使用分类变量使用sklearn进行线性回归

Question

I am trying to run a usual linear regression in Python using sk-learn, but I have some categorical data that I don't know exactly how to handle, especially because I imported the data using pandas read.csv() and I have learned from previous experiences and reading that Pandas and sk-learn don't get along quite well (yet).

My data looks like this:

Salary  AtBat   Hits    League  EastDivision
475     315     81      1       0
480     479     130     0       0
500     496     141     1       1

I wanna predict Salary using AtBat, Hits, League and EastDivision, where League and EastDivision are categorical.

If I import the data via numpy's loadtext() I get a numpy array which in theory I could use with sklearn, but when I use DictVectorizer I get an error. My code is:

import numpy as np
from sklearn.feature_extraction import DictVectorizer as DV

nphitters=np.loadtxt('Hitters.csv',delimiter=',', skiprows=1)
vec = DV( sparse = False )
catL=vec.fit_transform(nphitters[:,3:4])

And I get the error when I run the last line catL=vec.fit_transform(nphitters[:,3:4]) , the error is

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python2.7/dist-packages/sklearn/feature_extraction/dict_vectorizer.py", line 142, in fit_transform
    self.fit(X)
  File "/usr/lib/python2.7/dist-packages/sklearn/feature_extraction/dict_vectorizer.py", line 107, in fit
    for f, v in six.iteritems(x):
  File "/usr/lib/python2.7/dist-packages/sklearn/externals/six.py", line 268, in iteritems
    return iter(getattr(d, _iteritems)())
AttributeError: 'numpy.ndarray' object has no attribute 'iteritems'

I don't know how to fix it, and another thing is, once I get the categorical data working, how do I run the regression? Just as if the categorical variable were another numeric variable?

I have found several questions similar to mine, but none of them have really worked for me.

Answer 1

It looks like .fit_transform() expects a dict but .loadtxt() create a numpy array.

You can use .to_dict() after reading your data with pandas .

Answer 2

Basically what happens is that you are passing a vector of 1 and 0 to a function that will take keys and values (like a dictionary) and create a table for you

D = [{'foo': 1, 'bar': 2}, {'foo': 3, 'baz': 1}]

will become

array([[ 2.,  0.,  1.],
       [ 0.,  1.,  3.]])

or

|bar|baz|foo  |<br>
|---|---|-----|<br>
| 2 | 0 | 1   |<br>
| 0 | 0 | 3   |<br>

read: http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html

in your case, the data is ready for a linear regression as the features league and east division are dummies already.

Answer 3

scikit-learn has two new functions which do this for you

sklearn.preprocessing.LabelBinarizer
sklearn.preprocessing.LabelEncoder

If your want to process multiple values in a single row,

sklearn.preprocessing.MultiLabelBinarizer

eg:

array = [(dog, cat),(dog),(dog,fish)]
mb = MultiLabelBinarizer()
mb.fit_transform(array)

>> array([1, 0, 1, 0, 0, 0],
       [0, 1, 0, 0, 1, 1],
       [0, 0, 1, 1, 0, 0]])