Scaled Co-occurrence matrix with window size calculation in python

Question

Say, I've a dataset in CSV format, which contains sentences/paragraphs in rows. Suppose, it looks like this:

df = ['A B X B', 'X B B']

Now, I can generate co-occurrence matrix that looks like this

  A B X
A 0 2 1
B 2 0 4
X 1 4 0

Here, (A,B,X) are words. It says B appeared where X is present = 4 times Code that I used for it

def co_occurrence(sentences, window_size):
    d = defaultdict(int)
    vocab = set()
    for text in sentences:
        # preprocessing (use tokenizer instead)
        text = text.lower().split()
        # iterate over sentences
        for i in range(len(text)):
            token = text[i]
            vocab.add(token)  # add to vocab
            next_token = text[i+1 : i+1+window_size]
            for t in next_token:
                key = tuple( sorted([t, token]) )
                d[key] += 1

    # formulate the dictionary into dataframe
    vocab = sorted(vocab) # sort vocab
    df = pd.DataFrame(data=np.zeros((len(vocab), len(vocab)), dtype=np.int16),
                      index=vocab,
                      columns=vocab)
    for key, value in d.items():
        df.at[key[0], key[1]] = value
        df.at[key[1], key[0]] = value
    return df

The beauty of this code segment is that it allows me to choose windows size. That means if a particular word doesn't appear with a fixed amount of range from total sentence size then it gets ignored. But I would like to scale it.

So this means if a word is far from the target word "to" then it will be assigned lesser values. Unfortunately, I couldn't find a suitable solution for it. Is it possible with a package such as scikit-learn? Or is there any other way to do it except raw coding?

Answer 1

Here’s an implementation that can optionally scale the accumulated co-occurence values based on the distance between word tokens in the input sentences:

In [11]: sentences = ['from swerve of shore to bend of bay , brings'.split()]                                    

In [12]: index, matrix = co_occurence_matrix(sentences, window=3, scale=True)                                    

In [13]: cell = index['bend'], index['of']                                                                       

In [14]: matrix[cell]                                                                                            
Out[14]: 1.3333333333333333

In [15]: index, matrix = co_occurence_matrix(sentences, window=3, scale=False)                                   

In [16]: matrix[cell]                                                                                            
Out[16]: 2.0

In [17]: {w: matrix[index['to']][i] for w, i in index.items()}                                                   
Out[17]: 
{',': 0.0,
 'bend': 1.0,
 'of': 1.0,
 'bay': 0.3333333333333333,
 'brings': 0.0,
 'to': 0.0,
 'from': 0.0,
 'shore': 1.0,
 'swerve': 0.3333333333333333}