Python ElementHelper.get_body方法代碼示例

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
def get_aricle_cetd(doctree):
    cetd_parse(doctree)
    body = ElementHelper.get_body(doctree)
    # ElementHelper.print_element(body)
    CleanTreeByMark(body)
    RemoveAttribute(body)
    return ElementHelper.element_text_content(body)

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
    def get_clustered_records(cls, doctree):
        #get level_nodes_mapping
        all_level_nodes = cls.bfs_tree(doctree)

        root = ElementHelper.get_root(doctree)
        body = ElementHelper.get_body(doctree)

        #get max level and min level
        upper_bound = int(ElementHelper.get_element_depth(root))+1
        low_bound = int(body.get(px))+1

        for level in range(low_bound, upper_bound):

            level_nodes = all_level_nodes[level]
            #if parent is record node, then do not consider its children
            level_nodes = [node for node in level_nodes if not cls.is_node_or_ancestor_record(node)]

            for j in range(1,len(level_nodes)-1):
                left_node = level_nodes[j-1]
                #橫向比較
                right_bound = min(len(level_nodes), j+5)
                right_nodes = level_nodes[j:right_bound]

                #縱向比較
                down_nodes = right_nodes[0]
                right_nodes.extend(down_nodes)

                for right_node in right_nodes:
                    if cls.similar_check(left_node, right_node):
                        left_node.set(kg_record_mark,'1')
                        right_node.set(kg_record_mark, '1')
                        break

        record_groups = cls.merger_sibling_record_node(doctree)
        return record_groups

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
    def merger_sibling_record_node(cls, doctree, cluster):
        ''' 融合數據記錄
        1.首先對數據記錄進行修正，然後將連續的數據記錄放入到一個集合中
        將同層次相同標簽的節點的節點放入一個集合中,然後在就行糾正，具體詳見correct_record_mark
        :param doctree: 經過了初步的相似度比較之後標記了的DOM樹
        :param cluster: 初步的相似的數據記錄的集合
        :return:
        '''
        node_record_mapping = {}

        body = ElementHelper.get_body(doctree)
        thislevel = []
        thislevel.extend(body)
        # while thislevel:
        #     nextlevel = list()
        #     for node in thislevel:
        #         # correct nodes which
        #         cls.correct_record_mark(node)
        #
        #         if cls.is_node_or_ancestor_record(node):
        #             first_record_sibling = cls.find_first_sibling_record_node(node, doctree)
        #             node_record_mapping.setdefault(first_record_sibling, []).append(node)
        #ToDo 2016-04-20
        while thislevel:
            nextlevel = list()
            cls.correct_record_mark(thislevel, cluster)
            for node in thislevel:
                if len(node) > 0:
                    nextlevel.extend([child for child in node if not cls.is_node_or_ancestor_record(node)])
            thislevel = nextlevel

        return cluster

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
def html2words(docstring, base_url, encoding=None, supervisior=None):
    """
    從網頁源碼中抽取正文
    :param docstring:
    :param encoding:
    :return:
    """
    string_size=sys.getsizeof(docstring)
    byte_size=string_size / (1024)
    if byte_size < 1:
        return

    docstring=docstring.lower()
    doctree=HtmlHelper.create_doc(docstring, encoding)

    if doctree is None: return None
    copy_doc=copy.deepcopy(doctree)
    # try:
    #
    #     link_ratio=get_page_link_ratio(copy_doc)
    #     print 'link_ratio: %f' % link_ratio
    #
    #     if link_ratio > 0.6:
    #         print 'this is home page'
    #         return None
    # except ValueError:
    #     return None

    doctree=HtmlHelper.pre_process_domtree(doctree)
    if doctree is None:
        return None
    # get page title and para content
    para, title=HtmlHelper.get_article(doctree, debug=False)

    # get page meta keywords and meta description
    meta_description=HtmlHelper.get_meta_description(copy_doc)

    # get headlines in page
    cleaned_body=ElementHelper.get_body(doctree)
    headlines=HtmlHelper.get_headline_content_in_cleaned_body(cleaned_body)

    # get all urls
    url_items=[]
    for item in get_link_word_by_pair(docstring, base_url, supervisior): url_items.append(item)

    document=Document()
    document['base_url']=base_url
    document['title']=title
    document['meta']=meta_description
    document['headlines']=headlines
    document['para']=para
    document['url_items']=url_items


    return document

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
    def get_clustered_records(cls, doctree):
        #get level_nodes_mapping
        all_level_nodes = cls.bfs_tree(doctree)

        root = ElementHelper.get_root(doctree)
        body = ElementHelper.get_body(doctree)

