From d0029bae9fcdb085b62b034012640f5e23b7aee7 Mon Sep 17 00:00:00 2001
From: luigiba <baldarigigi@gmail.com>
Date: Mon, 5 Aug 2019 20:25:50 +0200
Subject: [PATCH] Parallelized Link Prediction

---
 .idea/misc.xml                                |   2 +-
 .idea/modules.xml                             |   2 +-
 .idea/vcs.xml                                 |   2 +-
 .idea/workspace.xml                           | 456 ++------------
 Config.py                                     | 576 ++++++++++++++----
 OpenKE_new_Spark.iml => OpenKEonSpark.iml     |   2 +-
 __pycache__/Config.cpython-36.pyc             | Bin 18420 -> 26845 bytes
 .../distribute_training.cpython-36.pyc        | Bin 7691 -> 7780 bytes
 base/Base.cpp                                 |   5 -
 base/Setting.h                                |   9 -
 base/Test.h                                   | 263 ++------
 commands.txt                                  |  46 ++
 distribute_training.py                        | 231 +++----
 gpu_info.py                                   | 186 ++++++
 main_spark.py                                 |  96 +--
 release/Base.so                               | Bin 74416 -> 60424 bytes
 run.sh                                        | 249 --------
 run_dbpedia_10.sh                             |  43 ++
 run_dbpedia_15.sh                             |  43 ++
 run_dbpedia_5.sh                              |  43 ++
 test.py                                       |  31 +
 test_1.py                                     |  32 +
 22 files changed, 1152 insertions(+), 1165 deletions(-)
 rename OpenKE_new_Spark.iml => OpenKEonSpark.iml (87%)
 create mode 100644 commands.txt
 create mode 100644 gpu_info.py
 delete mode 100644 run.sh
 create mode 100644 run_dbpedia_10.sh
 create mode 100644 run_dbpedia_15.sh
 create mode 100644 run_dbpedia_5.sh
 create mode 100644 test.py
 create mode 100644 test_1.py

