class IR:
def __init__(self,
documents: List,
stop_words: Set,
stemming: bool = True):
self.documents = documents
self.stop_words = stop_words
self.stemming = stemming
self.proccess_documents(self.documents)
self.dictionary = None
self.corpus = None
self.tfidf_model = None
self.tfidf_corpus = None
self.tfidf_similarity = None
self.lsi_model = None
self.lsi_corpus = None
self.lsi_similarity = None
self.build_dictionary()
self.build_models()
""" 1. Data loading and preprocessing """
def proccess_documents(self, documents: List) -> List:
# Tokenize documents
result = self.tokenize(documents)
# Lowercase all words
result = list(map(lambda x: self.lowercase(x), result))
# Remove stop words
result = self.filter_stopwords(result)
# Remove text punctuation
result = self.remove_text_punctation(result)
# Stem words
if self.stemming:
result = self.port_stem(result)
# Remove empty words from all documents
return self.filter_empty_words(result)
""" 2. Dictionary building """
def build_dictionary(self):
documents = self.proccess_documents(self.documents)
self.dictionary = Dictionary(documents)
self.corpus = list(
map(lambda doc: self.dictionary.doc2bow(doc), documents))
""" 3. Retrieval Models """
def build_models(self):
# Create tfidf model
self.tfidf_model = TfidfModel(self.corpus)
# Map bag of words to (word-index, word-weight)
self.tfidf_corpus = list(
map(lambda c: self.tfidf_model[c], self.corpus))
self.tfidf_similarity = MatrixSimilarity(self.tfidf_corpus)
self.lsi_model = LsiModel(self.tfidf_corpus,
id2word=self.dictionary,
num_topics=100)
self.lsi_corpus = list(
map(lambda c: self.lsi_model[c], self.tfidf_corpus))
self.lsi_similarity = MatrixSimilarity(self.lsi_corpus)
def filter_stopwords(self, paragraphs: List) -> List:
return list(
map(lambda p: list(filter(lambda x: x not in self.stop_words, p)),
paragraphs))
""" 4. Querying """
def procces_query(self, query: str) -> List:
tokenized = self.tokenize([query])
lowered = list(map(lambda x: self.lowercase(x), tokenized))
stop_word_filtered = self.filter_stopwords(lowered)
punctation_filtered = self.remove_text_punctation(stop_word_filtered)
if self.stemming:
return self.port_stem(punctation_filtered)
return punctation_filtered
def tfidf_query(self, query: str, number_of_results: int = 3) -> None:
# Proccess query
proccessed_query = self.procces_query(query)
query_corpus = self.dictionary.doc2bow(proccessed_query[0])
query_tfidf = self.tfidf_model[query_corpus]
similarity = enumerate(self.tfidf_similarity[query_tfidf])
# Query most relevant paragraphs using TFIDF model
query_result = sorted(similarity,
key=lambda kv: -kv[1])[:number_of_results]
# Print search result
for result in query_result:
number, _ = result
print("Paragraph:", number)
print(self.documents[number], "\n")
def lsi_query(self, query: str, number_of_results: int = 3) -> None:
# Proccess query
proccessed_query = self.procces_query(query)
query_corpus = self.dictionary.doc2bow(proccessed_query[0])
tfidf_query = self.tfidf_model[query_corpus]
lsi_query = self.lsi_model[tfidf_query]
# Fetch most relevant topics
relevant_topics = sorted(
lsi_query, key=lambda kv: -abs(kv[1]))[:number_of_results]
# Print
for result in relevant_topics:
number, _ = result
print("Topic:", number)
print(self.lsi_model.show_topic(number))
print()
# Find most relevant paragraphs using LSI similarity
doc2similarity = enumerate(self.lsi_similarity[lsi_query])
query_result = sorted(doc2similarity,
key=lambda kv: -kv[1])[:number_of_results]
# Print query result
for result in query_result:
number, _ = result
print("Paragraph:", number)
print(self.documents[number], "\n")
""" All methods below is helpers to preproccess both documents and queries. """
@staticmethod
def filter_empty_words(paragraphs: List) -> List:
return list(
map(lambda p: list(filter(lambda w: w != "", p)), paragraphs))
@staticmethod
def tokenize(documents: List) -> List:
return list(map(lambda x: x.split(), documents))
@staticmethod
def lowercase(words: List) -> List:
return list(map(lambda s: s.lower(), words))
@staticmethod
def port_stem(documents: List) -> List:
stemmer = PorterStemmer()
return list(
map(lambda p: list(map(lambda w: stemmer.stem(w), p)), documents))
@staticmethod
def remove_text_punctation(documents: List) -> List:
regular = "[" + string.punctuation + "\n\r\t" + "]"
return list(
map(lambda p: list(map(lambda w: re.sub(regular, "", w), p)),
documents))
lsi.save(os.path.join(DATA_PATH, 'lsi100'))
lsi2.save(os.path.join(DATA_PATH, 'lsi2'))
# In[16]:
lsi2.show_topics()
# In[23]:
# for topic in lsi.show_topics():
# print(topic)
lsi.show_topic(0, 100)
# ## Hold onto your hat
# This will take a lot of RAM!
# (and CPU)
# In[31]:
tweetids = pd.Series(range(len(bows)), name='tweet')
topicids = pd.Series(range(lsi.num_topics), name='topic')
# `dict()` keeps track of the columns for each topic, in case the lsi model shuffles or skips topics for odd tweets
df = pd.DataFrame([pd.Series(dict(lsi[bows[i]]), name='tweet') for i in tweetids],
columns=topicids,
index=tweetids)