CODEWITHSUNDEEP
pythonmachine-learningscikit-learn
tonicebrian
tonicebrian

Reputation: 4795

How to encode a categorical variable in sklearn?

I'm trying to use the car evaluation dataset from the UCI repository and I wonder whether there is a convenient way to binarize categorical variables in sklearn. One approach would be to use the DictVectorizer of LabelBinarizer but here I'm getting k different features whereas you should have just k-1 in order to avoid collinearization. I guess I could write my own function and drop one column but this bookkeeping is tedious, is there an easy way to perform such transformations and get as a result a sparse matrix?

Upvotes: 12

Views: 25054

Answers (3)

BBSysDyn
BBSysDyn

Reputation: 4601

The basic method is

import numpy as np
import pandas as pd, os
from sklearn.feature_extraction import DictVectorizer

def one_hot_dataframe(data, cols, replace=False):
    vec = DictVectorizer()
    mkdict = lambda row: dict((col, row[col]) for col in cols)
    vecData = pd.DataFrame(vec.fit_transform(data[cols].apply(mkdict, axis=1)).toarray())
    vecData.columns = vec.get_feature_names()
    vecData.index = data.index
    if replace is True:
        data = data.drop(cols, axis=1)
        data = data.join(vecData)
    return (data, vecData, vec)

data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],
        'year': [2000, 2001, 2002, 2001, 2002],
        'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}

df = pd.DataFrame(data)

df2, _, _ = one_hot_dataframe(df, ['state'], replace=True)
print df2

Here is how to do in sparse format

import numpy as np
import pandas as pd, os
import scipy.sparse as sps
import itertools

def one_hot_column(df, cols, vocabs):
    mats = []; df2 = df.drop(cols,axis=1)
    mats.append(sps.lil_matrix(np.array(df2)))
    for i,col in enumerate(cols):
        mat = sps.lil_matrix((len(df), len(vocabs[i])))
        for j,val in enumerate(np.array(df[col])):
            mat[j,vocabs[i][val]] = 1.
        mats.append(mat)

    res = sps.hstack(mats)   
    return res

data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],
        'year': ['2000', '2001', '2002', '2001', '2002'],
        'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}

df = pd.DataFrame(data)
print df

vocabs = []
vals = ['Ohio','Nevada']
vocabs.append(dict(itertools.izip(vals,range(len(vals)))))
vals = ['2000','2001','2002']
vocabs.append(dict(itertools.izip(vals,range(len(vals)))))

print vocabs

print one_hot_column(df, ['state','year'], vocabs).todense()

Upvotes: 16

rezakhorshidi
rezakhorshidi

Reputation: 539

if your data is a pandas DataFrame, then you can simply call get_dummies. Assume that your data frame is df, and you want to have one binary variable per level of variable 'key'. You can simply call:

pd.get_dummies(df['key'])

and then delete one of the dummy variables, to avoid the multi-colinearity problem. I hope this helps ...

Upvotes: 31

Peter Prettenhofer
Peter Prettenhofer

Reputation: 1971

DictVectorizer is the recommended way to generate a one-hot encoding of categorical variables; you can use the sparse argument to create a sparse CSR matrix instead of a dense numpy array. I usually don't care about multicollinearity and I haven't noticed a problem with the approaches that I tend to use (i.e. LinearSVC, SGDClassifier, Tree-based methods).

It shouldn't be a problem to patch the DictVectorizer to drop one column per categorical feature - you simple need to remove one term from DictVectorizer.vocabulary at the end of the fit method. (Pull requests are always welcome!)

Upvotes: 15

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