如何解决线性回归中的"Exception: Data must be 1-dimensional"？

Question

我需要在不使用scikit的情况下对波士顿住房数据集运行线性回归。

这就是我到目前为止想出的

import pandas as pd
import numpy as np
import matplotlib.pyplot as mlt
from sklearn.cross_validation import train_test_split 
data = pd.read_csv("housing.csv", delimiter=' ',
skipinitialspace=True,
names=['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE',
'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
)
df_x = data.drop('MEDV', axis = 1)
df_y = data['MEDV']
x_train, x_test, y_train, y_test = train_test_split(df_x, df_y,
test_size=0.2,
random_state=4
)
def hypothesis(x, theta):
return np.dot(x, theta.T)
def costfn(predictions, y, x):
a = 1 / (2 * len(x)) * np.sum((prediction - y) ** 2)
return a
def gradient(theta, alpha, predictions, x, y):
theta = np.subtract(theta, (alpha / len(x)) * np.dot(np.subtract(predictions, y).T, x))
return theta
alpha = 0.001
iters = 1000
theta = np.zeros([1, 13])
predictions = hypothesis(x_train, theta)
for i in range(iters):
predictions = hypothesis(x_train, theta)
theta = gradient(theta, alpha, predictions, x_train, y_train)
predictions = hypothesis(x_test, theta)
print(predictions)

我已经接受并输入并分离了测试和训练案例，所有这些都工作正常。但是我收到此错误-

Exception                                 Traceback (most recent call last)
<ipython-input-33-36492e2820ce> in <module>
6 for i in range(iters):
7     predictions = hypothesis(x_train, theta)
----> 8     theta = gradient(theta, alpha, predictions, x_train, y_train)
9 
10 predictions = hypothesis(x_test, theta)
<ipython-input-32-15d0b5b7bf16> in gradient(theta, alpha, predictions, x, y)
9 
10 
---> 11     theta = np.subtract(theta, (alpha / len(x)) * np.dot(np.subtract(predictions, y).T, x))
12     return theta
/usr/lib/python3/dist-packages/pandas/core/series.py in __array_wrap__(self, result, context)
502         """
503         return self._constructor(result, index=self.index,
--> 504                                  copy=False).__finalize__(self)
505 
506     def __array_prepare__(self, result, context=None):
/usr/lib/python3/dist-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
262             else:
263                 data = _sanitize_array(data, index, dtype, copy,
--> 264                                        raise_cast_failure=True)
265 
266                 data = SingleBlockManager(data, index, fastpath=True)
/usr/lib/python3/dist-packages/pandas/core/series.py in _sanitize_array(data, index, dtype, copy, raise_cast_failure)
3273     elif subarr.ndim > 1:
3274         if isinstance(data, np.ndarray):
-> 3275             raise Exception('Data must be 1-dimensional')
3276         else:
3277             subarr = _asarray_tuplesafe(data, dtype=dtype)
Exception: Data must be 1-dimensional

请帮忙。另外，如果我的逻辑错误，请告诉我，因为我是初学者。

Answer 1

pandas非常适合数据管理，但我倾向于坚持使用NumPy对象进行数学步骤。pandas试图在这里做一些聪明的事情，我不知道是什么，但是如果你df_x.values和df_y.values传递给train_test_split()，你的代码运行：

x_train, x_test, y_train, y_test = train_test_split(df_x.values,
df_y.values,
test_size=0.2,
random_state=4
)