diff --git a/.idea/misc.xml b/.idea/misc.xml
index 4c1170f..4938f72 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" languageLevel="JDK_12" default="false" project-jdk-name="Python 3.6" project-jdk-type="Python SDK">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6" project-jdk-type="Python SDK">
     <output url="file://$PROJECT_DIR$/out" />
   </component>
 </project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
index 80f322d..8a0e011 100644
--- a/.idea/modules.xml
+++ b/.idea/modules.xml
@@ -2,7 +2,7 @@
 <project version="4">
   <component name="ProjectModuleManager">
     <modules>
-      <module fileurl="file://$PROJECT_DIR$/OpenKE_new_Spark.iml" filepath="$PROJECT_DIR$/OpenKE_new_Spark.iml" />
+      <module fileurl="file://$PROJECT_DIR$/OpenKEonSpark.iml" filepath="$PROJECT_DIR$/OpenKEonSpark.iml" />
     </modules>
   </component>
 </project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
index 94a25f7..35eb1dd 100644
--- a/.idea/vcs.xml
+++ b/.idea/vcs.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+    <mapping directory="" vcs="Git" />
   </component>
 </project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index ea9b0b8..35e178a 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -1,8 +1,21 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="ChangeListManager">
-    <list default="true" id="fd9b69df-948f-43d7-abff-74da8c63edc1" name="Default Changelist" comment="" />
-    <ignored path="$PROJECT_DIR$/out/" />
+    <list default="true" id="0f79fb12-bf2b-474d-8da4-e622edb018dd" name="Default Changelist" comment="">
+      <change beforePath="$PROJECT_DIR$/.idea/misc.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/misc.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/modules.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/modules.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/vcs.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/vcs.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Config.py" beforeDir="false" afterPath="$PROJECT_DIR$/Config.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/OpenKE_new_Spark.iml" beforeDir="false" afterPath="$PROJECT_DIR$/OpenKEonSpark.iml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/base/Base.cpp" beforeDir="false" afterPath="$PROJECT_DIR$/base/Base.cpp" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/base/Setting.h" beforeDir="false" afterPath="$PROJECT_DIR$/base/Setting.h" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/base/Test.h" beforeDir="false" afterPath="$PROJECT_DIR$/base/Test.h" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/distribute_training.py" beforeDir="false" afterPath="$PROJECT_DIR$/distribute_training.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/main_spark.py" beforeDir="false" afterPath="$PROJECT_DIR$/main_spark.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/release/Base.so" beforeDir="false" afterPath="$PROJECT_DIR$/release/Base.so" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/run.sh" beforeDir="false" afterPath="$PROJECT_DIR$/run.sh" afterDir="false" />
+    </list>
     <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
@@ -10,68 +23,11 @@
     <option name="LAST_RESOLUTION" value="IGNORE" />
   </component>
   <component name="DefaultGradleProjectSettings">
-    <option name="isMigrated" value="true" />
+    <option name="testRunner" value="GRADLE" />
+    <option name="delegatedBuild" value="true" />
   </component>
-  <component name="FileEditorManager">
-    <leaf SIDE_TABS_SIZE_LIMIT_KEY="300" />
-  </component>
-  <component name="FileTemplateManagerImpl">
-    <option name="RECENT_TEMPLATES">
-      <list>
-        <option value="Python Script" />
-      </list>
-    </option>
-  </component>
-  <component name="FindInProjectRecents">
-    <findStrings>
-      <find>head_lef</find>
-      <find>head_rig</find>
-      <find>tail_rig</find>
-      <find>bern</find>
-      <find>get_similar_entites</find>
-      <find>summary_op</find>
-      <find>_global_step_tensor</find>
-      <find>config</find>
-      <find>argv</find>
-      <find>TODO</find>
-      <find>def set_mode</find>
-      <find>return</find>
-      <find>batch_h_addr</find>
-      <find>res_pos</find>
-      <find>corrupt_head</find>
-      <find>early_stop_stopping_setp</find>
-      <find>rand_max</find>
-      <find>valid_</find>
-      <find>corrupt</find>
-      <find>tail_lef</find>
-      <find>tail_type</find>
-      <find>trainList</find>
-      <find>judgeHeadBatch</find>
-      <find>tripleTotal</find>
-      <find>rand</find>
-      <find>corrupt(</find>
-      <find>setConstrainFlag</find>
-      <find>getTripleTotal</find>
-      <find>std</find>
-      <find>workThreads</find>
-    </findStrings>
-  </component>
-  <component name="IdeDocumentHistory">
-    <option name="CHANGED_PATHS">
-      <list>
-        <option value="$PROJECT_DIR$/base/Reader.h" />
-        <option value="$PROJECT_DIR$/base/Setting.h" />
-        <option value="$PROJECT_DIR$/base/Base.cpp" />
-        <option value="$PROJECT_DIR$/test.py" />
-        <option value="$PROJECT_DIR$/base/Random.h" />
-        <option value="$PROJECT_DIR$/base/Corrupt.h" />
-        <option value="$PROJECT_DIR$/distribute_training.py" />
-        <option value="$PROJECT_DIR$/Config.py" />
-        <option value="$PROJECT_DIR$/base/Test.h" />
-        <option value="$PROJECT_DIR$/example_test_transe.py" />
-        <option value="$PROJECT_DIR$/main_spark.py" />
-      </list>
-    </option>
+  <component name="Git.Settings">
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
   </component>
   <component name="ProjectFrameBounds" extendedState="6">
     <option name="x" value="293" />
@@ -79,37 +35,19 @@
     <option name="width" value="1400" />
     <option name="height" value="1000" />
   </component>
-  <component name="ProjectView">
-    <navigator proportions="" version="1">
-      <foldersAlwaysOnTop value="true" />
-    </navigator>
-    <panes>
-      <pane id="PackagesPane" />
-      <pane id="Scope" />
-      <pane id="ProjectPane">
-        <subPane>
-          <expand>
-            <path>
-              <item name="OpenKE_new_Spark" type="b2602c69:ProjectViewProjectNode" />
-              <item name="OpenKE_new_Spark" type="462c0819:PsiDirectoryNode" />
-            </path>
-            <path>
-              <item name="OpenKE_new_Spark" type="b2602c69:ProjectViewProjectNode" />
-              <item name="OpenKE_new_Spark" type="462c0819:PsiDirectoryNode" />
-              <item name="base" type="462c0819:PsiDirectoryNode" />
-            </path>
-          </expand>
-          <select />
-        </subPane>
-      </pane>
-    </panes>
-  </component>
+  <component name="ProjectId" id="1On7TJBKj3ZnByQP1GDdyVRo18W" />
   <component name="PropertiesComponent">
     <property name="SHARE_PROJECT_CONFIGURATION_FILES" value="true" />
-    <property name="project.structure.last.edited" value="Project" />
+    <property name="last_opened_file_path" value="$USER_HOME$/Desktop/DBpedia/generate.py" />
+    <property name="project.structure.last.edited" value="Problems" />
     <property name="project.structure.proportion" value="0.15" />
     <property name="project.structure.side.proportion" value="0.2" />
-    <property name="settings.editor.selected.configurable" value="preferences.editor" />
+    <property name="settings.editor.selected.configurable" value="preferences.pluginManager" />
+  </component>
+  <component name="RecentsManager">
+    <key name="CopyFile.RECENT_KEYS">
+      <recent name="$PROJECT_DIR$" />
+    </key>
   </component>
   <component name="RunDashboard">
     <option name="ruleStates">
@@ -123,9 +61,9 @@
       </list>
     </option>
   </component>
-  <component name="RunManager" selected="Python.example_test_transe">
-    <configuration name="example_test_transe" type="PythonConfigurationType" factoryName="Python" temporary="true">
-      <module name="OpenKE_new_Spark" />
+  <component name="RunManager" selected="Python.test_1">
+    <configuration name="test" type="PythonConfigurationType" factoryName="Python" temporary="true">
+      <module name="OpenKEonSpark" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
       <envs>
@@ -136,7 +74,7 @@
       <option name="IS_MODULE_SDK" value="true" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/example_test_transe.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -145,8 +83,8 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
-    <configuration name="test" type="PythonConfigurationType" factoryName="Python" temporary="true">
-      <module name="OpenKE_new_Spark" />
+    <configuration name="test_1" type="PythonConfigurationType" factoryName="Python" temporary="true">
+      <module name="OpenKEonSpark" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
       <envs>
@@ -157,7 +95,7 @@
       <option name="IS_MODULE_SDK" value="true" />
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/test_1.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="false" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -168,7 +106,7 @@
     </configuration>
     <recent_temporary>
       <list>
-        <item itemvalue="Python.example_test_transe" />
+        <item itemvalue="Python.test_1" />
         <item itemvalue="Python.test" />
       </list>
     </recent_temporary>
@@ -178,313 +116,39 @@
   </component>
   <component name="TaskManager">
     <task active="true" id="Default" summary="Default task">
-      <changelist id="fd9b69df-948f-43d7-abff-74da8c63edc1" name="Default Changelist" comment="" />
-      <created>1563207549465</created>
+      <changelist id="0f79fb12-bf2b-474d-8da4-e622edb018dd" name="Default Changelist" comment="" />
+      <created>1564592804266</created>
       <option name="number" value="Default" />
       <option name="presentableId" value="Default" />
-      <updated>1563207549465</updated>
+      <updated>1564592804266</updated>
     </task>
     <servers />
   </component>
-  <component name="TodoView">
-    <todo-panel id="selected-file">
-      <is-autoscroll-to-source value="true" />
-    </todo-panel>
-    <todo-panel id="all">
-      <are-packages-shown value="true" />
-      <is-autoscroll-to-source value="true" />
-    </todo-panel>
-  </component>
-  <component name="ToolWindowManager">
-    <frame x="67" y="25" width="1853" height="1055" extended-state="6" />
-    <layout>
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-      <window_info id="Image Layers" order="2" />
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-          </folding>
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-      </provider>
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-      <provider selected="true" editor-type-id="text-editor">
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-          <folding>
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-          </folding>
-        </state>
-      </provider>
-    </entry>
-    <entry file="file://$PROJECT_DIR$/base/Test.h">
-      <provider selected="true" editor-type-id="text-editor">
-        <state relative-caret-position="689">
-          <caret line="404" column="62" selection-start-line="404" selection-start-column="62" selection-end-line="404" selection-end-column="62" />
-        </state>
-      </provider>
-    </entry>
+  <component name="Vcs.Log.Tabs.Properties">
+    <option name="TAB_STATES">
+      <map>
+        <entry key="MAIN">
+          <value>
+            <State>
+              <option name="COLUMN_ORDER" />
+            </State>
+          </value>
+        </entry>
+      </map>
+    </option>
   </component>
-  <component name="masterDetails">
-    <states>
-      <state key="ArtifactsStructureConfigurable.UI">
-        <settings>
-          <artifact-editor />
-          <splitter-proportions>
-            <option name="proportions">
-              <list>
-                <option value="0.2" />
-              </list>
-            </option>
-          </splitter-proportions>
-        </settings>
-      </state>
-      <state key="FacetStructureConfigurable.UI">
-        <settings>
-          <splitter-proportions>
-            <option name="proportions">
-              <list>
-                <option value="0.2" />
-              </list>
-            </option>
-          </splitter-proportions>
-        </settings>
-      </state>
-      <state key="GlobalLibrariesConfigurable.UI">
-        <settings>
-          <splitter-proportions>
-            <option name="proportions">
-              <list>
-                <option value="0.2" />
-              </list>
-            </option>
-          </splitter-proportions>
-        </settings>
-      </state>
-      <state key="JdkListConfigurable.UI">
-        <settings>
-          <last-edited>Python 3.6</last-edited>
-          <splitter-proportions>
-            <option name="proportions">
-              <list>
-                <option value="0.2" />
-              </list>
-            </option>
-          </splitter-proportions>
-        </settings>
-      </state>
-      <state key="ModuleStructureConfigurable.UI">
-        <settings>
-          <last-edited>OpenKE_new_Spark</last-edited>
-          <splitter-proportions>
-            <option name="proportions">
-              <list>
-                <option value="0.2" />
-              </list>
-            </option>
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-      </state>
-      <state key="ProjectLibrariesConfigurable.UI">
-        <settings>
-          <splitter-proportions>
-            <option name="proportions">
-              <list>
-                <option value="0.2" />
-              </list>
-            </option>
-          </splitter-proportions>
-        </settings>
-      </state>
-    </states>
+  <component name="XDebuggerManager">
+    <breakpoint-manager>
+      <breakpoints>
+        <line-breakpoint enabled="true" suspend="THREAD" type="python-line">
+          <url>file://$PROJECT_DIR$/main_spark.py</url>
+          <option name="timeStamp" value="1" />
+        </line-breakpoint>
+      </breakpoints>
+    </breakpoint-manager>
   </component>
 </project>
\ No newline at end of file
diff --git a/Config.py b/Config.py
index 7601c38..687a44d 100644
--- a/Config.py
+++ b/Config.py
@@ -2,12 +2,11 @@
 import numpy as np
 import tensorflow as tf
 import os
-import time
-import datetime
 import ctypes
 import json
-#EDIT
 import sys
+import threading
+import time
 
