple”对象不可调用

请回答：a。另外，您必须在问题中提供完整的回溯。完整的回溯是什么，这样我们就可以在不深入代码的情况下看到引起错误的原因？1。这是一个很长的程序-尝试创建一个最小的、最好是自包含的、重现错误2的示例。发布完整的错误消息（不清楚程序在哪一行失败）3。尝试使用调试器介入程序-它可能会帮助您了解问题所在