        #get max level and min level
        upper_bound = int(ElementHelper.get_element_depth(root))+1
        low_bound = int(body.get(px))+1

        #記錄相似的節點
        cluster={}

        for level in range(low_bound, upper_bound):

            level_nodes = all_level_nodes[level]

            #if parent is record node, then do not consider its children
            level_nodes = [node for node in level_nodes if not cls.is_node_or_ancestor_record(node)]
            #在同一個父親節點下進行比較
            # tag_names = set([node.getparent() for node in level_nodes])
            # tmp = {}
            # for tag in tag_names:
            #     for node in level_nodes:
            #         tmp.setdefault(tag, []).append(node)
            tmp = cls.segement(level_nodes)

            for k, nodes in tmp.items():
                # if len(nodes)==1:break
                first = None
                node_set = set()
                for i in range(1,len(nodes)):
                    if nodes[i].get(kg_record_mark)=='1':
                        continue
                    left_node = nodes[i-1]
                    # 和集合類的所有元素比較，查看是否有相同的
                    right_nodes=nodes[i:]
                    for node in right_nodes:
                        if cls.similar_check(left_node, node):
                            if first is None:
                                first = left_node
                                node_set.add(nodes[i-1])
                            left_node.set(kg_record_mark, '1')
                            node.set(kg_record_mark, '1')
                            node_set.add(node)
                if first is not None:
                    cluster[first]=node_set

        record_groups = cls.merger_sibling_record_node(doctree, cluster)
        # record_groups = cluster
        record_groups = {k:v for k,v in record_groups.items() if k.get(kg_record_mark)=='1'}

        return record_groups

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
    def get_article_title_element(doctree):
        body = ElementHelper.get_body(doctree)

        title_node = HtmlHelper.get_title(doctree)

        if title_node is None:
            return None

        title_text = title_node.text
        title = get_title_util(body, title_text)
        if title is None: return None
        return title

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
def get_page_link_ratio(doctree):
    body = ElementHelper.get_body(doctree)
    CountChar(body)
    CountTag(body)
    CountLinkChar(body)
    CountLinkTag(body)
    char_num = float(body.attrib.get(kg_char_num))
    if char_num==0: char_num=1
    linkchar_num = float(body.attrib.get(kg_linkchar_num))
    ratio = linkchar_num/char_num

    return ratio

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
    def bfs_tree(cls, doctree):
        all_level_nodes = {}
        body = ElementHelper.get_body(doctree)

        level = int(body.get(px))
        thislevel = [body]
        while thislevel:
            nextlevel = list()
            all_level_nodes[level] = thislevel
            for node in thislevel:
                nextlevel.extend([child for child in node if len(node)>0])
            thislevel = nextlevel
            level += 1
        return all_level_nodes

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
    def get_clustered_records(cls, doctree):
        #get level_nodes_mapping
        all_level_nodes = cls.bfs_tree(doctree)

        root = ElementHelper.get_root(doctree)
        body = ElementHelper.get_body(doctree)

        #get max level and min level
        upper_bound = int(ElementHelper.get_element_depth(root))+1
        low_bound = int(body.get(px))+1

        for level in range(low_bound, upper_bound):

            level_nodes = all_level_nodes[level]
            try:
                next_level_nodes = all_level_nodes[level+1]
            except KeyError:
                next_level_nodes=None
            #if parent is record node, then do not consider its children
            level_nodes = [node for node in level_nodes if not cls.is_node_or_ancestor_record(node)]

            for j in range(1,len(level_nodes)-1):
                left_node = level_nodes[j-1]
                #將橫向名稱相同的節點放到一起進行比較
                # right_bound = min(len(level_nodes), j+5)
                # right_nodes = level_nodes[j:right_bound]
                # #縱向比較
                # down_nodes = right_nodes[0]
                # right_nodes.extend(down_nodes)

                right_nodes = [node for node in level_nodes[j:] if node.tag==left_node.tag]
                #縱向查找
                # if next_level_nodes is not None:
                #     for node in next_level_nodes:
                #         if node.tag==left_node.tag:
                #             right_node.append(node)





                for right_node in right_nodes:
                    if cls.similar_check(left_node, right_node):
                        left_node.set(kg_record_mark,'1')
                        right_node.set(kg_record_mark, '1')
                        break

        record_groups = cls.merger_sibling_record_node(doctree)
        return record_groups

