kse-01/search-data.py

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import re
import argparse
import os
import pandas as pd
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import nltk
import numpy as np
from nltk.corpus import stopwords
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from gensim.similarities import SparseMatrixSimilarity, MatrixSimilarity
from gensim.models import TfidfModel, LsiModel, LdaModel
from gensim.corpora import Dictionary
from collections import defaultdict
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nltk.download('stopwords')
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SCRIPT_DIR = os.path.abspath(os.path.dirname(__file__))
IN_DATASET = os.path.join(SCRIPT_DIR, "data.csv")
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# using ntlk stop words and example words for now
STOP_WORDS = set(stopwords.words('english')) \
.union(['test', 'tests', 'main', 'this'])
def find_all(regex, word):
matches = re.finditer(regex, word)
return [m.group(0).lower() for m in matches]
# https://stackoverflow.com/a/29920015
def camel_case_split(word):
return find_all('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)', word)
def identifier_split(identifier):
return [y for x in identifier.split("_") for y in camel_case_split(x)]
def comment_split(comment):
return find_all('[A-Za-z0-9]+', comment)
def remove_stopwords(input_bow_list):
return [word for word in input_bow_list if word not in STOP_WORDS]
def get_bow(data, split_f):
if data is None or (type(data) == float and np.isnan(data)):
return []
return remove_stopwords(split_f(data))
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def print_sims(corpus, query, df, dictionary):
index = SparseMatrixSimilarity(corpus, num_features=len(dictionary))
sims = index[query]
pick_top = 5
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for idx, score in sorted(enumerate(sims), key=lambda x: x[1], reverse=True)[:pick_top]:
row = df.loc[idx]
print("Similarity: {s:2.02f}%".format(s=score*100))
print("Python {feat}: {name}\nFile: {file}\nLine: {line}\n" \
.format(feat=row["type"], name=row["name"], file=row["file"], line=row["line"]))
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def search(query, method):
df = pd.read_csv(IN_DATASET)
df["name_bow"] = df["name"].apply(lambda n: get_bow(n, identifier_split))
df["comment_bow"] = df["comment"].apply(lambda c: get_bow(c, comment_split))
corpus_list = []
for idx, row in df.iterrows():
document_words = row["name_bow"] + row["comment_bow"]
corpus_list.append(document_words)
dictionary = Dictionary(corpus_list)
corpus_bow = [dictionary.doc2bow(text) for text in corpus_list]
query_bow = dictionary.doc2bow(get_bow(query, comment_split))
if method == "tfidf":
tfidf = TfidfModel(corpus_bow)
print_sims(tfidf[corpus_bow], tfidf[query_bow], df, dictionary)
elif method == "freq":
print_sims(corpus_bow, query_bow, df, dictionary)
elif method == "lsi":
lsi = LsiModel(corpus_bow)
print_sims(lsi[corpus_bow], lsi[query_bow], df, dictionary)
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def main():
parser = argparse.ArgumentParser()
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parser.add_argument("method", help="the method to compare similarities with", type=str)
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parser.add_argument("query", help="the query to search the corpus with", type=str)
args = parser.parse_args()
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search(args.query, args.method)
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if __name__ == "__main__":
main()