class LSASearchEngine(SmartSearchEngine):
#
# Registry implementation using kd-tree
def __init__(self):
super(LSASearchEngine, self).__init__()
self._service_array = []
self._lsi_index = None
self._tfidf_matrix = None
self._svd_matrix = None
def load_configuration(self, configuration_file):
super(LSASearchEngine, self).load_configuration(configuration_file)
config = configparser.ConfigParser()
config.read(configuration_file)
number_of_topics = config.getint('RegistryConfigurations',
'number_of_topics')
self._metric = config.get('RegistryConfigurations', 'metric').lower()
self._svd = TruncatedSVD(n_components=number_of_topics)
self._vectorizer = TfidfVectorizer(
sublinear_tf=False,
analyzer='word',
lowercase=False,
use_bm25idf=self._use_bm25idf,
bm25_tf=self._use_bm25tf,
k=self._bm25_k,
preprocessor=StringPreprocessorAdapter('english.long'))
def unpublish(self, service):
pass
def _preprocess(self, bag_of_words):
return bag_of_words.get_words_str()
def _after_publish(self, documents):
self._tfidf_matrix = self._vectorizer.fit_transform(documents)
self._svd_matrix = self._svd.fit_transform(
self._tfidf_matrix.toarray())
self._lsi_index = NearestNeighbors(len(self._service_array),
algorithm='brute',
metric=self._metric)
self._lsi_index.fit(self._svd_matrix)
def publish(self, service):
pass
def find(self, query):
query = StringTransformer().transform(query)
query_array = self._vectorizer.transform(
[self._query_transformer.transform(query).get_words_str()])
query_array = self._svd.transform(query_array.toarray())
result = self._lsi_index.kneighbors(query_array,
return_distance=False)[0]
result_list = []
for index in result:
result_list.append(self._service_array[index])
return result_list
def number_of_services(self):
pass
class BallTreeSearchEngine(SmartSearchEngine):
#
# Registry implementation using ball-tree
def __init__(self):
super(BallTreeSearchEngine, self).__init__()
self._service_array = []
self._balltree_index = None
self._tfidf_matrix = None
def load_configuration(self, configuration_file):
super(BallTreeSearchEngine,
self).load_configuration(configuration_file)
config = configparser.ConfigParser()
config.read(configuration_file)
self._metric = config.get('RegistryConfigurations', 'metric').lower()
self._leaf_size = config.getint('RegistryConfigurations', 'leaf_size')
self._vectorizer = TfidfVectorizer(
sublinear_tf=False,
analyzer='word',
lowercase=False,
use_bm25idf=self._use_bm25idf,
bm25_tf=self._use_bm25tf,
k=self._bm25_k,
preprocessor=StringPreprocessorAdapter())
def unpublish(self, service):
pass
def _preprocess(self, bag_of_words):
return bag_of_words.get_words_str()
def _after_publish(self, documents):
self._tfidf_matrix = self._vectorizer.fit_transform(documents)
self._balltree_index = BallTree(self._tfidf_matrix.toarray(),
leaf_size=self._leaf_size,
metric=self._metric)
def publish(self, service):
pass
def find(self, query):
query = StringTransformer().transform(query)
query_array = self._vectorizer.transform([
self._query_transformer.transform(query).get_words_str()
]).toarray()
result = self._balltree_index.query(query_array,
k=len(self._service_array),
return_distance=False)
result_list = []
for index in result[0]:
result_list.append(self._service_array[index])
return result_list
def number_of_services(self):
pass
class BernoulliRBMSearchEngine(SmartSearchEngine):
#
# Registry implementation using ball-tree
def __init__(self):
super(BernoulliRBMSearchEngine, self).__init__()
self._service_array = []
self._bernoulliRBM_index = None
self._tfidf_matrix = None
def load_configuration(self, configuration_file):
super(BernoulliRBMSearchEngine,
self).load_configuration(configuration_file)
self._vectorizer = TfidfVectorizer(
sublinear_tf=False,
analyzer='word',
lowercase=False,
use_bm25idf=self._use_bm25idf,
bm25_tf=self._use_bm25tf,
k=self._bm25_k,
preprocessor=StringPreprocessorAdapter())
def unpublish(self, service):
pass
def _preprocess(self, bag_of_words):
return bag_of_words.get_words_str()
def _after_publish(self, documents):
self._tfidf_matrix = self._vectorizer.fit_transform(documents)
self._bernoulliRBM = BernoulliRBM(learning_rate=1)
self._rbm_matrix = self._bernoulliRBM.fit_transform(self._tfidf_matrix)
self._bernoulliRBM_index = NearestNeighbors(len(self._service_array),
algorithm='brute',
metric='euclidean')
self._bernoulliRBM_index.fit(self._rbm_matrix)
def publish(self, service):
pass
def find(self, query):
query = StringTransformer().transform(query)
query_array = self._vectorizer.transform(
[self._query_transformer.transform(query).get_words_str()])
query_array = self._bernoulliRBM.transform(query_array.toarray())
result = self._bernoulliRBM_index.kneighbors(query_array,
return_distance=False)[0]
result_list = []
for index in result:
result_list.append(self._service_array[index])
return result_list
def number_of_services(self):
pass
def load_configuration(self, configuration_file):
super(BernoulliRBMSearchEngine,
