add test file

tensnsorflow/multi.py

@@ -192,7 +192,8 @@ def feature_engineer():
           "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," \
           "ot.tag as tag5,sixin.tag as tag6,cart.tag as tag7,doris.search_tag2,doris.search_tag3," \
-          "k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_time " \
+          "k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_time," \
+          "e.device_id,e.cid_id " \
           "from jerry_test.esmm_train_data_dwell e left join jerry_test.user_feature u on e.device_id = u.device_id " \
           "left join jerry_test.cid_type_top c on e.device_id = c.device_id " \
           "left join jerry_test.cid_time_cut cut on e.cid_id = cut.cid " \
@@ -211,66 +212,68 @@ def feature_engineer():
           "left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date " \
           "where e.stat_date >= '{}'".format(start)
 
-    # df = spark.sql(sql)
-    #
-    # df = df.drop_duplicates(["ucity_id", "level2_ids", "ccity_name", "device_type", "manufacturer",
-    #                          "channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids",
-    #                          "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7"])
-    #
-    # df = df.na.fill(dict(zip(features, features)))
-    #
-    # 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", "search_tag2", "search_tag3")\
-    #     .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),
-    #                [value_map.get(x[0], 1), value_map.get(x[13], 2), value_map.get(x[14], 3), value_map.get(x[15], 4),
-    #                 value_map.get(x[16], 5), value_map.get(x[17], 6), value_map.get(x[18], 7), value_map.get(x[19], 8),
-    #                 value_map.get(x[20], 9), value_map.get(x[21], 10),
-    #                 value_map.get(x[22], 11), value_map.get(x[23], 12), value_map.get(x[24], 13),
-    #                 value_map.get(x[25], 14), value_map.get(x[26], 15)],
-    #                app_list_func(x[27], leve2_map), app_list_func(x[28], leve3_map)
-    #                ))
-    #
-    #
-    # rdd.persist(storageLevel= StorageLevel.MEMORY_ONLY_SER)
-    #
-    # # 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.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], x[14], x[15]))
-    # f = time.time()
-    # spark.createDataFrame(train).toDF("y", "z", "app_list", "level2_list", "level3_list",
-    #                                   "tag1_list", "tag2_list", "tag3_list", "tag4_list",
-    #                                   "tag5_list", "tag6_list", "tag7_list", "ids", "search_tag2_list","search_tag3_list") \
-    #     .repartition(1).write.format("tfrecords").save(path=path + "tr/", mode="overwrite")
-    # h = time.time()
-    # print("train tfrecord done")
-    # print((h - f) / 60)
-    #
-    # print("训练集样本总量：")
-    # print(rdd.count())
-    #
-    # get_pre_number()
-    #
-    # test = 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], x[14], x[15]))
-    #
-    # spark.createDataFrame(test).toDF("y", "z", "app_list", "level2_list", "level3_list",
-    #                                  "tag1_list", "tag2_list", "tag3_list", "tag4_list",
-    #                                  "tag5_list", "tag6_list", "tag7_list", "ids", "search_tag2_list","search_tag3_list") \
-    #     .repartition(1).write.format("tfrecords").save(path=path + "va/", mode="overwrite")
-    #
-    # print("va tfrecord done")
-    #
-    # rdd.unpersist()
+    df = spark.sql(sql)
+
+    df = df.drop_duplicates(["ucity_id", "level2_ids", "ccity_name", "device_type", "manufacturer",
+                             "channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids",
+                             "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7"])
+
+    df = df.na.fill(dict(zip(features, features)))
+
+    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", "search_tag2", "search_tag3","cid_id","device_id")\
+        .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),
+                   [value_map.get(x[0], 1), value_map.get(x[13], 2), value_map.get(x[14], 3), value_map.get(x[15], 4),
+                    value_map.get(x[16], 5), value_map.get(x[17], 6), value_map.get(x[18], 7), value_map.get(x[19], 8),
+                    value_map.get(x[20], 9), value_map.get(x[21], 10),
+                    value_map.get(x[22], 11), value_map.get(x[23], 12), value_map.get(x[24], 13),
+                    value_map.get(x[25], 14), value_map.get(x[26], 15)],
+                   app_list_func(x[27], leve2_map), app_list_func(x[28], leve3_map),x[13],x[29],x[30]
+                   ))
+
+
+    rdd.persist(storageLevel= StorageLevel.MEMORY_ONLY_SER)
+
+    # 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.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], x[14], x[15],x[16],x[17],x[18]))
+    f = time.time()
+    spark.createDataFrame(train).toDF("y", "z", "app_list", "level2_list", "level3_list",
+                                      "tag1_list", "tag2_list", "tag3_list", "tag4_list",
+                                      "tag5_list", "tag6_list", "tag7_list", "ids",
+                                      "search_tag2_list","search_tag3_list","city","cid_id","uid") \
+        .repartition(1).write.format("tfrecords").save(path=path + "tr/", mode="overwrite")
+    h = time.time()
+    print("train tfrecord done")
+    print((h - f) / 60)
+
+    print("训练集样本总量：")
+    print(rdd.count())
+
+    get_pre_number()
+
+    test = 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], x[14], x[15],x[16],x[17],x[18]))
+
+    spark.createDataFrame(test).toDF("y", "z", "app_list", "level2_list", "level3_list",
+                                     "tag1_list", "tag2_list", "tag3_list", "tag4_list",
+                                     "tag5_list", "tag6_list", "tag7_list", "ids",
+                                     "search_tag2_list","search_tag3_list","city","cid_id","uid") \
+        .repartition(1).write.format("tfrecords").save(path=path + "va/", mode="overwrite")
+
+    print("va tfrecord done")
+
+    rdd.unpersist()
 
     return validate_date, value_map, app_list_map, leve2_map, leve3_map