add feature file

tensnsorflow/es/feature.py

+
+import pandas as pd
+import pymysql
+import datetime
+
+
+def con_sql(db,sql):
+    cursor = db.cursor()
+    cursor.execute(sql)
+    result = cursor.fetchall()
+    df = pd.DataFrame(list(result))
+    db.close()
+    return df
+
+
+def multi_hot(df,column,n):
+    df[column] = df[column].fillna("lost_na")
+    app_list_value = [i.split(",") for i in df[column].unique()]
+    app_list_unique = []
+    for i in app_list_value:
+        app_list_unique.extend(i)
+    app_list_unique = list(set(app_list_unique))
+    number = len(app_list_unique)
+    app_list_map = dict(zip(app_list_unique, list(range(n, number + n))))
+    df[column] = df[column].apply(app_list_func, args=(app_list_map,))
+    return number,app_list_map
+
+
+def get_data():
+    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select max(stat_date) from {}".format(train_data_set)
+    validate_date = con_sql(db, sql)[0].values.tolist()[0]
+    print("validate_date:" + validate_date)
+    temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
+    start = (temp - datetime.timedelta(days=10)).strftime("%Y-%m-%d")
+    print(start)
+    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select e.y,e.z,e.stat_date,e.ucity_id,feat.level2_ids,e.ccity_name,u.device_type,u.manufacturer," \
+          "u.channel,c.top,e.device_id,cut.time,dl.app_list,e.diary_service_id,feat.level3_ids,feat.level2 " \
+          "from {} e left join user_feature u on e.device_id = u.device_id " \
+          "left join cid_type_top c on e.device_id = c.device_id " \
+          "left join cid_time_cut cut on e.cid_id = cut.cid " \
+          "left join device_app_list dl on e.device_id = dl.device_id " \
+          "left join diary_feat feat on e.cid_id = feat.diary_id " \
+          "where e.stat_date >= '{}'".format(train_data_set,start)
+    df = con_sql(db, sql)
+
+    df = df.rename(columns={0: "y", 1: "z", 2: "stat_date", 3: "ucity_id", 4: "clevel2_id", 5: "ccity_name",
+                            6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "device_id",
+                            11: "time",12:"app_list",13:"service_id",14:"level3_ids",15:"level2"})
+    print("esmm data ok")
+
+    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select level2_id,treatment_method,price_min,price_max,treatment_time,maintain_time,recover_time " \
+          "from train_Knowledge_network_data"
+    knowledge = con_sql(db, sql)
+    knowledge = knowledge.rename(columns={0: "level2", 1: "method", 2: "min", 3: "max",
+                                          4: "treatment_time", 5: "maintain_time",6: "recover_time"})
+    knowledge["level2"] = knowledge["level2"].astype("str")
+
+    df = pd.merge(df, knowledge, on='level2', how='left')
+    df = df.drop("level2", axis=1)
+
+    service_id = tuple(df["service_id"].unique())
+    db = pymysql.connect(host='rdsfewzdmf0jfjp9un8xj.mysql.rds.aliyuncs.com', port=3306, user='work',
+                         passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
+    sql = "select s.id,d.hospital_id from api_service s left join api_doctor d on s.doctor_id = d.id " \
+          "where s.id in {}".format(service_id)
+    hospital = con_sql(db, sql)
+    hospital = hospital.rename(columns={0: "service_id", 1: "hospital_id"})
+    # print(hospital.head())
+    # print("hospital")
+    # print(hospital.count())
+    hospital["service_id"] = hospital["service_id"].astype("str")
+    df = pd.merge(df, hospital, on='service_id', how='left')
+    df = df.drop("service_id", axis=1)
+
+    print(df.count())
+    print("before")
+    print(df.shape)
+
+    df = df.drop_duplicates(["ucity_id", "clevel2_id", "ccity_name", "device_type", "manufacturer",
+                             "channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids"])
+    print("去重后样本数量:",df.shape)
+
+    app_list_number,app_list_map = multi_hot(df,"app_list",2)
+    level2_number,level2_map = multi_hot(df,"clevel2_id",2+app_list_number)
+    level3_number,level3_map = multi_hot(df, "level3_ids", 2 + app_list_number + level2_number)
+
+    unique_values = []
+    features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
+                "channel", "top", "time", "stat_date","hospital_id",
+                "method", "min", "max", "treatment_time", "maintain_time", "recover_time"]
+    for i in features:
+        df[i] = df[i].astype("str")
+        df[i] = df[i].fillna("lost")
+        # 下面这行代码是为了区分不同的列中有相同的值
+        df[i] = df[i] + i
+        unique_values.extend(list(df[i].unique()))
+
+    temp = list(range(2+app_list_number+level2_number + level3_number, 2 + app_list_number+level2_number + level3_number + len(unique_values)))
+    value_map = dict(zip(unique_values,temp))
+
+    df = df.drop("device_id", axis=1)
+    train = df[df["stat_date"] != validate_date+"stat_date"]
+    test = df[df["stat_date"] == validate_date+"stat_date"]
+    for i in ["ucity_id", "ccity_name", "device_type", "manufacturer",
+                "channel", "top", "time", "stat_date","hospital_id",