self).load_configuration(configuration_file)
self._vectorizer = TfidfVectorizer(
sublinear_tf=False,
analyzer='word',
lowercase=False,
use_bm25idf=self._use_bm25idf,
bm25_tf=self._use_bm25tf,
k=self._bm25_k,
preprocessor=StringPreprocessorAdapter())
def load_configuration(self, configuration_file):
super(KMeansSearchEngine, self).load_configuration(configuration_file)
config = configparser.ConfigParser()
config.read(configuration_file)
self._n_clusters = config.getint('RegistryConfigurations', 'n_clusters')
self._vectorizer = TfidfVectorizer(sublinear_tf=False,
analyzer='word', lowercase=False, use_bm25idf=self._use_bm25idf,
bm25_tf=self._use_bm25tf, k = self._bm25_k,
preprocessor=StringPreprocessorAdapter('english.long'))
def load_configuration(self, configuration_file):
super(KDTreeSearchEngine, self).load_configuration(configuration_file)
config = configparser.ConfigParser()
config.read(configuration_file)
self._metric = config.get('RegistryConfigurations', 'metric').lower()
self._leaf_size = config.getint('RegistryConfigurations', 'leaf_size')
self._vectorizer = TfidfVectorizer(sublinear_tf=False,
analyzer='word', lowercase=False, use_bm25idf=self._use_bm25idf,
bm25_tf=self._use_bm25tf, k = self._bm25_k,
preprocessor=StringPreprocessorAdapter())
def load_configuration(self, configuration_file):
super(LSASearchEngine, self).load_configuration(configuration_file)
config = configparser.ConfigParser()
config.read(configuration_file)
number_of_topics = config.getint('RegistryConfigurations',
'number_of_topics')
self._metric = config.get('RegistryConfigurations', 'metric').lower()
self._svd = TruncatedSVD(n_components=number_of_topics)
self._vectorizer = TfidfVectorizer(
sublinear_tf=False,
analyzer='word',
lowercase=False,
use_bm25idf=self._use_bm25idf,
bm25_tf=self._use_bm25tf,
k=self._bm25_k,
preprocessor=StringPreprocessorAdapter('english.long'))
def load_configuration(self, configuration_file):
super(LDASearchEngine, self).load_configuration(configuration_file)
config = configparser.ConfigParser()
config.read(configuration_file)
number_of_topics = config.getint('RegistryConfigurations',
'number_of_topics')
self._lda = LatentDirichletAllocation(n_components=number_of_topics,
learning_method='batch',
random_state=23)
self._metric = config.get('RegistryConfigurations', 'metric').lower()
self._vectorizer = TfidfVectorizer(
sublinear_tf=False,
analyzer='word',
lowercase=False,
use_bm25idf=self._use_bm25idf,
bm25_tf=self._use_bm25tf,
k=self._bm25_k,
preprocessor=StringPreprocessorAdapter('english.long'))
class KMeansSearchEngine(SmartSearchEngine):
#
# Registry implementation using clusters
def __init__(self):
super(KMeansSearchEngine, self).__init__()
self._service_array = []
self._cluster_index = None
self._cluster = {}
self._document_cluster = {}
self._tfidf_matrix = None
def load_configuration(self, configuration_file):
super(KMeansSearchEngine, self).load_configuration(configuration_file)
config = configparser.ConfigParser()
config.read(configuration_file)
self._n_clusters = config.getint('RegistryConfigurations', 'n_clusters')
self._vectorizer = TfidfVectorizer(sublinear_tf=False,
analyzer='word', lowercase=False, use_bm25idf=self._use_bm25idf,
bm25_tf=self._use_bm25tf, k = self._bm25_k,
preprocessor=StringPreprocessorAdapter('english.long'))
def unpublish(self, service):
pass
def _preprocess(self, bag_of_words):
return bag_of_words.get_words_str()
def _after_publish(self, documents):
self._tfidf_matrix = self._vectorizer.fit_transform(documents)
self._cluster_index = KMeans(n_clusters=self._n_clusters)
self._cluster_index.fit(self._tfidf_matrix.toarray())
i = 0
self._document_cluster = {}
for document in documents:
if not self._cluster_index.labels_[i] in self._document_cluster:
self._document_cluster[self._cluster_index.labels_[i]] = []
self._document_cluster[self._cluster_index.labels_[i]].append((document, i))
i += 1
print(('Number of clusters: ' + str(len(self._document_cluster))))
for label in self._document_cluster:
print(('Label elements: ' + str(len(self._document_cluster[label]))))
for label in self._document_cluster:
self._cluster[label] = NearestNeighbors(len(self._document_cluster[label]), algorithm='brute', metric='euclidean')
tfidf_matrix = self._vectorizer.transform(doc[0] for doc in self._document_cluster[label])
self._cluster[label].fit(tfidf_matrix.toarray())
def publish(self, service):
pass
def find(self, query):
query = StringTransformer().transform(query)
query_array = self._vectorizer.transform([self._query_transformer.transform(query).get_words_str()]).toarray()
target_label = self._cluster_index.predict(query_array)[0]
target_indexes = self._cluster[target_label].kneighbors(query_array, return_distance=False)[0]
result = []
for target in target_indexes:
result.append(self._document_cluster[target_label][target][1])
result_list = []
for index in result:
result_list.append(self._service_array[index])
return result_list
def number_of_services(self):
pass
