String Matching Using TF-IDF, NGrams and Cosine Similarity in Python

I am working on my first major data science project. I am attempting to match names between a large list of data from one source, to a cleansed dictionary in another. I am using this string matching blog as a guide.

I am attempting to use two different data sets. Unfortunately, I can't seem to get good results and I think I am not applying this appropriately.

Code:

import pandas as pd, numpy as np, re, sparse_dot_topn.sparse_dot_topn as ct
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy.sparse import csr_matrix


df_dirty = {"name":["gogle","bing","amazn","facebook","fcbook","abbasasdfzz","zsdfzl","gogle","bing","amazn","facebook","fcbook","abbasasdfzz","zsdfzl"]}

df_clean = {"name":["google","bing","amazon","facebook"]}

print (df_dirty["name"])
print (df_clean["name"])


def ngrams(string, n=3):
    string = (re.sub(r'[,-./]|\sBD',r'', string)).upper()
    ngrams = zip(*[string[i:] for i in range(n)])
    return [''.join(ngram) for ngram in ngrams]


def awesome_cossim_top(A, B, ntop, lower_bound=0):
    # force A and B as a CSR matrix.
    # If they have already been CSR, there is no overhead
    A = A.tocsr()
    B = B.tocsr()
    M, _ = A.shape
    _, N = B.shape

    idx_dtype = np.int32

    nnz_max = M * ntop

    indptr = np.zeros(M + 1, dtype=idx_dtype)
    indices = np.zeros(nnz_max, dtype=idx_dtype)
    data = np.zeros(nnz_max, dtype=A.dtype)

    ct.sparse_dot_topn(
        M, N, np.asarray(A.indptr, dtype=idx_dtype),
        np.asarray(A.indices, dtype=idx_dtype),
        A.data,
        np.asarray(B.indptr, dtype=idx_dtype),
        np.asarray(B.indices, dtype=idx_dtype),
        B.data,
        ntop,
        lower_bound,
        indptr, indices, data)

    return csr_matrix((data, indices, indptr), shape=(M, N))


def get_matches_df(sparse_matrix, name_vector, top=5):
    non_zeros = sparse_matrix.nonzero()

    sparserows = non_zeros[0]
    sparsecols = non_zeros[1]

    if top:
        print (top)
        nr_matches = top
    else:
        print (sparsecols.size)
        nr_matches = sparsecols.size

    left_side = np.empty([nr_matches], dtype=object)
    right_side = np.empty([nr_matches], dtype=object)
    similairity = np.zeros(nr_matches)

    for index in range(0, nr_matches):
        left_side[index] = name_vector[sparserows[index]]
        right_side[index] = name_vector[sparsecols[index]]
        similairity[index] = sparse_matrix.data[index]

    return pd.DataFrame({'left_side': left_side,
                         'right_side': right_side,
                         'similairity': similairity})


company_names = df_clean['name']
vectorizer = TfidfVectorizer(min_df=1, analyzer=ngrams)
tf_idf_matrix = vectorizer.fit_transform(company_names)


import time
t1 = time.time()
matches = awesome_cossim_top(tf_idf_matrix, tf_idf_matrix.transpose(), 4, 0.8)
t = time.time()-t1
print("SELFTIMED:", t)



matches_df = get_matches_df(matches, company_names, top=4)
matches_df = matches_df[matches_df['similairity'] < 0.99999] # Remove all exact matches


with pd.option_context('display.max_rows', None, 'display.max_columns', None):
    print(matches_df)

The expected result is as follows:

gogle = google
amazn = amazon
fcbook = facebook

You can import awesome_cossim_top function directly from the sparse_dot_topn lib.

Change the function get_matches_df with this:

def get_matches_df(sparse_matrix, A, B, top=100):
    non_zeros = sparse_matrix.nonzero()

    sparserows = non_zeros[0]
    sparsecols = non_zeros[1]

    if top:
        nr_matches = top
    else:
        nr_matches = sparsecols.size

    left_side = np.empty([nr_matches], dtype=object)
    right_side = np.empty([nr_matches], dtype=object)
    similairity = np.zeros(nr_matches)

    for index in range(0, nr_matches):
        left_side[index] = A[sparserows[index]]
        right_side[index] = B[sparsecols[index]]
        similairity[index] = sparse_matrix.data[index]

    return pd.DataFrame({'left_side': left_side,
                         'right_side': right_side,
                         'similairity': similairity})

Now you can execute your code as below:

df_dirty = {"name":["gogle","bing","amazn","facebook","fcbook","abbasasdfzz","zsdfzl"]}

df_clean = {"name":["google","bing","amazon","facebook"]}

print (df_dirty["name"])
print (df_clean["name"])

vectorizer = TfidfVectorizer(min_df=1, analyzer=ngrams)
tf_idf_matrix_clean = vectorizer.fit_transform(df_clean['name'])
tf_idf_matrix_dirty = vectorizer.transform(df_dirty['name'])

t1 = time.time()
matches = awesome_cossim_top(tf_idf_matrix_dirty, tf_idf_matrix_clean.transpose(), 1, 0)
t = time.time()-t1
print("SELFTIMED:", t)

matches_df = get_matches_df(matches, df_dirty['name'], df_clean['name'], top=0)

with pd.option_context('display.max_rows', None, 'display.max_columns', None):
    print(matches_df)

Basically the example you found identifies duplicates in its own array and you want to use 2 sources instead of one.

Hope it helps!

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