 class Config(object):
     '''
@@ -15,35 +14,35 @@ class Config(object):
     '''
     #EDIT
     def __init__(self, cpp_lib_path=None, init_new_entities=False):
-        #EDIT
         self.init_new_entities = init_new_entities
 
         if init_new_entities == False:
-            # base_file = os.path.abspath(os.path.join(os.path.dirname(__file__), '../release/Base.so'))
-            #TODO: add main_spark parameter
             if cpp_lib_path == None:
-                cpp_lib_path = '/home/luigi/IdeaProjects/OpenKE_new_Spark/release/Base.so'
+                cpp_lib_path = '/home/luigi/IdeaProjects/OpenKEonSpark/release/Base.so'
             base_file = os.path.abspath(cpp_lib_path)
-
             self.lib = ctypes.cdll.LoadLibrary(base_file)
             self.lib.sampling.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int64, ctypes.c_int64, ctypes.c_int64]
-            self.lib.getHeadBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
-            self.lib.getTailBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
-            self.lib.testHead.argtypes = [ctypes.c_void_p]
-            self.lib.testTail.argtypes = [ctypes.c_void_p]
+
+            #link prediction
+            self.lib.getTailBatch.argtypes = [ctypes.c_int64, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
+            self.lib.testTail.argtypes = [ctypes.c_int64, ctypes.c_void_p]
+            self.lib.testTail.restype = ctypes.POINTER(ctypes.c_int64 * 4)
+            self.lib.getHeadBatch.argtypes = [ctypes.c_int64, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
+            self.lib.testHead.argtypes = [ctypes.c_int64, ctypes.c_void_p]
+            self.lib.testHead.restype = ctypes.POINTER(ctypes.c_int64 * 4)
+            self.test_head = 0
+
+            #triple classification
             self.lib.getTestBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
             self.lib.getValidBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
             self.lib.getBestThreshold.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
             self.lib.test_triple_classification.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
-            self.test_flag = False
+
             self.in_path = None
+            self.test_log_path = None
             self.out_path = None
             self.bern = 0
-
-            #EDIT
-            self.constrainFlag = 0
-
-            self.hidden_size = 100
+            self.hidden_size = 64
             self.ent_size = self.hidden_size
             self.rel_size = self.hidden_size
             self.train_times = 0
@@ -63,7 +62,6 @@ def __init__(self, cpp_lib_path=None, init_new_entities=False):
             self.test_link_prediction = False
             self.test_triple_classification = False
             self.valid_triple_classification = False
-            self.early_stopping = None # It expects a tuple of the following: (patience, min_delta)
 
     def init_link_prediction(self):
         r'''
@@ -71,12 +69,9 @@ def init_link_prediction(self):
         '''
         self.lib.importTestFiles()
         self.lib.importTypeFiles()
-        self.test_h = np.zeros(self.lib.getEntityTotal(), dtype = np.int64)
-        self.test_t = np.zeros(self.lib.getEntityTotal(), dtype = np.int64)
-        self.test_r = np.zeros(self.lib.getEntityTotal(), dtype = np.int64)
-        self.test_h_addr = self.test_h.__array_interface__['data'][0]
-        self.test_t_addr = self.test_t.__array_interface__['data'][0]
-        self.test_r_addr = self.test_r.__array_interface__['data'][0]
+        self.N_THREADS_LP = 10
+        self.lp_res = []
+        for _ in range(self.N_THREADS_LP): self.lp_res.append({})
 
     def init_triple_classification(self):
         r'''
@@ -85,8 +80,6 @@ def init_triple_classification(self):
         self.lib.importTestFiles()
         self.lib.importTypeFiles()
 
-
-        #EDIT
         self.acc = np.zeros(1, dtype = np.float32)
         self.acc_addr = self.acc.__array_interface__['data'][0]
 
@@ -119,7 +112,7 @@ def init_triple_classification(self):
         self.relThresh = np.zeros(self.lib.getRelationTotal(), dtype = np.float32)
         self.relThresh_addr = self.relThresh.__array_interface__['data'][0]
 
-    #EDIT
+
     def init_valid_triple_classification(self):
         self.lib.importTestFiles()
         self.lib.importTypeFiles()
@@ -147,34 +140,23 @@ def init_valid_triple_classification(self):
 
     # prepare for train and test
     def init(self):
-        #EDIT
         if self.init_new_entities == False:
-
             self.trainModel = None
             if self.in_path != None:
                 self.lib.setInPath(ctypes.create_string_buffer(self.in_path.encode(), len(self.in_path) * 2))
                 self.lib.setBern(self.bern)
-
-                #EDIT
-                self.lib.setConstrainFlag(self.constrainFlag)
-
                 self.lib.setWorkThreads(self.workThreads)
                 self.lib.randReset()
                 self.lib.importTrainFiles()
                 self.relTotal = self.lib.getRelationTotal()
                 self.entTotal = self.lib.getEntityTotal()
-
-                #EDIT
-                # self.trainTotal = self.lib.getTrainTotal()
                 self.trainTotal = self.lib.getTrainTotal_()
-
                 self.testTotal = self.lib.getTestTotal()
                 self.validTotal = self.lib.getValidTotal()
-
-                #EDIT
-                # self.batch_size = int(self.lib.getTrainTotal() / self.nbatches)
                 self.bt = self.lib.getBatchTotal()
+
                 tot = None
+
                 if self.bt > 0:
                     tot = self.bt
                 else:
@@ -191,7 +173,6 @@ def init(self):
                 print("Batch size is {}".format(self.batch_size))
                 print("Number of batches: {}".format(self.nbatches))
 
-
                 self.batch_seq_size = self.batch_size * (1 + self.negative_ent + self.negative_rel)
                 self.batch_h = np.zeros(self.batch_size * (1 + self.negative_ent + self.negative_rel), dtype = np.int64)
                 self.batch_t = np.zeros(self.batch_size * (1 + self.negative_ent + self.negative_rel), dtype = np.int64)
@@ -207,10 +188,12 @@ def init(self):
                 self.init_link_prediction()
             if self.test_triple_classification:
                 self.init_triple_classification()
-            #EDIT
             if self.valid_triple_classification:
                 self.init_valid_triple_classification()
 
