From 349bc92a619a92bad029c9511972390697265ddc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ren=C3=A9=20Knaebel?= <rknaebel@uni-potsdam.de>
Date: Mon, 13 Nov 2017 14:37:41 +0100
Subject: [PATCH] reorder curve storing

---
 main.py | 40 +++++++++++++++++++++-------------------
 1 file changed, 21 insertions(+), 19 deletions(-)

diff --git a/main.py b/main.py
index 3a9b9ef..bafbd5a 100644
--- a/main.py
+++ b/main.py
@@ -610,17 +610,12 @@ def main_beta():
                                                                                                      args.domain_length,
                                                                                                      args.window)
     path, model_prefix = os.path.split(os.path.normpath(args.model_path))
-    print(path, model_prefix)
-    try:
-        curves = joblib.load(f"{path}/curves.joblib")
-        logger.info(f"load file {path}/curves.joblib successfully")
-    except Exception:
-        curves = {}
-    logger.info(f"currently {len(curves)} models in file: {curves.keys()}")
-    curves[model_prefix] = {"all": {}}
-    
-    domains = domain_val.value.reshape(-1, 40)
-    domains = np.apply_along_axis(lambda d: dataset.decode_domain(d), 1, domains)
+    curves = {
+        model_prefix: {"all": {}}
+    }
+
+    # domains = domain_val.value.reshape(-1, 40)
+    # domains = np.apply_along_axis(lambda d: dataset.decode_domain(d), 1, domains)
 
     def load_df(res):
         df_server = None
@@ -634,12 +629,12 @@ def main_beta():
             data["server_pred"] = server.flatten()
             data["server_val"] = val.flatten()
 
-            if res["server_pred"].flatten().shape == server_val.value.flatten().shape:
-                df_server = pd.DataFrame(data={
-                    "server_pred": res["server_pred"].flatten(),
-                    "domain": domains,
-                    "server_val": server_val.value.flatten()
-                })
+            # if res["server_pred"].flatten().shape == server_val.value.flatten().shape:
+            #     df_server = pd.DataFrame(data={
+            #         "server_pred": res["server_pred"].flatten(),
+            #         "domain": domains,
+            #         "server_val": server_val.value.flatten()
+            #     })
 
         res = pd.DataFrame(data=data)
         res["client_val"] = np.logical_or(res.hits_vt == 1.0, res.hits_trusted >= 3)
@@ -716,8 +711,15 @@ def main_beta():
                 df_domain_avg.server_val.as_matrix(),
                 server_domain_avg_preds)
 
-    joblib.dump(curves, f"{path}/curves.joblib")
-
+    joblib.dump(curves, f"{args.model_path}_curves.joblib")
+    try:
+        curves_all: dict = joblib.load(f"{path}/curves.joblib")
+        logger.info(f"load file {path}/curves.joblib successfully")
+        curves_all[model_prefix] = curves[model_prefix]
+    except Exception:
+        curves_all = curves
+    logger.info(f"currently {len(curves_all)} models in file: {curves_all.keys()}")
+    joblib.dump(curves_all, f"{path}/curves.joblib")
 
 import matplotlib