dist predict test

eda/esmm/Model_pipline/dist_predict.py

 import tensorflow as tf
 import pymysql
 from pyspark.conf import SparkConf
-import pytispark.pytispark as pti
 from pyspark.sql import SparkSession
 import datetime
 import pandas as pd
 from datetime import date, timedelta
 import time
-from pyspark import StorageLevel
 from pyspark.sql import Row
-import os
 import sys
 from sqlalchemy import create_engine
 
@@ -30,7 +27,8 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
             "tag5_list": tf.VarLenFeature(tf.int64),
             "tag6_list": tf.VarLenFeature(tf.int64),
             "tag7_list": tf.VarLenFeature(tf.int64),
-            "number": tf.VarLenFeature(tf.int64),
+            "search_tag2_list": tf.VarLenFeature(tf.int64),
+            "search_tag3_list": tf.VarLenFeature(tf.int64),
             "uid": tf.VarLenFeature(tf.string),
             "city": tf.VarLenFeature(tf.string),
             "cid_id": tf.VarLenFeature(tf.string)
@@ -40,26 +38,24 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
         z = parsed.pop('z')
         return parsed, {"y": y, "z": z}
 
-    # Extract lines from input files using the Dataset API, can pass one filename or filename list
-    dataset = tf.data.TFRecordDataset(filenames).map(_parse_fn, num_parallel_calls=10).prefetch(500000)    # multi-thread pre-process then prefetch
 
-    # Randomizes input using a window of 256 elements (read into memory)
-    if perform_shuffle:
-        dataset = dataset.shuffle(buffer_size=256)
+    files = tf.data.Dataset.list_files(filenames)
+    dataset = files.apply(
+        tf.data.experimental.parallel_interleave(
+            lambda  file: tf.data.TFRecordDataset(file),
+            cycle_length=8
+        )
+    )
 
-    # epochs from blending together.
-    dataset = dataset.repeat(num_epochs)
-    dataset = dataset.batch(batch_size) # Batch size to use
-    # dataset = dataset.padded_batch(batch_size, padded_shapes=({"feeds_ids": [None], "feeds_vals": [None], "title_ids": [None]}, [None]))   #不定长补齐
+    dataset = dataset.apply(tf.data.experimental.map_and_batch(map_func=_parse_fn, batch_size=batch_size, num_parallel_calls=8))
+    dataset = dataset.prefetch(10000)
 
-    #return dataset.make_one_shot_iterator()
     iterator = dataset.make_one_shot_iterator()
     batch_features, batch_labels = iterator.get_next()
-    #return tf.reshape(batch_ids,shape=[-1,field_size]), tf.reshape(batch_vals,shape=[-1,field_size]), batch_labels
-    #print("-"*100)
-    #print(batch_features,batch_labels)
+
     return batch_features, batch_labels
 
+
 def model_fn(features, labels, mode, params):
     """Bulid Model function f(x) for Estimator."""
     #------hyperparameters----
@@ -88,7 +84,8 @@ def model_fn(features, labels, mode, params):
     tag5_list = features['tag5_list']
     tag6_list = features['tag6_list']
     tag7_list = features['tag7_list']
-    number = features['number']
+    search_tag2_list = features['search_tag2_list']
+    search_tag3_list = features['search_tag3_list']
     uid = features['uid']
     city = features['city']
     cid_id = features['cid_id']
@@ -107,12 +104,14 @@ def model_fn(features, labels, mode, params):
         tag5 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag5_list, sp_weights=None, combiner="sum")
         tag6 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag6_list, sp_weights=None, combiner="sum")
         tag7 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag7_list, sp_weights=None, combiner="sum")
+        search_tag2 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=search_tag2_list, sp_weights=None, combiner="sum")
+        search_tag3 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=search_tag3_list, sp_weights=None, combiner="sum")
 
         # x_concat = tf.reshape(embedding_id,shape=[-1, common_dims])  # None * (F * K)
         x_concat = tf.concat([tf.reshape(embedding_id, shape=[-1, common_dims]), app_id, level2, level3, tag1,
-                              tag2, tag3, tag4, tag5, tag6, tag7], axis=1)
+                              tag2, tag3, tag4, tag5, tag6, tag7,search_tag2,search_tag3], axis=1)
+
 
-        sample_id = tf.sparse.to_dense(number)
         uid = tf.sparse.to_dense(uid,default_value="")
         city = tf.sparse.to_dense(city,default_value="")
         cid_id = tf.sparse.to_dense(cid_id,default_value="")
@@ -149,7 +148,7 @@ def model_fn(features, labels, mode, params):
         pcvr = tf.sigmoid(y_cvr)
         pctcvr = pctr*pcvr
 
