change test file

tensnsorflow/feature_engineering.py

@@ -19,22 +19,6 @@ def app_list_func(x,l):
     return e
 
 
-def multi_hot(df,column,n):
-    a = time.time()
-    v = list(set(df.select(column).rdd.map(lambda x: x[0]).collect()))
-    b = time.time()
-    print(column)
-    print("cost time 分钟")
-    print((b-a)/60)
-    app_list_value = [str(i).split(",") for i in v]
-    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))))
-    return number,app_list_map
-
 def get_list(db,sql,n):
     cursor = db.cursor()
     cursor.execute(sql)
@@ -61,30 +45,114 @@ def get_map():
     db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select level2_ids from diary_feat"
     b = time.time()
-    leve2_number, leve2_map = get_list(db, sql, apps_number)
+    leve2_number, leve2_map = get_list(db, sql, 1+apps_number)
     print("leve2")
     print((time.time() - b) / 60)
     db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select level3_ids from diary_feat"
     c = time.time()
-    leve3_number, leve3_map = get_list(db, sql, leve2_number)
+    leve3_number, leve3_map = get_list(db, sql, 1+leve2_number+apps_number)
     print((time.time() - c) / 60)
 
+    return apps_number, app_list_map,leve2_number, leve2_map,leve3_number, leve3_map
+
+
+def get_unique(db,sql):
+    cursor = db.cursor()
+    cursor.execute(sql)
+    result = cursor.fetchall()
+    v = list(set([i[0] for i in result]))
+    db.close()
+    print(sql)
+    print(len(v))
+    return v
+
 
+def feature_engineer():
+    apps_number, app_list_map, level2_number, level2_map, level3_number, level3_map = get_map()
 
+    unique_values = []
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct stat_date from esmm_train_data_dur"
+    unique_values.extend(get_unique(db,sql))
 
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct ucity_id from esmm_train_data_dur"
+    unique_values.extend(get_unique(db, sql))
 
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct ccity_name from esmm_train_data_dur"
+    unique_values.extend(get_unique(db, sql))
 
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct time from cid_time_cut"
+    unique_values.extend(get_unique(db, sql))
 
-def feature_engineer():
     db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
-    sql = "select max(stat_date) from esmm_train_data"
+    sql = "select distinct device_type from user_feature"
+    unique_values.extend(get_unique(db, sql))
+
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct manufacturer from user_feature"
+    unique_values.extend(get_unique(db, sql))
+
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct channel from user_feature"
+    unique_values.extend(get_unique(db, sql))
+
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct top from cid_type_top"
+    unique_values.extend(get_unique(db, sql))
+
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct price_min from train_Knowledge_network_data"
+    unique_values.extend(get_unique(db, sql))
+
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct treatment_method from train_Knowledge_network_data"
+    unique_values.extend(get_unique(db, sql))
+
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct price_max from train_Knowledge_network_data"
+    unique_values.extend(get_unique(db, sql))
+
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct treatment_time from train_Knowledge_network_data"
+    unique_values.extend(get_unique(db, sql))
+
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct maintain_time from train_Knowledge_network_data"
+    unique_values.extend(get_unique(db, sql))
+
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select distinct recover_time from train_Knowledge_network_data"
+    unique_values.extend(get_unique(db, sql))
+
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    sql = "select max(stat_date) from esmm_train_data_dur"
     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=300)).strftime("%Y-%m-%d")
     print(start)
 
+    db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC')
+    sql = "select doctor.hospital_id from jerry_test.esmm_train_data_dur e " \
+          "left join eagle.src_zhengxing_api_service service on e.diary_service_id = service.id " \
+          "left join eagle.src_zhengxing_api_doctor doctor on service.doctor_id = doctor.id " \
+          "where e.stat_date >= '{}'".format(start)
+    unique_values.extend(get_unique(db, sql))
+    features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
+                "channel", "top", "time", "stat_date", "hospital_id",
+                "treatment_method", "price_min", "price_max", "treatment_time", "maintain_time", "recover_time"]
+    unique_values.extend(features)
+    print("unique_values length")
+    print(len(unique_values))
+
+    temp = list(range(2 + apps_number + level2_number + level3_number,
+                      2 + apps_number + level2_number + level3_number + len(unique_values)))
+    value_map = dict(zip(unique_values, temp))
+
     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,cut.time,dl.app_list,feat.level3_ids,doctor.hospital_id," \
           "wiki.tag as tag1,question.tag as tag2,search.tag as tag3,budan.tag as tag4," \
@@ -113,36 +181,17 @@ def feature_engineer():
                              "channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids",
                              "tag1","tag2","tag3","tag4","tag5","tag6","tag7"])
 