+    def set_test_log_path(self, p):
+        self.test_log_path = p
+
     def get_ent_total(self):
         return self.entTotal
 
@@ -232,7 +215,6 @@ def set_test_link_prediction(self, flag):
     def set_test_triple_classification(self, flag):
         self.test_triple_classification = flag
 
-    #EDIT
     def set_valid_triple_classification(self, flag):
         self.valid_triple_classification = flag
 
@@ -251,9 +233,8 @@ def set_out_files(self, path):
     def set_bern(self, bern):
         self.bern = bern
 
-    #EDIT
-    def set_constrain_flag(self, constrainFlag):
-        self.constrainFlag = constrainFlag
+    def set_test_head(self, test_head):
+        self.test_head = test_head
 
     def set_dimension(self, dim):
         self.hidden_size = dim
@@ -275,9 +256,6 @@ def set_nbatches(self, nbatches):
     def set_margin(self, margin):
         self.margin = margin
 
-    # def set_work_threads(self, threads):
-    #     self.workThreads = threads
-
     def set_ent_neg_rate(self, rate):
         self.negative_ent = rate
 
@@ -294,9 +272,6 @@ def set_export_files(self, path, steps = 0):
     def set_export_steps(self, steps):
         self.export_steps = steps
 
-    def set_early_stopping(self, early_stopping):
-        self.early_stopping = early_stopping
-
     # call C function for sampling
     def sampling(self):
         self.lib.sampling(self.batch_h_addr, self.batch_t_addr, self.batch_r_addr, self.batch_y_addr, self.batch_size, self.negative_ent, self.negative_rel)
@@ -307,7 +282,6 @@ def save_tensorflow(self):
             with self.sess.as_default():
                 self.saver.save(self.sess, self.exportName)
 
-    #EDIT
     def save_tensorflow_weights(self, export_name=None, write_meta_graph=False):
         if export_name == None:
             export_name = self.exportName
@@ -315,7 +289,6 @@ def save_tensorflow_weights(self, export_name=None, write_meta_graph=False):
             with self.sess.as_default():
                 self.saver.save(self.sess, export_name, write_meta_graph=write_meta_graph, write_state=False)
 
-    # restore model
     def restore_tensorflow(self):
         with self.graph.as_default():
             with self.sess.as_default():
@@ -376,37 +349,10 @@ def set_parameters(self, lists):
         for i in lists:
             self.set_parameters_by_name(i, lists[i])
 
-    #EDIT
-    # def set_model(self, model):
-    # 	self.model = model
-    # 	self.graph = tf.Graph()
-    # 	with self.graph.as_default():
-    # 		self.sess = tf.Session()
-    # 		with self.sess.as_default():
-    # 			initializer = tf.contrib.layers.xavier_initializer(uniform = True)
-    # 			with tf.variable_scope("model", reuse=None, initializer = initializer):
-    # 				self.trainModel = self.model(config = self)
-    # 				if self.optimizer != None:
-    # 					pass
-    # 				elif self.opt_method == "Adagrad" or self.opt_method == "adagrad":
-    # 					self.optimizer = tf.train.AdagradOptimizer(learning_rate = self.alpha, initial_accumulator_value=1e-20)
-    # 				elif self.opt_method == "Adadelta" or self.opt_method == "adadelta":
-    # 					self.optimizer = tf.train.AdadeltaOptimizer(self.alpha)
-    # 				elif self.opt_method == "Adam" or self.opt_method == "adam":
-    # 					self.optimizer = tf.train.AdamOptimizer(self.alpha)
-    # 				else:
-    # 					self.optimizer = tf.train.GradientDescentOptimizer(self.alpha)
-    # 				grads_and_vars = self.optimizer.compute_gradients(self.trainModel.loss)
-    # 				self.train_op = self.optimizer.apply_gradients(grads_and_vars)
-    # 			self.saver = tf.train.Saver()
-    # 			self.sess.run(tf.global_variables_initializer())
-
-
     def set_model(self, model):
         self.model = model
 
 
-    #EDIT
     def import_model(self, ckpt):
         self.graph = tf.Graph()
         with self.graph.as_default():
@@ -417,7 +363,7 @@ def import_model(self, ckpt):
                 print("Importing variables...")
                 self.saver.restore(self.sess, ckpt)
 
-    #EDIT
+
     def set_model_and_session(self, model):
         self.model = model
         self.graph = tf.Graph()
@@ -452,42 +398,250 @@ def test_step(self, test_h, test_t, test_r):
         return predict
 
