add hyperband savefile config, minor change of parameter name

arguments.py

@@ -15,6 +15,9 @@ parser.add_argument("--data", action="store", dest="train_data",
 parser.add_argument("--test", action="store", dest="test_data",
                     default="data/full_future_dataset.csv.tar.gz")
 
+parser.add_argument("--hyper_result", action="store", dest="hyperband_results",
+                    default="")
+
 
 parser.add_argument("--model", action="store", dest="model_path",
                     default="results/model_x")

hyperband.py

@@ -7,6 +7,7 @@ from math import ceil, log
 from random import random as rng
 from time import ctime, time
 
+import joblib
 import numpy as np
 from keras.callbacks import EarlyStopping
 
@@ -24,7 +25,7 @@ def sample_params(param_distribution: dict):
 
 
 class Hyperband:
-    def __init__(self, param_distribution, X, y, max_iter=81):
+    def __init__(self, param_distribution, X, y, max_iter=81, savefile=None):
         self.get_params = lambda: sample_params(param_distribution)
 
         self.max_iter = max_iter  # maximum iterations per configuration
@@ -39,6 +40,8 @@ class Hyperband:
         self.best_loss = np.inf
         self.best_counter = -1
 
+        self.savefile = savefile
+        
         self.X = X
         self.y = y
     
@@ -143,4 +146,7 @@ class Hyperband:
                 random_configs = [random_configs[i] for i in indices if not early_stops[i]]
                 random_configs = random_configs[0:int(n_configs / self.eta)]
         
+            if self.savefile:
+                joblib.dump(self.results, self.savefile)
+        
         return self.results

main.py

@@ -63,7 +63,7 @@ PARAMS = {
     #
     'dropout': 0.5,  # currently fix
     'domain_features': args.domain_embedding,
-    'embedding_size': args.embedding,
+    'embedding': args.embedding,
     'flow_features': 3,
     'filter_embedding': args.filter_embedding,
     'dense_embedding': args.dense_embedding,
@@ -132,7 +132,7 @@ def main_hyperband():
                              [domain_tr, flow_tr],
                              [client_tr, server_tr])
     results = hp.run()
-    joblib.dump(results, "hyperband.joblib")
+    joblib.dump(results, args.hyperband_results)
 
 
 def main_train(param=None):

models/__init__.py

@@ -9,7 +9,7 @@ def get_models_by_params(params: dict):
     # mainly embedding model
     network_type = params.get("type")
     network_depth = params.get("depth")
-    embedding_size = params.get("embedding_size")
+    embedding_size = params.get("embedding")
     input_length = params.get("input_length")
     filter_embedding = params.get("filter_embedding")
     kernel_embedding = params.get("kernel_embedding")