robotIdentificationTopic/text_analysis/src.py

#coding:utf8
import joblib
#accountName:johnsonleung
def predict():
    raw_data = {"user_file":{"accountId": "39234393", "accountName": "hello", "nickName": "Johnson Leung", "fansCount": 308,"likeCount": 92707,"postCount": 14237, "otherInfo": "{\"\"otherInfo\"\":\"\"{\"\"bio\"\": \"\"Huge}", "authentication": 0},
                "post_file":{"count":1,"LikeCount":12,"CommentsCount":1,"ShareCount":1,"length":150,"tags":0,"https":0,"at":0,"diffdate":1}}
    '''
    需要计算的入参
    1.count:帖子总数量
    2.LikeCount：帖子点赞数的平均值
    3.CommentsCount：帖子评论数的平均值
    4.ShareCount：帖子分享数的平均值
    5.length：帖子文本长度的平均值
    6.tags：帖子文本中包含“#”数量的平均值
    7.https：帖子文本中包含“https”数量的平均值
    8.at：帖子文本中包含“@”数量的平均值
    9.diffdate:全部帖子的最小值(帖子A发表时间和抓取时间的最大值-A的发表时间)
    '''
    #用户数据
    user_data=[]
    try:
        user_data_otherInfo_1 = 0 if raw_data["user_file"]["otherInfo"].strip() == "" else 1
    except:
        user_data_otherInfo_1=0
    try:
        user_data_nickName_2 = 0 if raw_data["user_file"]["nickName"].strip() == "" else 1
    except:
        user_data_nickName_2=0
    try:
        user_data_fansCount_3 = int(raw_data["user_file"]["fansCount"])
    except:
        user_data_fansCount_3=0
    try:
        user_data_likeCount_4=int(raw_data["user_file"]["likeCount"])
    except:
        user_data_likeCount_4=0
    try:
        user_data_postCount_5=int(raw_data["user_file"]["postCount"])
    except:
        user_data_postCount_5=0
    try:
        user_data_authentication_6=int(raw_data["user_file"]["authentication"])
    except:
        user_data_authentication_6=0
    user_data.extend([user_data_otherInfo_1,user_data_nickName_2,user_data_fansCount_3,user_data_likeCount_4,user_data_postCount_5,user_data_authentication_6])
    #帖子数据
    post_data=[]
    try:
        post_data_count_1 = int(raw_data["post_file"]["count"])
    except:
        post_data_count_1=0
    try:
        post_data_LikeCount_2 = int(raw_data["post_file"]["LikeCount"])
    except:
        post_data_LikeCount_2=0
    try:
        post_data_CommentsCount_3 = int(raw_data["post_file"]["CommentsCount"])
    except:
        post_data_CommentsCount_3=0
    try:
        post_data_ShareCount_4 = int(raw_data["post_file"]["ShareCount"])
    except:
        post_data_ShareCount_4=0
    try:
        post_data_length_5 = int(raw_data["post_file"]["length"])
    except:
        post_data_length_5=0
    try:
        post_data_tags_6 = int(raw_data["post_file"]["tags"])
    except:
        post_data_tags_6=0
    try:
        post_data_https_7 = int(raw_data["post_file"]["https"])
    except:
        post_data_https_7=0
    try:
        post_data_at_8 = int(raw_data["post_file"]["at"])
    except:
        post_data_at_8=0
    try:
        post_data_diffdate_9 = int(raw_data["post_file"]["diffdate"])
    except:
        post_data_diffdate_9=0
    post_data.extend([post_data_count_1,post_data_LikeCount_2,post_data_CommentsCount_3,post_data_ShareCount_4,post_data_length_5,post_data_tags_6,post_data_https_7,post_data_at_8,post_data_diffdate_9])
    features=[user_data+post_data]
    bot_user = joblib.load("model/bot_user.pkl")  # 加载训练好的模型
    result=bot_user.predict(features)
    print(result)
    # 参数顺序[['otherInfo', 'nickName', 'fansCount', 'likeCount','postCount', 'authentication', 'count', 'LikeCount', 'CommentsCount', 'ShareCount','length', 'tags', 'https', 'at', 'diffdate']]
predict()