140 lines
4.5 KiB
Python
140 lines
4.5 KiB
Python
import re
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import argparse
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import os
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import pandas as pd
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import nltk
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import numpy as np
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from nltk.corpus import stopwords
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from gensim.similarities import SparseMatrixSimilarity, MatrixSimilarity
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from gensim.models import TfidfModel, LsiModel, LdaModel
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from gensim.models.doc2vec import TaggedDocument, Doc2Vec
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from gensim.corpora import Dictionary
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from collections import defaultdict
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import coloredlogs
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import logging
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coloredlogs.install()
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logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
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nltk.download('stopwords')
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SCRIPT_DIR = os.path.abspath(os.path.dirname(__file__))
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IN_DATASET = os.path.join(SCRIPT_DIR, "data.csv")
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DOC2VEC_MODEL = os.path.join(SCRIPT_DIR, "doc2vec_model.dat")
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# using ntlk stop words and example words for now
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STOP_WORDS = set(stopwords.words('english')) \
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.union(['test', 'tests', 'main', 'this', 'self'])
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def find_all(regex, word):
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matches = re.finditer(regex, word)
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return [m.group(0).lower() for m in matches]
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# https://stackoverflow.com/a/29920015
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def camel_case_split(word):
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return find_all('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)', word)
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def identifier_split(identifier):
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return [y for x in identifier.split("_") for y in camel_case_split(x)]
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def comment_split(comment):
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return find_all('[A-Za-z0-9]+', comment)
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def remove_stopwords(input_bow_list):
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return [word for word in input_bow_list if word not in STOP_WORDS]
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def get_bow(data, split_f):
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if data is None or (type(data) == float and np.isnan(data)):
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return []
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return remove_stopwords(split_f(data))
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def print_sims(corpus, query, df, dictionary):
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index = SparseMatrixSimilarity(corpus, num_features=len(dictionary))
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sims = index[query]
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pick_top = 5
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print_results(sorted(enumerate(sims), key=lambda x: x[1], reverse=True)[:pick_top], df)
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def print_results(idxs_scores, df):
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print("\n===== RESULTS: =====")
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for idx, score in idxs_scores:
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row = df.loc[idx]
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comment = row["comment"]
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if type(comment) != str:
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desc = ""
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else:
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comment = re.sub(re.compile(r'[\s\n]+', re.MULTILINE), ' ', comment)
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desc = "Description: {c}\n".format(c=comment)
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desc = (desc[:75] + '...\n') if len(desc) > 75 else desc
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print("\nSimilarity: {s:2.02f}%".format(s=score*100))
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print("Python {feat}: {name}\n{desc}File: {file}\nLine: {line}" \
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.format(feat=row["type"], name=row["name"], desc=desc, file=row["file"], line=row["line"]))
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def build_doc2vec_model(corpus_list):
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dvdocs = [TaggedDocument(text, [i]) for i, text in enumerate(corpus_list)]
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model = Doc2Vec(vector_size=100, epochs=100, sample=1e-5)
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model.build_vocab(dvdocs)
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model.train(dvdocs, total_examples=model.corpus_count, epochs=model.epochs)
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model.save(DOC2VEC_MODEL)
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return model
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def search(query, method):
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df = pd.read_csv(IN_DATASET)
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df["name_bow"] = df["name"].apply(lambda n: get_bow(n, identifier_split))
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df["comment_bow"] = df["comment"].apply(lambda c: get_bow(c, comment_split))
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corpus_list = []
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for idx, row in df.iterrows():
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document_words = row["name_bow"] + row["comment_bow"]
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corpus_list.append(document_words)
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query_w = get_bow(query, comment_split)
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if method != "doc2vec":
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dictionary = Dictionary(corpus_list)
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corpus_bow = [dictionary.doc2bow(text) for text in corpus_list]
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query_bow = dictionary.doc2bow(query_w)
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if method == "tfidf":
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tfidf = TfidfModel(corpus_bow)
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print_sims(tfidf[corpus_bow], tfidf[query_bow], df, dictionary)
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elif method == "freq":
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print_sims(corpus_bow, query_bow, df, dictionary)
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elif method == "lsi":
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lsi = LsiModel(corpus_bow)
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print_sims(lsi[corpus_bow], lsi[query_bow], df, dictionary)
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elif method == "doc2vec":
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if os.path.exists(DOC2VEC_MODEL):
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model = Doc2Vec.load(DOC2VEC_MODEL)
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else:
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model = build_doc2vec_model(corpus_list)
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dvquery = model.infer_vector(query_w)
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print_results(model.dv.most_similar([dvquery], topn=5), df)
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else:
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raise Error("method unknown")
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def main():
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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)
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args = parser.parse_args()
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search(args.query, args.method)
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if __name__ == "__main__":
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main()
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