I need to get matrix of TF-IDF features from the text stored in columns of a huge dataframe, loaded from a CSV file (which cannot fit in memory). I am trying to iterate over dataframe using chunks but it is returning generator objects which is not an expected variable type for the method TfidfVectorizer. I guess I am doing something wrong while writing a generator method ChunkIterator
shown below.
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
#Will work only for small Dataset
csvfilename = 'data_elements.csv'
df = pd.read_csv(csvfilename)
vectorizer = TfidfVectorizer()
corpus = df['text_column'].values
vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
#Trying to use a generator to parse over a huge dataframe
def ChunkIterator(filename):
for chunk in pd.read_csv(csvfilename, chunksize=1):
yield chunk['text_column'].values
corpus = ChunkIterator(csvfilename)
vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
Can anybody please advise how to modify the ChunkIterator
method above, or any other approach using dataframe. I would like to avoid creating separate text files for each row in the dataframe. Following is some dummy csv file data for recreating the scenario.
id,text_column,tags
001, This is the first document .,['sports','entertainment']
002, This document is the second document .,"['politics', 'asia']"
003, And this is the third one .,['europe','nato']
004, Is this the first document ?,"['sports', 'soccer']"
The method accepts generators just fine. But it requires a iterable of raw documents, i.e. strings. Your generator is an iterable of numpy.ndarray
objects. So try something like:
def ChunkIterator(filename):
for chunk in pd.read_csv(csvfilename, chunksize=1):
for document in chunk['text_column'].values:
yield document
Note, I don't really understand why you are using pandas here. Just use the regular csv
module, something like:
import csv
def doc_generator(filepath, textcol=0, skipheader=True):
with open(filepath) as f:
reader = csv.reader(f)
if skipheader:
next(reader, None)
for row in reader:
yield row[textcol]
So, in your case, pass 1
to textcol, for example:
In [1]: from sklearn.feature_extraction.text import TfidfVectorizer
In [2]: import csv
...: def doc_generator(filepath, textcol=0, skipheader=True):
...: with open(filepath) as f:
...: reader = csv.reader(f)
...: if skipheader:
...: next(reader, None)
...: for row in reader:
...: yield row[textcol]
...:
In [3]: vectorizer = TfidfVectorizer()
In [4]: result = vectorizer.fit_transform(doc_generator('testing.csv', textcol=1))
In [5]: result
Out[5]:
<4x9 sparse matrix of type '<class 'numpy.float64'>'
with 21 stored elements in Compressed Sparse Row format>
In [6]: result.todense()
Out[6]:
matrix([[ 0. , 0.46979139, 0.58028582, 0.38408524, 0. ,
0. , 0.38408524, 0. , 0.38408524],
[ 0. , 0.6876236 , 0. , 0.28108867, 0. ,
0.53864762, 0.28108867, 0. , 0.28108867],
[ 0.51184851, 0. , 0. , 0.26710379, 0.51184851,
0. , 0.26710379, 0.51184851, 0.26710379],
[ 0. , 0.46979139, 0.58028582, 0.38408524, 0. ,
0. , 0.38408524, 0. , 0.38408524]])
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