 
-    #EDIT
-    # def run(self):
-    # 	with self.graph.as_default():
-    # 		with self.sess.as_default():
-    # 			if self.importName != None:
-    # 				self.restore_tensorflow()
-    # 			if self.early_stopping is not None:
-    # 				patience, min_delta = self.early_stopping
-    # 				best_loss = np.finfo('float32').max
-    # 				wait_steps = 0
-    # 			for times in range(self.train_times):
-    # 				loss = 0.0
-    # 				t_init = time.time()
-    # 				for batch in range(self.nbatches):
-    # 					self.sampling()
-    # 					loss += self.train_step(self.batch_h, self.batch_t, self.batch_r, self.batch_y)
-    # 				t_end = time.time()
-    # 				if self.log_on:
-    # 					print('Epoch: {}, loss: {}, time: {}'.format(times, loss, (t_end - t_init)))
-    # 				if self.exportName != None and (self.export_steps!=0 and times % self.export_steps == 0):
-    # 					self.save_tensorflow()
-    # 				if self.early_stopping is not None:
-    # 					if loss + min_delta < best_loss:
-    # 						best_loss = loss
-    # 						wait_steps = 0
-    # 					elif wait_steps < patience:
-    # 						wait_steps += 1
-    # 					else:
-    # 						print('Early stopping. Losses have not been improved enough in {} times'.format(patience))
-    # 						break
-    # 			if self.exportName != None:
-    # 				self.save_tensorflow()
-    # 			if self.out_path != None:
-    # 				self.save_parameters(self.out_path)
-
 
