from harvesttext import HarvestText
ht = HarvestText()

para = "上港的武磊和恒大的郜林，谁是**最好的前锋？那当然是武磊武球王了，他是射手榜第一，原来是弱点的单刀也有了进步"
entity_mention_dict = {'武磊':['武磊','武球王'],'郜林':['郜林','郜飞机'],'前锋':['前锋'],'上海上港':['上港'],'广州恒大':['恒大'],'单刀球':['单刀']}
entity_type_dict = {'武磊':'球员','郜林':'球员','前锋':'位置','上海上港':'球队','广州恒大':'球队','单刀球':'术语'}
ht.add_entities(entity_mention_dict,entity_type_dict)
print("\nSentence segmentation")
print(ht.seg(para,return_sent=True))    # return_sent=False时，则返回词语列表

print("\nPOS tagging with entity types")
for word, flag in ht.posseg(para):
	print("%s:%s" % (word, flag),end = " ")

for span, entity in ht.entity_linking(para):
	print(span, entity)

print(ht.cut_sentences(para))

ht0 = HarvestText()
sent = "上海上港足球队的武磊是**最好的前锋。"
print(ht0.named_entity_recognition(sent))

ht0 = HarvestText()
para = "上港的武磊武球王是**最好的前锋。"
entity_mention_dict = {'武磊': ['武磊', '武球王'], "上海上港":["上港"]}
entity_type_dict = {'武磊': '球员', "上海上港":"球队"}
ht0.add_entities(entity_mention_dict, entity_type_dict)
for arc in ht0.dependency_parse(para):
    print(arc)
print(ht0.triple_extraction(para))

print(ht0.triple_extraction(para))

def entity_error_check():
    ht0 = HarvestText()
    typed_words = {"人名":["武磊"]}
    ht0.add_typed_words(typed_words)
    sent1 = "武磊和吴力只差一个拼音"
    print(sent1)
    print(ht0.entity_linking(sent1, pinyin_recheck=True))
    sent2 = "武磊和吴磊只差一个字"
    print(sent2)
    print(ht0.entity_linking(sent2, char_recheck=True))
    sent3 = "吴磊和吴力都可能是武磊的代称"
    print(sent3)
    print(ht0.get_linking_mention_candidates(sent3, pinyin_recheck=True, char_recheck=True))
entity_error_check()

print("\nsentiment dictionary")
sents = ["武磊威武，中超第一射手！",
      "武磊强，中超最第一本土球员！",
      "郜林不行，只会抱怨的球员注定上限了",
      "郜林看来不行，已经到上限了"]
sent_dict = ht.build_sent_dict(sents,min_times=1,pos_seeds=["第一"],neg_seeds=["不行"])
print("%s:%f" % ("威武",sent_dict["威武"]))
print("%s:%f" % ("球员",sent_dict["球员"]))
print("%s:%f" % ("上限",sent_dict["上限"]))

print("\nsentence sentiment")
sent = "武球王威武，中超最强球员！"
print("%f:%s" % (ht.analyse_sent(sent),sent))

print("\nsentiment dictionary using default seed words")
docs = ["张市筹设兴华实业公司外区资本家踊跃投资晋察冀边区兴华实业公司，自筹备成立以来，解放区内外企业界人士及一般商民，均踊跃认股投资",
        "打倒万恶的资本家",
    "该公司原定资本总额为二十五万万元，现已由各界分认达二十万万元，所属各厂、各公司亦募得股金一万万余元",
    "连日来解放区以外各工商人士，投函向该公司询问经营性质与范围以及股东权限等问题者甚多，络绎抵此的许多资本家，于参观该公司所属各厂经营状况后，对**政府扶助与奖励私营企业发展的政策，均极表赞同，有些资本家因款项未能即刻汇来，多向筹备处预认投资的额数。由平津来张的林明棋先生，一次即以现款入股六十余万元"
   ]
# scale: 将所有词语的情感值范围调整到[-1,1]
# 省略pos_seeds, neg_seeds,将采用默认的情感词典 get_qh_sent_dict()
print("scale=\"0-1\", 按照最大为1，最小为0进行线性伸缩，0.5未必是中性")
sent_dict = ht.build_sent_dict(docs,min_times=1,scale="0-1")
print("%s:%f" % ("赞同",sent_dict["赞同"]))
print("%s:%f" % ("二十万",sent_dict["二十万"]))
print("%s:%f" % ("万恶",sent_dict["万恶"]))
print("%f:%s" % (ht.analyse_sent(docs[0]), docs[0]))
print("%f:%s" % (ht.analyse_sent(docs[1]), docs[1]))

print("scale=\"+-1\", 在正负区间内分别伸缩，保留0作为中性的语义")
sent_dict = ht.build_sent_dict(docs,min_times=1,scale="+-1")
print("%s:%f" % ("赞同",sent_dict["赞同"]))
print("%s:%f" % ("二十万",sent_dict["二十万"]))
print("%s:%f" % ("万恶",sent_dict["万恶"]))
print("%f:%s" % (ht.analyse_sent(docs[0]), docs[0]))
print("%f:%s" % (ht.analyse_sent(docs[1]), docs[1]))

docs = ["武磊威武，中超第一射手！",
		"郜林看来不行，已经到上限了。",
		"武球王威武，中超最强前锋！",
		"武磊和郜林，谁是**最好的前锋？"]
inv_index = ht.build_index(docs)
print(ht.get_entity_counts(docs, inv_index))  # 获得文档中所有实体的出现次数
# {'武磊': 3, '郜林': 2, '前锋': 2}

print(ht.search_entity("武磊", docs, inv_index))  # 单实体查找
# ['武磊威武，中超第一射手！', '武球王威武，中超最强前锋！', '武磊和郜林，谁是**最好的前锋？']

print(ht.search_entity("武磊 郜林", docs, inv_index))  # 多实体共现
# ['武磊和郜林，谁是**最好的前锋？']