-    features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
-                "channel", "top", "time", "stat_date", "hospital_id",
-                "treatment_method", "price_min", "price_max", "treatment_time", "maintain_time", "recover_time"]
-
     df = df.na.fill(dict(zip(features, features)))
 
-    apps_number, app_list_map = multi_hot(df, "app_list", 1)
-    level2_number, leve2_map = multi_hot(df, "level2_ids", 1 + apps_number)
-    level3_number, leve3_map = multi_hot(df, "level3_ids", 1 + apps_number + level2_number)
-
-    unique_values = []
-    for i in features:
-        a = time.time()
-        unique_values.extend(list(set(df.select(i).rdd.map(lambda x: x[0]).collect())))
-        b = time.time()
-        print(i)
-        print((b-a)/60)
-    temp = list(range(2 + apps_number + level2_number + level3_number,
-                      2 + apps_number + level2_number + level3_number + len(unique_values)))
-    value_map = dict(zip(unique_values, temp))
-
     c = time.time()
     rdd = df.select("stat_date","y", "z","app_list","level2_ids","level3_ids",
                     "tag1","tag2","tag3","tag4","tag5","tag6","tag7",
                      "ucity_id", "ccity_name","device_type", "manufacturer", "channel", "top", "time",
                     "hospital_id","treatment_method", "price_min", "price_max", "treatment_time",
                     "maintain_time","recover_time").rdd.repartition(200).map(lambda x: (x[0],float(x[1]),float(x[2]),app_list_func(x[3], app_list_map), app_list_func(x[4], leve2_map),
-                        app_list_func(x[5], leve3_map), app_list_func(x[6], leve2_map),app_list_func(x[7], leve2_map),
-                        app_list_func(x[8], leve2_map), app_list_func(x[9], leve2_map),app_list_func(x[10], leve2_map),
-                        app_list_func(x[11], leve2_map),app_list_func(x[12], leve2_map),
+                        app_list_func(x[5], level3_map), app_list_func(x[6], level2_map),app_list_func(x[7], level2_map),
+                        app_list_func(x[8], level2_map), app_list_func(x[9], level2_map),app_list_func(x[10], level2_map),
+                        app_list_func(x[11], level2_map),app_list_func(x[12], level2_map),
                         [value_map[x[0]], value_map[x[13]],value_map[x[14]], value_map[x[15]], value_map[x[16]],
                          value_map[x[17]],value_map[x[18]], value_map[x[19]], value_map[x[20]],value_map[x[21]],
                          value_map[x[22]], value_map[x[23]], value_map[x[24]],value_map[x[25]],value_map[x[26]]]))
@@ -151,14 +200,6 @@ def feature_engineer():
     print((d-c)/60)
     rdd.persist()
     # TODO 上线后把下面train fliter 删除，因为最近一天的数据也要作为训练集
-    # train = rdd.filter(lambda x: x[0] != validate_date) \
-    #     .map(lambda x: (float(x[1]),float(x[2]),app_list_func(x[3], app_list_map), app_list_func(x[4], leve2_map),
-    #                     app_list_func(x[5], leve3_map), app_list_func(x[6], leve2_map),app_list_func(x[7], leve2_map),
-    #                     app_list_func(x[8], leve2_map), app_list_func(x[9], leve2_map),app_list_func(x[10], leve2_map),
-    #                     app_list_func(x[11], leve2_map),app_list_func(x[12], leve2_map),
-    #                     [value_map[x[0]], value_map[x[13]],value_map[x[14]], value_map[x[15]], value_map[x[16]],
-    #                      value_map[x[17]],value_map[x[18]], value_map[x[19]], value_map[x[20]],value_map[x[21]],
-    #                      value_map[x[22]], value_map[x[23]], value_map[x[24]],value_map[x[25]],value_map[x[26]]]))
 
     train = rdd.filter(lambda x: x[0] != validate_date).map(lambda x:(x[1],x[2],x[3],x[4],x[5],x[6],x[7],x[8],x[9],
                                                                       x[10],x[11],x[12],x[13]))
@@ -183,10 +224,10 @@ def feature_engineer():
 
     rdd.unpersist()
 