+    def test_lp_range(self, index, lef, rig):
+        l1_filter_tot = l1_tot = r1_tot = r1_filter_tot = l_tot = r_tot = l_filter_rank = l_rank = l_filter_reci_rank = l_reci_rank = 0.0
+        l3_filter_tot = l3_tot = r3_tot = r3_filter_tot = l_filter_tot = r_filter_tot = r_filter_rank = r_rank = r_filter_reci_rank = r_reci_rank = 0.0
+
+        #TYPE_C
+        l1_filter_tot_constrain = l1_tot_constrain = r1_tot_constrain = r1_filter_tot_constrain = l_tot_constrain = r_tot_constrain = l_filter_rank_constrain = l_rank_constrain = l_filter_reci_rank_constrain = l_reci_rank_constrain = 0.0
+        l3_filter_tot_constrain = l3_tot_constrain = r3_tot_constrain = r3_filter_tot_constrain = l_filter_tot_constrain = r_filter_tot_constrain = r_filter_rank_constrain = r_rank_constrain = r_filter_reci_rank_constrain = r_reci_rank_constrain = 0.0
+
+
+        test_h = np.zeros(self.lib.getEntityTotal(), dtype = np.int64)
+        test_t = np.zeros(self.lib.getEntityTotal(), dtype = np.int64)
+        test_r = np.zeros(self.lib.getEntityTotal(), dtype = np.int64)
+        test_h_addr = test_h.__array_interface__['data'][0]
+        test_t_addr = test_t.__array_interface__['data'][0]
+        test_r_addr = test_r.__array_interface__['data'][0]
+
+        print("Test link prediction range from {} to {}".format(lef, rig-1))
+
+        if os.path.exists(self.test_log_path+"thread"+str(index)):
+            with open(self.test_log_path+"thread"+str(index), 'r') as f:
+                last_i = int(f.readline())
+                print("Restoring test results from index {}".format(last_i))
+
+                lef = last_i + 1
+
+                r_filter_tot = float(f.readline())
+                r_tot = float(f.readline())
+                r3_filter_tot = float(f.readline())
+                r3_tot = float(f.readline())
+                r1_filter_tot = float(f.readline())
+                r1_tot = float(f.readline())
+                r_filter_rank = float(f.readline())
+                r_rank = float(f.readline())
+                r_filter_reci_rank = float(f.readline())
+                r_reci_rank = float(f.readline())
+                r_filter_tot_constrain = float(f.readline())
+                r_tot_constrain = float(f.readline())
+                r3_filter_tot_constrain = float(f.readline())
+                r3_tot_constrain = float(f.readline())
+                r1_filter_tot_constrain = float(f.readline())
+                r1_tot_constrain = float(f.readline())
+                r_filter_rank_constrain = float(f.readline())
+                r_rank_constrain = float(f.readline())
+                r_filter_reci_rank_constrain = float(f.readline())
+                r_reci_rank_constrain = float(f.readline())
+
+                if self.test_head != 0:
+                    l_filter_tot = float(f.readline())
+                    l_tot = float(f.readline())
+                    l3_filter_tot = float(f.readline())
+                    l3_tot = float(f.readline())
+                    l1_filter_tot = float(f.readline())
+                    l1_tot = float(f.readline())
+                    l_filter_rank = float(f.readline())
+                    l_rank = float(f.readline())
+                    l_filter_reci_rank = float(f.readline())
+                    l_reci_rank = float(f.readline())
+                    l_filter_tot_constrain = float(f.readline())
+                    l_tot_constrain = float(f.readline())
+                    l3_filter_tot_constrain = float(f.readline())
+                    l3_tot_constrain = float(f.readline())
+                    l1_filter_tot_constrain = float(f.readline())
+                    l1_tot_constrain = float(f.readline())
+                    l_filter_rank_constrain = float(f.readline())
+                    l_rank_constrain = float(f.readline())
+                    l_filter_reci_rank_constrain = float(f.readline())
+                    l_reci_rank_constrain = float(f.readline())
+
+        test_triples_done = 0
+        for i in range(lef, rig):
+            #tail
+            self.lib.getTailBatch(i, test_h_addr, test_t_addr, test_r_addr)
+            res = self.test_step(test_h, test_t, test_r)
+            test_tail_res = [j for j in self.lib.testTail(i, res.__array_interface__['data'][0]).contents]
+            r_s = test_tail_res[0]
+            r_filter_s = test_tail_res[1]
+            r_s_constrain = test_tail_res[2]
+            r_filter_s_constrain = test_tail_res[3]
+
+            if (r_filter_s < 10): r_filter_tot += 1
+            if (r_s < 10): r_tot += 1
+            if (r_filter_s < 3): r3_filter_tot += 1
+            if (r_s < 3): r3_tot += 1
+            if (r_filter_s < 1): r1_filter_tot += 1
+            if (r_s < 1): r1_tot += 1
+
+            r_filter_rank += (1+r_filter_s)
+            r_rank += (1+r_s)
+            r_filter_reci_rank += np.divide(1.0, (1+r_filter_s))
+            r_reci_rank += np.divide(1.0, (1+r_s))
+
+            #TYPE_C
+            if (r_filter_s_constrain < 10): r_filter_tot_constrain += 1
+            if (r_s_constrain < 10): r_tot_constrain += 1
+            if (r_filter_s_constrain < 3): r3_filter_tot_constrain += 1
+            if (r_s_constrain < 3): r3_tot_constrain += 1
+            if (r_filter_s_constrain < 1): r1_filter_tot_constrain += 1
+            if (r_s_constrain < 1): r1_tot_constrain += 1
+
+            r_filter_rank_constrain += (1+r_filter_s_constrain)
+            r_rank_constrain += (1+r_s_constrain)
+            r_filter_reci_rank_constrain += np.divide(1.0, (1+r_filter_s_constrain))
+            r_reci_rank_constrain += np.divide(1.0, (1+r_s_constrain))
+
+            #head
+            if self.test_head != 0:
+                #head
+                self.lib.getHeadBatch(i, test_h_addr, test_t_addr, test_r_addr)
+                res = self.test_step(test_h, test_t, test_r)
+                test_head_res = [j for j in self.lib.testHead(i, res.__array_interface__['data'][0]).contents]
+                l_s = test_head_res[0]
+                l_filter_s = test_head_res[1]
+                l_s_constrain = test_head_res[2]
+                l_filter_s_constrain = test_head_res[3]
+
+                if (l_filter_s < 10): l_filter_tot += 1
+                if (l_s < 10): l_tot += 1
+                if (l_filter_s < 3): l3_filter_tot += 1
+                if (l_s < 3): l3_tot += 1
+                if (l_filter_s < 1): l1_filter_tot += 1
+                if (l_s < 1): l1_tot += 1