+              "method", "min", "max", "treatment_time", "maintain_time", "recover_time"]:
+        train[i] = train[i].map(value_map)
+        test[i] = test[i].map(value_map)
+
+    print("train shape")
+    print(train.shape)
+    print("test shape")
+    print(test.shape)
+
+    write_csv(train, "tr",100000)
+    write_csv(test, "va",80000)
+
+    return validate_date,value_map,app_list_map,level2_map,level3_map
+
+
+def app_list_func(x,l):
+    b = x.split(",")
+    e = []
+    for i in b:
+        if i in l.keys():
+            e.append(l[i])
+        else:
+            e.append(0)
+    return ",".join([str(j) for j in e])
+
+
+def write_csv(df,name,n):
+    for i in range(0, df.shape[0], n):
+        if i == 0:
+            temp = df.iloc[0:n]
+        elif i + n > df.shape[0]:
+            temp = df.iloc[i:]
+        else:
+            temp = df.iloc[i:i + n]
+        temp.to_csv(path + name+ "/{}_{}.csv".format(name,i), index=False)
+
+
+def get_predict(date,value_map,app_list_map,level2_map,level3_map):
+    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select e.y,e.z,e.label,e.ucity_id,feat.level2_ids,e.ccity_name," \
+          "u.device_type,u.manufacturer,u.channel,c.top,e.device_id,e.cid_id,cut.time," \
+          "dl.app_list,e.hospital_id,feat.level3_ids,feat.level2 " \
+          "from esmm_pre_data e left join user_feature u on e.device_id = u.device_id " \
+          "left join cid_type_top c on e.device_id = c.device_id " \
+          "left join cid_time_cut cut on e.cid_id = cut.cid " \
+          "left join device_app_list dl on e.device_id = dl.device_id " \
+          "left join diary_feat feat on e.cid_id = feat.diary_id"
+    df = con_sql(db, sql)
+    df = df.rename(columns={0: "y", 1: "z", 2: "label", 3: "ucity_id", 4: "clevel2_id", 5: "ccity_name",
+                            6: "device_type", 7: "manufacturer", 8: "channel", 9: "top",10: "device_id",
+                            11: "cid_id", 12: "time",13:"app_list",14:"hospital_id",15:"level3_ids",
+                            16: "level2"})
+
+    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select level2_id,treatment_method,price_min,price_max,treatment_time,maintain_time,recover_time " \
+          "from train_Knowledge_network_data"
+    knowledge = con_sql(db, sql)
+    knowledge = knowledge.rename(columns={0: "level2", 1: "method", 2: "min", 3: "max",
+                                          4: "treatment_time", 5: "maintain_time", 6: "recover_time"})
+    knowledge["level2"] = knowledge["level2"].astype("str")
+
+    df = pd.merge(df, knowledge, on='level2', how='left')
+    df = df.drop("level2", axis=1)
+    df = df.drop_duplicates(["ucity_id", "clevel2_id", "ccity_name", "device_type", "manufacturer",
+                             "channel", "top", "time", "app_list", "hospital_id", "level3_ids"])
+
+
+    df["stat_date"] = date
+    print(df.head(6))
+    df["app_list"] = df["app_list"].fillna("lost_na")
+    df["app_list"] = df["app_list"].apply(app_list_func,args=(app_list_map,))
+    df["clevel2_id"] = df["clevel2_id"].fillna("lost_na")
+    df["clevel2_id"] = df["clevel2_id"].apply(app_list_func, args=(level2_map,))
+    df["level3_ids"] = df["level3_ids"].fillna("lost_na")
+    df["level3_ids"] = df["level3_ids"].apply(app_list_func, args=(level3_map,))
+
+    # print("predict shape")
+    # print(df.shape)
+    df["uid"] = df["device_id"]
+    df["city"] = df["ucity_id"]
+    features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
+                "channel", "top", "time", "stat_date","hospital_id",
+                "method", "min", "max", "treatment_time", "maintain_time", "recover_time"]
+    for i in features:
+        df[i] = df[i].astype("str")
+        df[i] = df[i].fillna("lost")
+        df[i] = df[i] + i
+
+    native_pre = df[df["label"] == 0]
+    native_pre = native_pre.drop("label", axis=1)
+    nearby_pre = df[df["label"] == 1]
+    nearby_pre = nearby_pre.drop("label", axis=1)
+
+    for i in ["ucity_id", "ccity_name", "device_type", "manufacturer",
+                "channel", "top", "time", "stat_date","hospital_id",
+              "method", "min", "max", "treatment_time", "maintain_time", "recover_time"]:
+        native_pre[i] = native_pre[i].map(value_map)
+        # TODO 没有覆盖到的类别会处理成na，暂时用0填充，后续完善一下
+        native_pre[i] = native_pre[i].fillna(0)
+
+        nearby_pre[i] = nearby_pre[i].map(value_map)
+        # TODO 没有覆盖到的类别会处理成na，暂时用0填充，后续完善一下
+        nearby_pre[i] = nearby_pre[i].fillna(0)
+
+    print("native")
+    print(native_pre.shape)
+
+    native_pre[["uid","city","cid_id"]].to_csv(path+"native.csv",index=False)
+    write_csv(native_pre, "native",200000)
+
+    print("nearby")
+    print(nearby_pre.shape)
+
+    nearby_pre[["uid","city","cid_id"]].to_csv(path+"nearby.csv",index=False)
+    write_csv(nearby_pre, "nearby", 160000)
+
+
+if __name__ == '__main__':
+    train_data_set = "esmm_train_data"
+    path = "/data/esmm/"
+    date,value,app_list,level2,level3 = get_data()
+    get_predict(date, value,app_list,level2,level3)
+