utils.py

# encoding = "utf-8"
import pymysql
import pandas as pd
import numpy as np
import redis



# 从Tidb数据库的表里获取数据，并转化成df格式
def con_sql(sql):
    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
    cursor = db.cursor()
    cursor.execute(sql)
    result = cursor.fetchall()
    df = pd.DataFrame(list(result)).dropna()
    db.close()
    return df

# 把数据写到redis里
# TODO 生产环境的redis地址没有提供，下面的地址是测试环境的，需要改成生产环境地址
def add_data_to_redis(key,val):
    r = redis.StrictRedis(host='10.30.50.58', port=6379, db = 12)
    r.set(key, val)
    # 设置key的过期时间，36小时后过期
    r.expire(key,36*60*60)

# ffm 格式转换函数、类
class FFMFormatPandas:
    def __init__(self):
        self.field_index_ = None
        self.feature_index_ = None
        self.y = None

    def fit(self, df, y=None):
        self.y = y
        df_ffm = df[df.columns.difference([self.y])]
        if self.field_index_ is None:
            self.field_index_ = {col: i for i, col in enumerate(df_ffm)}

        if self.feature_index_ is not None:
            last_idx = max(list(self.feature_index_.values()))

        if self.feature_index_ is None:
            self.feature_index_ = dict()
            last_idx = 0

        for col in df.columns:
            vals = df[col].unique()
            for val in vals:
                if pd.isnull(val):
                    continue
                name = '{}_{}'.format(col, val)
                if name not in self.feature_index_:
                    self.feature_index_[name] = last_idx
                    last_idx += 1
            self.feature_index_[col] = last_idx
            last_idx += 1
        return self

    def fit_transform(self, df, y=None):
        self.fit(df, y)
        return self.transform(df)

    def transform_row_(self, row, t):
        ffm = []
        if self.y is not None:
            ffm.append(str(row.loc[row.index == self.y][0]))
        if self.y is None:
            ffm.append(str(0))

        for col, val in row.loc[row.index != self.y].to_dict().items():
            col_type = t[col]
            name = '{}_{}'.format(col, val)
            if col_type.kind == 'O':
                ffm.append('{}:{}:1'.format(self.field_index_[col], self.feature_index_[name]))
            elif col_type.kind == 'i':
                ffm.append('{}:{}:{}'.format(self.field_index_[col], self.feature_index_[col], val))
        return ' '.join(ffm)

    def transform(self, df):
        t = df.dtypes.to_dict()
        return pd.Series({idx: self.transform_row_(row, t) for idx, row in df.iterrows()})
    # 下面这个方法不是这个类原有的方法，是新增的。目的是用来判断这个用户是不是在训练数据集中存在
    def is_feature_index_exist(self, name):
        if name in self.feature_index_:
            return True
        else:
            return False