+
+                l_filter_rank += (l_filter_s+1)
+                l_rank += (1+l_s)
+                l_filter_reci_rank += np.divide(1.0, (l_filter_s+1))
+                l_reci_rank += np.divide(1.0, (l_s+1))
+
+                #TYPE_C
+                if (l_filter_s_constrain < 10): l_filter_tot_constrain += 1
+                if (l_s_constrain < 10): l_tot_constrain += 1
+                if (l_filter_s_constrain < 3): l3_filter_tot_constrain += 1
+                if (l_s_constrain < 3): l3_tot_constrain += 1
+                if (l_filter_s_constrain < 1): l1_filter_tot_constrain += 1
+                if (l_s_constrain < 1): l1_tot_constrain += 1
+
+                l_filter_rank_constrain += (l_filter_s_constrain+1)
+                l_rank_constrain += (1+l_s_constrain)
+                l_filter_reci_rank_constrain += np.divide(1.0, (l_filter_s_constrain+1))
+                l_reci_rank_constrain += np.divide(1.0, (l_s_constrain+1))
+
+            if index == 0: sys.stdout.write("\r# of test triples processed: {}".format(i * self.N_THREADS_LP))
+
+            test_triples_done += 1
+
+
+            if test_triples_done % 100 == 0:
+                with open(self.test_log_path+"thread"+str(index), "w") as f:
+                    f.write(str(i)+'\n')
+
+                    f.write(str(r_filter_tot)+'\n')
+                    f.write(str(r_tot)+'\n')
+                    f.write(str(r3_filter_tot)+'\n')
+                    f.write(str(r3_tot)+'\n')
+                    f.write(str(r1_filter_tot)+'\n')
+                    f.write(str(r1_tot)+'\n')
+                    f.write(str(r_filter_rank)+'\n')
+                    f.write(str(r_rank)+'\n')
+                    f.write(str(r_filter_reci_rank)+'\n')
+                    f.write(str(r_reci_rank)+'\n')
+                    f.write(str(r_filter_tot_constrain)+'\n')
+                    f.write(str(r_tot_constrain)+'\n')
+                    f.write(str(r3_filter_tot_constrain)+'\n')
+                    f.write(str(r3_tot_constrain)+'\n')
+                    f.write(str(r1_filter_tot_constrain)+'\n')
+                    f.write(str(r1_tot_constrain)+'\n')
+                    f.write(str(r_filter_rank_constrain)+'\n')
+                    f.write(str(r_rank_constrain)+'\n')
+                    f.write(str(r_filter_reci_rank_constrain)+'\n')
+                    f.write(str(r_reci_rank_constrain)+'\n')
+
+                    if self.test_head != 0:
+                        f.write(str(l_filter_tot)+'\n')
+                        f.write(str(l_tot)+'\n')
+                        f.write(str(l3_filter_tot)+'\n')
+                        f.write(str(l3_tot)+'\n')
+                        f.write(str(l1_filter_tot)+'\n')
+                        f.write(str(l1_tot)+'\n')
+                        f.write(str(l_filter_rank)+'\n')
+                        f.write(str(l_rank)+'\n')
+                        f.write(str(l_filter_reci_rank)+'\n')
+                        f.write(str(l_reci_rank)+'\n')
+                        f.write(str(l_filter_tot_constrain)+'\n')
+                        f.write(str(l_tot_constrain)+'\n')
+                        f.write(str(l3_filter_tot_constrain)+'\n')
+                        f.write(str(l3_tot_constrain)+'\n')
+                        f.write(str(l1_filter_tot_constrain)+'\n')
+                        f.write(str(l1_tot_constrain)+'\n')
+                        f.write(str(l_filter_rank_constrain)+'\n')
+                        f.write(str(l_rank_constrain)+'\n')
+                        f.write(str(l_filter_reci_rank_constrain)+'\n')
+                        f.write(str(l_reci_rank_constrain)+'\n')
+
+
+        #tail
+        self.lp_res[index] = {'r_filter_tot':r_filter_tot,
+                              'r_tot':r_tot,
+                              'r3_filter_tot':r3_filter_tot,
+                              'r3_tot':r3_tot,
+                              'r1_filter_tot':r1_filter_tot,
+                              'r1_tot':r1_tot,
+
+                              'r_filter_rank':r_filter_rank,
+                              'r_rank':r_rank,
+                              'r_filter_reci_rank':r_filter_reci_rank,
+                              'r_reci_rank':r_reci_rank,
+
+                              'r_filter_tot_constrain':r_filter_tot_constrain,
+                              'r_tot_constrain':r_tot_constrain,
+                              'r3_filter_tot_constrain':r3_filter_tot_constrain,
+                              'r3_tot_constrain': r3_tot_constrain,
+                              'r1_filter_tot_constrain':r1_filter_tot_constrain,
+                              'r1_tot_constrain':r1_tot_constrain,
+
+                              'r_filter_rank_constrain':r_filter_rank_constrain,
+                              'r_rank_constrain':r_rank_constrain,
+                              'r_filter_reci_rank_constrain':r_filter_reci_rank_constrain,
+                              'r_reci_rank_constrain':r_reci_rank_constrain}
+
+        #head
+        if self.test_head != 0:
+            self.lp_res[index]['l_filter_tot'] = l_filter_tot
+            self.lp_res[index]['l_tot'] = l_tot
+            self.lp_res[index]['l3_filter_tot'] = l3_filter_tot
+            self.lp_res[index]['l3_tot'] = l3_tot
+            self.lp_res[index]['l1_filter_tot'] = l1_filter_tot
+            self.lp_res[index]['l1_tot'] = l1_tot
+
+            self.lp_res[index]['l_filter_rank'] = l_filter_rank
+            self.lp_res[index]['l_rank'] = l_rank
+            self.lp_res[index]['l_filter_reci_rank'] = l_filter_reci_rank
+            self.lp_res[index]['l_reci_rank'] = l_reci_rank
+
+            self.lp_res[index]['l_filter_tot_constrain'] = l_filter_tot_constrain
+            self.lp_res[index]['l_tot_constrain'] = l_tot_constrain
+            self.lp_res[index]['l3_filter_tot_constrain'] = l3_filter_tot_constrain
+            self.lp_res[index]['l3_tot_constrain'] = l3_tot_constrain
+            self.lp_res[index]['l1_filter_tot_constrain'] = l1_filter_tot_constrain
+            self.lp_res[index]['l1_tot_constrain'] = l1_tot_constrain
+
+            self.lp_res[index]['l_filter_rank_constrain'] = l_filter_rank_constrain
+            self.lp_res[index]['l_rank_constrain'] = l_rank_constrain
+            self.lp_res[index]['l_filter_reci_rank_constrain'] = l_filter_reci_rank_constrain
+            self.lp_res[index]['l_reci_rank_constrain'] = l_reci_rank_constrain
 
