def get_top(data, topn, threshold):
sent_tokens, word_tokens = ms.tokenize(data)
words = list(set(ms.get_words(data)))
N = len(sent_tokens)
idf = similarity.idf(word_tokens, words, N)
matrix = similarity.get_similarity_matrix(word_tokens, idf)
gr = build_graph(sent_tokens, threshold, idf)
keysentences = get_keysentences(gr)
return keysentences[:topn]
def prediction(input_data, target="item", method="weighted_sum"):
'''
Predict value not rated
:param input_data: rating data to be predicted
:param method: prediction method(weighted_sum, linear_regression, ...)
:returns: predicted rating value
'''
sim = similarity.get_similarity_matrix(input_data, target=target)
axis = 0 if target == "item" else 1
if method == "weighted_sum":
return weighted_sum(input_data, sim, axis=axis)
elif method == "linear_regression":
pass
#%%
user_list = [
"Chang", "Chan", "jmpark", "Ruby", "suji kang", "Cold New User",
"Hot New User", "Chang's soul mate"
]
restaurant_list = ["아비꼬", "롱타임노씨", "피맥하우스", "오신 매운갈비찜", "오빠닭", "바나나 피자"]
test_data = np.array([[2.5, 3.5, 3.0, 3.5, 2.5, 3.0],
[3.0, 3.5, 1.5, 5.0, 3.5, 3.0],
[2.5, 3.5, 0, 3.5, 0, 4.0], [0, 3.5, 3.0, 4.0, 2.5, 4.5],
[1.0, 0, 3.0, 2.0, 0, 1.5], [0, 0, 0, 0, 0, 0],
[1.0, 0, 2.5, 3.5, 3.5, 4.5],
[2.5, 3.5, 3.0, 3.5, 2.5, 3.0]])
#%%
similarity_matrix = get_similarity_matrix(test_data, target="item")
print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
print("@@@@@Item-based Similarity Matrix@@@@@@@@@@@@@@@@@@@@@@")
print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
print(similarity_matrix)
print("\n\n")
result = prediction(test_data, target="item")
print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
print("@@@@@Item-based Predicted Rating Matrix@@@@@@@@@@@@@@@@")
print(result)
print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
print("\n\n")
similarity_matrix = get_similarity_matrix(test_data, target="user")
print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
key=calculated_page_rank.get,
reverse=False)
return keysentences
if __name__ == "__main__":
#creating a argparser
parser = argparse.ArgumentParser(
description="Pass the data fname and threshold value")
parser.add_argument("fname", help="Provide the data file name")
parser.add_argument("threshold",
help="Provide the threshold value",
default=0.15,
type=float)
parser.add_argument("N",
help="Top N sentences to be picked",
default=10,
type=int)
args = parser.parse_args()
with open(args.fname, "r") as f:
data = f.read()
sent_tokens, word_tokens = ms.tokenize(data)
words = list(set(ms.get_words(data)))
N = len(sent_tokens)
idf = similarity.idf(word_tokens, words, N)
matrix = similarity.get_similarity_matrix(word_tokens, idf)
print("Printing similarity matrix:\n", matrix)
gr = build_graph(sent_tokens, args.threshold, idf)
keysentences = get_keysentences(gr)
print("Printing Top 10 Key sentences:\n", keysentences[:args.N])
it += 1
B.append(r)
'''
#key sentences are obtained
def get_keysentences(graph):
# weight is the similarity value obtained from the idf_modified_cosine
calculated_page_rank = nx.pagerank(graph, weight='weight')
#most important words in ascending order of importance
keysentences = sorted(calculated_page_rank, key=calculated_page_rank.get, reverse=False)
return keysentences
if __name__ == "__main__":
#creating a argparser
parser = argparse.ArgumentParser(description="Pass the data fname and threshold value")
parser.add_argument("fname", help="Provide the data file name")
parser.add_argument("threshold", help="Provide the threshold value", default=0.15, type=float)
parser.add_argument("N", help="Top N sentences to be picked", default=10, type=int)
args = parser.parse_args()
with open(args.fname, "r") as f:
data = f.read()
sent_tokens, word_tokens = ms.tokenize(data)
words = list(set(ms.get_words(data)))
N = len(sent_tokens)
idf = similarity.idf(word_tokens, words, N)
matrix = similarity.get_similarity_matrix(word_tokens, idf)
print ("Printing similarity matrix:\n", matrix)
gr = build_graph(sent_tokens, args.threshold, idf)
keysentences = get_keysentences(gr)
print ("Printing Top 10 Key sentences:\n",keysentences[:args.N])