update

eda/esmm/Model_pipline/dist_predict.py

 from datetime import date, timedelta
 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
 import time
-
+from pyspark import StorageLevel
 
 def model_fn(features, labels, mode, params):
     """Bulid Model function f(x) for Estimator."""
@@ -135,7 +141,7 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
     #print(batch_features,batch_labels)
     return batch_features, batch_labels
 
-def main(_):
+def esmm_predict(dist_data):
     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"]
@@ -153,13 +159,28 @@ def main(_):
             log_step_count_steps=100, save_summary_steps=100)
     Estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir="hdfs://172.16.32.4:8020/strategy/esmm/model_ckpt/DeepCvrMTL/", params=model_params, config=config)
 
-    preds = Estimator.predict(input_fn=lambda: input_fn(te_files, num_epochs=1, batch_size=10000), predict_keys=["pctcvr","pctr","pcvr"])
-    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(dist_data, num_epochs=1, batch_size=10000), predict_keys=["pctcvr","pctr","pcvr"])
+    indices = []
+    for prob in preds:
+        indices.append([prob['pctr'], prob['pcvr'], prob['pctcvr']])
+    return indices
+
 
 
 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()
+    spark.sparkContext.setLogLevel("WARN")
+
+    path = "hdfs://172.16.32.4:8020/strategy/esmm/"
+
     b = time.time()
     tf.logging.set_verbosity(tf.logging.INFO)
     tf.app.run()