I know feature hashing (hashing-trick) is used to reduce the dimensionality and handle sparsity of bit vectors but I don't understand how it really works. Can anyone explain this to me.Is there any python library available to do feature hashing?
Thank you.
It works by applying a hash function to the features and using their hash values as indices directly, rather than looking the indices up in an associative array. This trick is often attributed to Weinberger et al. (2009), but there exists a much earlier description of this method published by John Moody in 1989.
Implements feature hashing, aka the hashing trick. This class turns sequences of symbolic feature names (strings) into scipy. sparse matrices, using a hash function to compute the matrix column corresponding to a name. The hash function employed is the signed 32-bit version of Murmurhash3.
The hashing trick is a machine learning technique used to encode categorical features into a numerical vector representation of pre-defined fixed length. It works by using the categorical hash values as vector indices, and updating the vector values at those indices.
Hashing is the process of transforming any given key or a string of characters into another value. This is usually represented by a shorter, fixed-length value or key that represents and makes it easier to find or employ the original string. The most popular use for hashing is the implementation of hash tables.
On Pandas, you could use something like this:
import pandas as pd
import numpy as np
data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],
'year': [2000, 2001, 2002, 2001, 2002],
'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}
data = pd.DataFrame(data)
def hash_col(df, col, N):
cols = [col + "_" + str(i) for i in range(N)]
def xform(x): tmp = [0 for i in range(N)]; tmp[hash(x) % N] = 1; return pd.Series(tmp,index=cols)
df[cols] = df[col].apply(xform)
return df.drop(col,axis=1)
print hash_col(data, 'state',4)
The output would be
pop year state_0 state_1 state_2 state_3
0 1.5 2000 0 1 0 0
1 1.7 2001 0 1 0 0
2 3.6 2002 0 1 0 0
3 2.4 2001 0 0 0 1
4 2.9 2002 0 0 0 1
Also on Series level, you could
import numpy as np, os import sys, pandas as pd
def hash_col(df, col, N):
df = df.replace('',np.nan)
cols = [col + "_" + str(i) for i in range(N)]
tmp = [0 for i in range(N)]
tmp[hash(df.ix[col]) % N] = 1
res = df.append(pd.Series(tmp,index=cols))
return res.drop(col)
a = pd.Series(['new york',30,''],index=['city','age','test'])
b = pd.Series(['boston',30,''],index=['city','age','test'])
print hash_col(a,'city',10)
print hash_col(b,'city',10)
This will work per single Series, column name will be assumed to be a Pandas index. It also replaces blank strings with nan, and floats everything.
age 30
test NaN
city_0 0
city_1 0
city_2 0
city_3 0
city_4 0
city_5 0
city_6 0
city_7 1
city_8 0
city_9 0
dtype: object
age 30
test NaN
city_0 0
city_1 0
city_2 0
city_3 0
city_4 0
city_5 1
city_6 0
city_7 0
city_8 0
city_9 0
dtype: object
If, however, there is a vocabulary, and you simply want to one-hot-encode, you could use
import numpy as np
import pandas as pd, os
import scipy.sparse as sps
def hash_col(df, col, vocab):
cols = [col + "=" + str(v) for v in vocab]
def xform(x): tmp = [0 for i in range(len(vocab))]; tmp[vocab.index(x)] = 1; return pd.Series(tmp,index=cols)
df[cols] = df[col].apply(xform)
return df.drop(col,axis=1)
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 = hash_col(df, 'state', ['Ohio','Nevada'])
print sps.csr_matrix(df2)
which will give
pop year state=Ohio state=Nevada
0 1.5 2000 1 0
1 1.7 2001 1 0
2 3.6 2002 1 0
3 2.4 2001 0 1
4 2.9 2002 0 1
I also added sparsification of the final dataframe as well. In incremental setting where we might not have encountered all values beforehand (but we somehow obtained the list of all possible values somehow), the approach above can be used. Incremental ML methods would need the same number of features at each increment, hence one-hot encoding must produce the same number of rows at each batch.
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