-    predictions={"pcvr": pcvr, "pctr": pctr, "pctcvr": pctcvr, "sample_id": sample_id, "uid":uid, "city":city, "cid_id":cid_id}
+    predictions = {"pctcvr": pctcvr, "uid": uid, "city": city, "cid_id": cid_id}
     export_outputs = {tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: tf.estimator.export.PredictOutput(predictions)}
     # Provide an estimator spec for `ModeKeys.PREDICT`
     if mode == tf.estimator.ModeKeys.PREDICT:
@@ -161,7 +160,6 @@ def model_fn(features, labels, mode, params):
 def main(te_file):
     dt_dir = (date.today() + timedelta(-1)).strftime('%Y%m%d')
     model_dir = "hdfs://172.16.32.4:8020/strategy/esmm/model_ckpt/DeepCvrMTL/" + dt_dir
-    te_files = ["hdfs://172.16.32.4:8020/strategy/esmm/nearby/part-r-00000"]
     model_params = {
         "field_size": 15,
         "feature_size": 600000,
@@ -176,25 +174,13 @@ def main(te_file):
             log_step_count_steps=100, save_summary_steps=100)
     Estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir=model_dir, params=model_params, config=config)
 
-    preds = Estimator.predict(input_fn=lambda: input_fn(te_file, num_epochs=1, batch_size=10000), predict_keys=["pctcvr","pctr","pcvr","sample_id","uid","city","cid_id"])
-
-    # with open("/home/gmuser/esmm/nearby/pred.txt", "w") as fo:
-    #     for prob in preds:
-    #         fo.write("%f\t%f\t%f\n" % (prob['pctr'], prob['pcvr'], prob['pctcvr']))
+    preds = Estimator.predict(input_fn=lambda: input_fn(te_file, num_epochs=1, batch_size=10000), predict_keys=["pctcvr","uid","city","cid_id"])
 
-    # ctcvr = []
     str_result = ""
     for prob in preds:
-        # ctcvr.append((prob["sample_id"][0],prob['pctcvr']))
-        str_result = str_result + str(prob["sample_id"][0]) + ":"+ str(prob["uid"][0]) + ":" + str(prob["city"][0]) + ":" + str(prob["cid_id"][0]) + ":" + str(prob['pctcvr']) + ";"
-        # str_result = list(prob.keys())
-    # return str_result
+        str_result = str_result + str(prob["uid"][0]) + ":" + str(prob["city"][0]) + ":" + str(prob["cid_id"][0]) + ":" + str(prob['pctcvr']) + ";"
     return str_result[:-1]
 
-    # indices = []
-    # for prob in preds:
-    #     indices.append([prob['pctr'], prob['pcvr'], prob['pctcvr']])
-    # return indices
 
 def trans(x):
     return str(x)[2:-1] if str(x)[0] == 'b' else x
@@ -230,24 +216,23 @@ if __name__ == "__main__":
 
         te_files = []
         for i in range(0,10):
-            te_files.append([path + "test_native/part-r-0000" + str(i)])
+            te_files.append([path + "native/part-r-0000" + str(i)])
         for i in range(10,100):
-            te_files.append([path + "test_native/part-r-000" + str(i)])
+            te_files.append([path + "native/part-r-000" + str(i)])
 
-        # te_files = ["hdfs://172.16.32.4:8020/strategy/esmm/test_native/part-r-00000"]
 
         rdd_te_files = spark.sparkContext.parallelize(te_files)
         print("-" * 100)
-        indices = rdd_te_files.repartition(100).map(lambda x: main(x))
+        indices = rdd_te_files.repartition(40).map(lambda x: main(x))
         # print(indices.take(1))
         print("dist predict native")
 
         te_result_dataframe = spark.createDataFrame(indices.flatMap(lambda x: x.split(";")).map(
-            lambda l: Row(sample_id=l.split(":")[0],uid=l.split(":")[1],city=l.split(":")[2],cid_id=l.split(":")[3],ctcvr=l.split(":")[4])))
+            lambda l: Row(uid=l.split(":")[0],city=l.split(":")[1],cid_id=l.split(":")[2],ctcvr=l.split(":")[3])))
 
-        # te_result_dataframe.show()
+        te_result_dataframe.show()
 