# 谁是最被人们热议的前锋？用这里的接口可以很简便地回答这个问题
subdocs = ht.search_entity("#球员# 前锋", docs, inv_index)
print(subdocs)  # 实体、实体类型混合查找
# ['武球王威武，中超最强前锋！', '武磊和郜林，谁是**最好的前锋？']
inv_index2 = ht.build_index(subdocs)
print(ht.get_entity_counts(subdocs, inv_index2, used_type=["球员"]))  # 可以限定类型
# {'武磊': 2, '郜林': 1}

# 在现有实体库的基础上随时新增，比如从新词发现中得到的漏网之鱼
ht.add_new_entity("颜骏凌", "颜骏凌", "球员")
docs = ["武磊和颜骏凌是队友",
		"武磊和郜林都是国内顶尖前锋"]
G = ht.build_entity_graph(docs)
print(dict(G.edges.items()))
G = ht.build_entity_graph(docs, used_types=["球员"])
print(dict(G.edges.items()))

entity_mention_dict, entity_type_dict = get_sanguo_entity_dict()
ht0.add_entities(entity_mention_dict, entity_type_dict)
sanguo1 = get_sanguo()[0]
stopwords = get_baidu_stopwords()
docs = ht0.cut_sentences(sanguo1)
G = ht0.build_word_ego_graph(docs,"刘备",min_freq=3,other_min_freq=2,stopwords=stopwords)

print("\nText summarization")
docs = ["武磊威武，中超第一射手！",
		"郜林看来不行，已经到上限了。",
		"武球王威武，中超最强前锋！",
		"武磊和郜林，谁是**最好的前锋？"]
for doc in ht.get_summary(docs, topK=2):
	print(doc)
# 武球王威武，中超最强前锋！
# 武磊威武，中超第一射手！	

def load_resources():
	from harvesttext.resources import get_qh_sent_dict,get_baidu_stopwords,get_sanguo,get_sanguo_entity_dict
    sdict = get_qh_sent_dict()              # {"pos":[积极词...],"neg":[消极词...]}
    print("pos_words:",list(sdict["pos"])[10:15])
    print("neg_words:",list(sdict["neg"])[5:10])
    
    stopwords = get_baidu_stopwords()
    print("stopwords:", list(stopwords)[5:10])

    docs = get_sanguo()                 # 文本列表，每个元素为一章的文本
    print("三国演义最后一章末16字:\n",docs[-1][-16:])
    entity_mention_dict, entity_type_dict = get_sanguo_entity_dict()
    print("刘备 指称：",entity_mention_dict["刘备"])
    print("刘备 类别：",entity_type_dict["刘备"])
    print("蜀 类别：", entity_type_dict["蜀"])
    print("益州 类别：", entity_type_dict["益州"])
load_resources()

def using_typed_words():
    from harvesttext.resources import get_qh_typed_words,get_baidu_stopwords
    ht0 = HarvestText()
    typed_words, stopwords = get_qh_typed_words(), get_baidu_stopwords()
    ht0.add_typed_words(typed_words)
    sentence = "THUOCL是自然语言处理的一套中文词库，词表来自主流网站的社会标签、搜索热词、输入法词库等。"
    print(sentence)
    print(ht0.posseg(sentence,stopwords=stopwords))
using_typed_words()

para = "上港的武磊和恒大的郜林，谁是**最好的前锋？那当然是武磊武球王了，他是射手榜第一，原来是弱点的单刀也有了进步"
#返回关于新词质量的一系列信息，允许手工改进筛选(pd.DataFrame型)
new_words_info = ht.word_discover(para)
#new_words_info = ht.word_discover(para, threshold_seeds=["武磊"])  
new_words = new_words_info.index.tolist()
print(new_words)

def new_word_register():
    new_words = ["落叶球","666"]
    ht.add_new_words(new_words)   # 作为广义上的"新词"登录
    ht.add_new_entity("落叶球", mention0="落叶球", type0="术语")  # 作为特定类型登录
    print(ht.seg("这个落叶球踢得真是666", return_sent=True))
    for word, flag in ht.posseg("这个落叶球踢得真是666"):
        print("%s:%s" % (word, flag), end=" ")

# find_with_rules()
from harvesttext.match_patterns import UpperFirst, AllEnglish, Contains, StartsWith, EndsWith
text0 = "我喜欢Python，因为requests库很适合爬虫"
ht0 = HarvestText()

found_entities = ht0.find_entity_with_rule(text0, rulesets=[AllEnglish()], type0="英文名")
print(found_entities)
print(ht0.posseg(text0))

from harvesttext import loadHT,saveHT
para = "上港的武磊和恒大的郜林，谁是**最好的前锋？那当然是武磊武球王了，他是射手榜第一，原来是弱点的单刀也有了进步"
saveHT(ht,"ht_model1")
ht2 = loadHT("ht_model1")

# 消除记录
ht2.clear()
print("cut with cleared model")
print(ht2.seg(para))

QA = NaiveKGQA(SVOs, entity_type_dict=entity_type_dict)
questions = ["你好","孙中山干了什么事？","谁发动了什么？","清政府签订了哪些条约？",
			 "英国与鸦片战争的关系是什么？","谁复辟了帝制？"]
for question0 in questions:
	print("问："+question0)
	print("答："+QA.answer(question0))