コード例 #1
0
ファイル: vs_tf_skl.py プロジェクト: happinesstaker/FYP
class VS_TF_SKL(vector_space.VectorSpace):

    collection_of_document_term_vectors = sp.csc_matrix([]) # should we convert it to sparse matrix?
    model = NMF(n_components=FYPsetting.LSA_DIMENSION, random_state=0)
    #model = TruncatedSVD(n_components=FYPsetting.LSA_DIMENSION, random_state=42)
    tfidf = TFIDF(collection_of_document_term_vectors)

    def __init__(self, documents = [], transforms = [TFIDF, LSA]):
        start_time = datetime.now()
        print datetime.now(), " Building base class..."
        vector_space.VectorSpace.__init__(self, documents, transforms)
        print datetime.now(), " Base class built."
        print datetime.now()-start_time
        self._make_term_doc_matrix(documents, transforms)


    def _make_term_doc_matrix(self, documents, transforms):

        start_time = datetime.now()
        print datetime.now(), " Building term-doc matrix..."
        matrix = [self._make_vector(doc) for doc in documents]
        print datetime.now(), " Term-doc matrix finished."
        matrix = sp.coo_matrix(matrix).asfptype()
        print datetime.now(), " Term-doc matrix converted to sparse matrix."
        print datetime.now() -  start_time
        print datetime.now(), " Calculating tf-idf..."
        self.tfidf = TFIDF(matrix.tocsr())
        matrix = self.tfidf.transform()
        print datetime.now(), " Doing transformation..."
        if matrix.get_shape()[0]>FYPsetting.LSA_DIMENSION:
            self.model.fit_transform(matrix)
        self.collection_of_document_term_vectors = sp.coo_matrix(matrix).tocsc()
        print datetime.now(), " Reduced tf-idf matrix converted to sparse matrix..."
        print self.collection_of_document_term_vectors.todense()
        print datetime.now(), " Tf-idf matrix built."

    def _make_vector(self, word_string):
        vector = [0] * len(self.vector_index_to_keyword_mapping)

        word_list = self.parser.tokenise_and_remove_stop_words(word_string.split(" "))

        for word in word_list:
            try:
                index = self.vector_index_to_keyword_mapping[word]
                vector[index] += 1 #Use simple Term Count Model
            except:
                pass
        return vector

    def _build_query_vector(self, term_list):
        """ convert query string into a term vector """
        query = self._make_vector(" ".join(term_list))
        return query
コード例 #2
0
ファイル: vs_tf_skl.py プロジェクト: happinesstaker/FYP
    def _make_term_doc_matrix(self, documents, transforms):

        start_time = datetime.now()
        print datetime.now(), " Building term-doc matrix..."
        matrix = [self._make_vector(doc) for doc in documents]
        print datetime.now(), " Term-doc matrix finished."
        matrix = sp.coo_matrix(matrix).asfptype()
        print datetime.now(), " Term-doc matrix converted to sparse matrix."
        print datetime.now() -  start_time
        print datetime.now(), " Calculating tf-idf..."
        self.tfidf = TFIDF(matrix.tocsr())
        matrix = self.tfidf.transform()
        print datetime.now(), " Doing transformation..."
        if matrix.get_shape()[0]>FYPsetting.LSA_DIMENSION:
            self.model.fit_transform(matrix)
        self.collection_of_document_term_vectors = sp.coo_matrix(matrix).tocsc()
        print datetime.now(), " Reduced tf-idf matrix converted to sparse matrix..."
        print self.collection_of_document_term_vectors.todense()
        print datetime.now(), " Tf-idf matrix built."
コード例 #3
0
ファイル: vs_tf.py プロジェクト: happinesstaker/FYP
class VS_TF(vector_space.VectorSpace):

    collection_of_document_term_vectors = sp.csc_matrix([])  # should we convert it to sparse matrix?

    def __init__(self, documents=[], transforms=[TFIDF, LSA]):
        start_time = datetime.now()
        print datetime.now(), " Building base class..."
        vector_space.VectorSpace.__init__(self, documents, transforms)
        print datetime.now(), " Base class built."
        print datetime.now() - start_time
        self._make_term_doc_matrix(documents, transforms)

    def related(self, document_id):
        """ find documents that are related to the document indexed by passed Id within the document Vectors"""
        ratings = [
            self._cosine(self.collection_of_document_term_vectors[document_id], document_vector)
            for document_vector in self.collection_of_document_term_vectors
        ]
        ratings.sort(reverse=True)
        return ratings

    def search(self, searchList):
        """ search for documents that match based on a list of terms """
        queryVector = self._build_query_vector(searchList)

        ratings = [
            self._cosine(queryVector, documentVector) for documentVector in self.collection_of_document_term_vectors
        ]
        ratings.sort(reverse=True)
        return ratings

    def _make_term_doc_matrix(self, documents, transforms):

        start_time = datetime.now()
        print datetime.now(), " Building term-doc matrix..."
        matrix = [self._make_vector(doc) for doc in documents]
        print datetime.now(), " Term-doc matrix finished."
        matrix = sp.coo_matrix(matrix).asfptype()
        print datetime.now(), " Term-doc matrix converted to sparse matrix."
        print datetime.now() - start_time
        print datetime.now(), " Calculating tf-idf..."
        self.tfidf = TFIDF(matrix.tocsr())
        matrix = self.tfidf.transform()
        print datetime.now(), " Doing transformation..."
        if matrix.get_shape()[0] > FYPsetting.LSA_DIMENSION:
            matrix = LSA(matrix).transform(FYPsetting.LSA_DIMENSION)
        self.collection_of_document_term_vectors = sp.coo_matrix(matrix).tocsc()
        print datetime.now(), " Reduced tf-idf matrix converted to sparse matrix..."
        print self.collection_of_document_term_vectors.todense()
        print datetime.now(), " Tf-idf matrix built."

    def _make_vector(self, word_string):
        vector = [0] * len(self.vector_index_to_keyword_mapping)

        word_list = self.parser.tokenise_and_remove_stop_words(word_string.split(" "))

        for word in word_list:
            try:
                index = self.vector_index_to_keyword_mapping[word]
                vector[index] += 1  # Use simple Term Count Model
            except:
                pass
        return vector

    def _build_query_vector(self, term_list):
        """ convert query string into a term vector """
        query = self._make_vector(" ".join(term_list))
        return query