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
def clean_body(clusters, doctree, title_node=None, debug = False):
    #filter user comments and all link records
    clusters = filter_cluster(clusters)
    if len(clusters) == 0:
        title = ElementHelper.element_text_content(title_node)
        return title, title

    #choose cluster which has most texts
    maxCluster = get_biggest_cluster(clusters)
    nodes = clusters[maxCluster]

    # m = {}
    # m[maxCluster] = nodes
    # filter_cluster(m)


    #get all children of max cluster record
    allnodes = []
    for node in nodes:
        children = ElementHelper.get_children(node)
        allnodes.extend(children)

    #sort max cluster nodes by its preorder index
    allnodes.sort(lambda p,q:ElementHelper.get_element_preorder_num(p)-ElementHelper.get_element_preorder_num(q))
    # debug=True
    if debug:
        print 'maxCluster: %s' % maxCluster
        for n in allnodes:
            print ElementHelper.get_xpath_by_element(n, doctree), n.get(py)
    s = ElementHelper.get_element_preorder_num(allnodes[0])
    t = ElementHelper.get_element_preorder_num(allnodes[-1])

    #correct start position by title node
    title_text = ''
    #====================================================================
    #在實驗時借助title糾正正文文本的起始位置可以提高recall,但是實際過程中不需要
    if title_node is not None:
        index = ElementHelper.get_element_preorder_num(title_node)
        if index < s:
            s = index #ToDo:add 2016/03/09
            title_text = ElementHelper.element_text_content(title_node)
    #====================================================================

    body = ElementHelper.get_body(doctree)
    # remove nodes which not belong to main text
    set_text_mark(body, s, t)
    remove_nontext_element(body)

    return body, title_text

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
def cetd_parse(doctree):
    body = ElementHelper.get_body(doctree)
    CountChar(body)
    CountTag(body)
    CountLinkChar(body)
    CountLinkTag(body)
    char_num = float(body.attrib.get(kg_char_num))
    if char_num==0: char_num=1
    linkchar_num = float(body.attrib.get(kg_linkchar_num))
    ratio = linkchar_num/char_num
    ComputeTextDensity(body, ratio)
    ComputeDensitySum(body, ratio)
    max_density_sum = FindMaxDensitySum(body)
    SetMark(body, 0)
    thresold = GetThreshold(body, max_density_sum)
    MarkContent(body, thresold)

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
def get_article_wish(clusters, doctree, title_node=None, debug = False):
    #filter
    clusters = filter_cluster(clusters)

    if len(clusters) == 0:
        return ElementHelper.element_text_content(title_node)

    maxCluster = get_biggest_cluster(clusters)

    nodes = clusters[maxCluster]

    m = {}
    m[maxCluster] = nodes
    filter_cluster(m)

    #============================================
    if debug:
    # for test
        print 'maxCluster: %s' %maxCluster
        for n in nodes:
            print ElementHelper.get_xpath_by_element(n, doctree)

    allnodes = []

    for node in nodes:
        children = ElementHelper.get_children(node)
        allnodes.extend(children)

    allnodes.sort(lambda p,q:ElementHelper.get_element_preorder_num(p)-ElementHelper.get_element_preorder_num(q))
    s = ElementHelper.get_element_preorder_num(allnodes[0])
    t = ElementHelper.get_element_preorder_num(allnodes[-1])
    title_text = ''
    if title_node is not None:
        index = ElementHelper.get_element_preorder_num(title_node)
        if index < s:
            s = index
            title_text = ElementHelper.element_text_content(title_node)

    body = ElementHelper.get_body(doctree)

    set_text_mark(body, s, t)

    remove_nontext_element(body)

    # ElementHelper.print_element(body)

    return ElementHelper.element_text_content_list(body), title_text

# 需要導入模塊: from util import ElementHelper [as 別名]
# 或者: from util.ElementHelper import get_body [as 別名]
    def merger_sibling_record_node(cls, doctree):
        node_record_mapping = {}

        body = ElementHelper.get_body(doctree)
        thislevel = []
        thislevel.extend(body)
        while thislevel:
            nextlevel = list()
            for node in thislevel:
                # correct nodes which
                cls.correct_record_mark(node)

                if cls.is_node_or_ancestor_record(node):
                    first_record_sibling = cls.find_first_sibling_record_node(node, doctree)
                    node_record_mapping.setdefault(first_record_sibling, []).append(node)

            for node in thislevel:
                if len(node) > 0:
                    nextlevel.extend([child for child in node if not cls.is_node_or_ancestor_record(node)])
            thislevel = nextlevel

        return node_record_mapping