-    return validate_date,value_map,app_list_map,leve2_map,leve3_map
+    return validate_date,value_map,app_list_map,level2_map,level3_map
 
 
-def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
+def get_predict():
     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," \
@@ -222,12 +263,12 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
             "hospital_id", "treatment_method", "price_min", "price_max", "treatment_time",
             "maintain_time", "recover_time") \
         .rdd.repartition(200).map(lambda x: (x[0],float(x[1]),float(x[2]),x[3],x[4],x[5],
-                            app_list_func(x[6], app_list_map),app_list_func(x[7], leve2_map),
-                            app_list_func(x[8], leve3_map), app_list_func(x[9], leve2_map),
-                            app_list_func(x[10], leve2_map),app_list_func(x[11], leve2_map),
-                            app_list_func(x[12], leve2_map), app_list_func(x[13], leve2_map),
-                            app_list_func(x[14], leve2_map), app_list_func(x[15], leve2_map),
-                           [value_map.get(date, 299999),value_map.get(x[16], 299998),
+                            app_list_func(x[6], app_list_map),app_list_func(x[7], level2_map),
+                            app_list_func(x[8], level3_map), app_list_func(x[9], level2_map),
+                            app_list_func(x[10], level2_map),app_list_func(x[11], level2_map),
+                            app_list_func(x[12], level2_map), app_list_func(x[13], level2_map),
+                            app_list_func(x[14], level2_map), app_list_func(x[15], level2_map),
+                           [value_map.get(validate_date, 299999),value_map.get(x[16], 299998),
                             value_map.get(x[17], 299997),value_map.get(x[18], 299996),
                             value_map.get(x[19], 299995), value_map.get(x[20], 299994),
                             value_map.get(x[21], 299993), value_map.get(x[22], 299992),
@@ -286,25 +327,25 @@ def con_sql(db,sql):
 
 
 if __name__ == '__main__':
-    # sparkConf = SparkConf().set("spark.hive.mapred.supports.subdirectories", "true") \
-    #     .set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive", "true") \
-    #     .set("spark.tispark.plan.allow_index_double_read", "false") \
-    #     .set("spark.tispark.plan.allow_index_read", "true") \
-    #     .set("spark.sql.extensions", "org.apache.spark.sql.TiExtensions") \
-    #     .set("spark.tispark.pd.addresses", "172.16.40.158:2379").set("spark.io.compression.codec", "lzf")\
-    #     .set("spark.driver.maxResultSize", "8g").set("spark.sql.avro.compression.codec","snappy")
-    #
-    # spark = SparkSession.builder.config(conf=sparkConf).enableHiveSupport().getOrCreate()
-    # ti = pti.TiContext(spark)
-    # ti.tidbMapDatabase("jerry_test")
-    # ti.tidbMapDatabase("eagle")
-    # spark.sparkContext.setLogLevel("WARN")
-    # path = "hdfs:///strategy/esmm/"
-    # local_path = "/home/gmuser/esmm/"
-    #
-    # validate_date, value_map, app_list_map, leve2_map, leve3_map = feature_engineer()
-    # get_predict(validate_date, value_map, app_list_map, leve2_map, leve3_map)
-    get_map()
+    sparkConf = SparkConf().set("spark.hive.mapred.supports.subdirectories", "true") \
+        .set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive", "true") \
+        .set("spark.tispark.plan.allow_index_double_read", "false") \
+        .set("spark.tispark.plan.allow_index_read", "true") \
+        .set("spark.sql.extensions", "org.apache.spark.sql.TiExtensions") \
+        .set("spark.tispark.pd.addresses", "172.16.40.158:2379").set("spark.io.compression.codec", "lzf")\
+        .set("spark.driver.maxResultSize", "8g").set("spark.sql.avro.compression.codec","snappy")
+
+    spark = SparkSession.builder.config(conf=sparkConf).enableHiveSupport().getOrCreate()
+    ti = pti.TiContext(spark)
+    ti.tidbMapDatabase("jerry_test")
+    ti.tidbMapDatabase("eagle")
+    spark.sparkContext.setLogLevel("WARN")
+    path = "hdfs:///strategy/esmm/"
+    local_path = "/home/gmuser/esmm/"
+
+    validate_date, value_map, app_list_map, level2_map, level3_map = feature_engineer()
+    get_predict()
+