param predict sucess

eda/esmm/Model_pipline/dist_predict.py

@@ -162,14 +162,14 @@ def main(te_file):
 
     preds = Estimator.predict(input_fn=lambda: input_fn(te_file, 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']))
+    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']))
 
-    indices = []
-    for prob in preds:
-        indices.append([prob['pctr'], prob['pcvr'], prob['pctcvr']])
-    return indices
+    # indices = []
+    # for prob in preds:
+    #     indices.append([prob['pctr'], prob['pcvr'], prob['pctcvr']])
+    # return indices
 
 def test_map(x):
     return x * x
@@ -198,13 +198,13 @@ if __name__ == "__main__":
 
     tf.logging.set_verbosity(tf.logging.INFO)
 
-    # te_files = [path + "nearby/part-r-00000"]
-    # main(te_files)
-
-    te_files = [[path+"nearby/part-r-00000"],[path+"native/part-r-00000"]]
-    rdd_te_files = spark.sparkContext.parallelize(te_files)
-    indices = rdd_te_files.repartition(2).map(lambda x: main(x))
-    print(indices.collect())
+    te_files = [path + "nearby/part-r-00000"]
+    main(te_files)
+    # te_files = [[path+"nearby/part-r-00000"],[path+"native/part-r-00000"]]
+    # rdd_te_files = spark.sparkContext.parallelize(te_files)
+    # indices = rdd_te_files.repartition(2).map(lambda x: main(x))
+    # print(indices.collect())
+    # tf.app.run()
 
 
     b = time.time()