def run(self, query, page=Page(0, 20)):
'''
Procedure:
1. tokenize the query
2. calculate query weight
3. calculate all distances
4. sort distances
5. return sorted result
Args:
query: query string
page: page size and offset
Returns:
list of identifiers
'''
# tokenize query
tokens = tokenize_text(query)
if len(tokens) <= 0:
return []
# calculate query weigth
bag_of_words = bag(tokens)
max_freq = bag_of_words[max(bag_of_words, key=bag_of_words.get)]
query_tf = lil_matrix((1, self.tokens_no))
for token, freq in bag_of_words.items():
if token not in self.tokens:
continue
index = self.tokens[token]
query_tf[0, index] = tf(freq, max_freq)
query_tf = csr_matrix(query_tf)
query_w = csr_matrix(query_tf.multiply(self.idf))
# calculate distances between all documents and query
distances = self.distance(self.tf_idf, query_w)
# sort results and return specified page
distances = distances[:, 0]
sorted_indices = np.argsort(distances)
top = sorted_indices[page.start_index:page.end_index]
f = np.vectorize(lambda x: self.iterative_docs[x])
result = list(f(top))
return result
def run(self, query, page=Page(0, 20)):
'''
Procedure:
1. tokenize the query
2. calculate query weight
3. calculate all distances
4. sort distances
5. return sorted result
Args:
query: query string
page: page size and offset
Returns:
list of identifiers
'''
# tokenize query
tokens = tokenize_text(query)
if len(tokens) <= 0:
return []
# calculate query weigth
bag_of_words = bag(tokens)
max_freq = bag_of_words[max(bag_of_words, key=bag_of_words.get)]
query_tf = lil_matrix((1, self.tokens_no))
for token, freq in bag_of_words.items():
if token not in self.tokens:
continue
index = self.tokens[token]
query_tf[0, index] = tf(freq, max_freq)
query_tf = csr_matrix(query_tf)
query_w = csr_matrix(query_tf.multiply(self.idf))
# calculate distances between all documents and query
distances = self.distance(self.tf_idf, query_w)
# sort results and return specified page
distances = distances[:, 0]
sorted_indices = np.argsort(distances)
top = sorted_indices[page.start_index:page.end_index]
f = np.vectorize(lambda x: self.iterative_docs[x])
result = list(f(top))
return result
def preprocess_one(raw_file):
'''
Procedure:
1. create document
2. fill with the data
Args:
raw_file: text (string)
Returns:
Document
'''
document = Document()
document.text = raw_file
document.identifier = text_hash(raw_file)
document.content_hash = document.identifier
document.tokens = tokenize_text(raw_file)
document.bag = bag(document.tokens)
return document
def preprocess_one(raw_file):
'''
Procedure:
1. create document
2. fill with the data
Args:
raw_file: text (string)
Returns:
Document
'''
document = Document()
document.text = raw_file
document.identifier = text_hash(raw_file)
document.content_hash = document.identifier
document.tokens = tokenize_text(raw_file)
document.bag = bag(document.tokens)
return document
