How to solve “Exception: Data must be 1-dimensional” in linear regression?

Question

I need to run linear regression on boston housing dataset without using scikit.

This is what I've come up with till now

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)

I've taken and input and separated test and train cases and all that is working fine. But I'm getting this error -

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

Please help. Also, if my logic is wrong please tell me as I'm a beginner.

Answer 1

pandas is great for data management, but I tend to stick to NumPy objects for the mathematical steps. pandas is trying to do something clever here, I don't know what, but if you pass df_x.values and df_y.values to train_test_split() , your code runs:

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