-        te_result_dataframe.repartition(50).write.format("parquet").save(path=path+"native_result/",mode="overwrite")
+        # te_result_dataframe.repartition(50).write.format("parquet").save(path=path+"native_result/",mode="overwrite")
 
         print("耗时(秒)：")
         print((time.time()-b))
@@ -276,21 +261,19 @@ if __name__ == "__main__":
         # #nearby data
         te_files = []
         for i in range(0,10):
-            te_files.append([path + "test_nearby/part-r-0000" + str(i)])
+            te_files.append([path + "nearby/part-r-0000" + str(i)])
         for i in range(10,100):
-            te_files.append([path + "test_nearby/part-r-000" + str(i)])
-
+            te_files.append([path + "nearby/part-r-000" + str(i)])
 
-        # te_files = ["hdfs://172.16.32.4:8020/strategy/esmm/test_nearby/part-r-00000"]
 
         rdd_te_files = spark.sparkContext.parallelize(te_files)
         print("-"*100)
-        indices = rdd_te_files.repartition(100).map(lambda x: main(x))
+        indices = rdd_te_files.repartition(40).map(lambda x: main(x))
         # print(indices.take(1))
         print("dist predict nearby")
 
         te_result_dataframe = spark.createDataFrame(indices.flatMap(lambda x: x.split(";")).map(
-            lambda l: Row(sample_id=l.split(":")[0],uid=l.split(":")[1],city=l.split(":")[2],cid_id=l.split(":")[3],ctcvr=l.split(":")[4])))
+            lambda l: Row(uid=l.split(":")[0],city=l.split(":")[1],cid_id=l.split(":")[2],ctcvr=l.split(":")[3])))
 
         # print("nearby rdd data")
         # te_result_dataframe.show()
@@ -338,43 +321,45 @@ if __name__ == "__main__":
         df_all['city_id'] = df_all['city_id'].astype(str)
         df_all["time"] = str(datetime.datetime.now().strftime('%Y%m%d%H%M'))
         print("union_device_count", df_all.shape)
-
-        host = '172.16.40.158'
-        port = 4000
-        user = 'root'
-        password = '3SYz54LS9#^9sBvC'
-        db = 'jerry_test'
-        charset = 'utf8'
-
-
-
-        #delete table
-        df_merge = df_all['device_id'] + df_all['city_id']
-        to_delete = list(df_merge.values)
-        total = len(to_delete)
-        df_merge_str = [str(to_delete[:int(total / 5)]).strip('[]')]
-        for i in range(2, 6):
-            start = int(total * (i - 1) / 5)
-            end = int(total * i / 5)
-            tmp = str(to_delete[start:end]).strip('[]')
-            df_merge_str.append(tmp)
-
-        try:
-            for i in df_merge_str:
-                delete_str = 'delete from esmm_device_diary_queue where concat(device_id,city_id) in ({0})'.format(i)
-                con = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC',
-                                      db='jerry_test')
-                cur = con.cursor()
-                cur.execute(delete_str)
-                con.commit()
-                print("delete done")
-                con.close()
-            engine = create_engine(str(r"mysql+pymysql://%s:" + '%s' + "@%s:%s/%s") % (user, password, host, port, db))
-            df_all.to_sql('esmm_device_diary_queue', con=engine, if_exists='append', index=False, chunksize=8000)
-            print("insert done")
-
-        except Exception as e:
-            print(e)
+        print(df_all.head(10))
+
+
+        # host = '172.16.40.158'
+        # port = 4000
+        # user = 'root'
+        # password = '3SYz54LS9#^9sBvC'
+        # db = 'jerry_test'
+        # charset = 'utf8'
+        #
+        #
+        #
+        # #delete table
+        # df_merge = df_all['device_id'] + df_all['city_id']
+        # to_delete = list(df_merge.values)
+        # total = len(to_delete)
+        # df_merge_str = [str(to_delete[:int(total / 5)]).strip('[]')]
+        # for i in range(2, 6):
+        #     start = int(total * (i - 1) / 5)
+        #     end = int(total * i / 5)
+        #     tmp = str(to_delete[start:end]).strip('[]')
+        #     df_merge_str.append(tmp)
+        #
+        # try:
+        #     for i in df_merge_str:
+        #         delete_str = 'delete from esmm_device_diary_queue where concat(device_id,city_id) in ({0})'.format(i)
+        #         con = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC',
+        #                               db='jerry_test')
+        #         cur = con.cursor()
+        #         cur.execute(delete_str)
+        #         con.commit()
+        #         print("delete done")
+        #         con.close()
+        #     engine = create_engine(str(r"mysql+pymysql://%s:" + '%s' + "@%s:%s/%s") % (user, password, host, port, db))
+        #     df_all.to_sql('esmm_device_diary_queue', con=engine, if_exists='append', index=False, chunksize=8000)
+        #     print("insert done")
+        #
+        # except Exception as e:
+        #     print(e)
 
         print("耗时(min)：")
         print((time.time()-b)/60)