 
     def test(self):
@@ -495,22 +649,169 @@ def test(self):
             with self.sess.as_default():
                 if self.importName != None:
                     self.restore_tensorflow()
+
+                test_time_start = time.time()
+
                 if self.test_link_prediction:
-                    total = self.lib.getTestTotal()
-                    for times in range(total):
-                        self.lib.getHeadBatch(self.test_h_addr, self.test_t_addr, self.test_r_addr)
-                        res = self.test_step(self.test_h, self.test_t, self.test_r)
-                        self.lib.testHead(res.__array_interface__['data'][0])
-
-                        self.lib.getTailBatch(self.test_h_addr, self.test_t_addr, self.test_r_addr)
-                        res = self.test_step(self.test_h, self.test_t, self.test_r)
-                        self.lib.testTail(res.__array_interface__['data'][0])
-                        if self.log_on:
-                            #EDIT
-                            # print(times)
-                            sys.stdout.write("\r# of test triples processed: {}/{}".format(times, total))
-
-                    self.lib.test_link_prediction()
+                    l1_filter_tot = l1_tot = r1_tot = r1_filter_tot = l_tot = r_tot = l_filter_rank = l_rank = l_filter_reci_rank = l_reci_rank = 0.0
+                    l3_filter_tot = l3_tot = r3_tot = r3_filter_tot = l_filter_tot = r_filter_tot = r_filter_rank = r_rank = r_filter_reci_rank = r_reci_rank = 0.0
+                    #TYPE_C
+                    l1_filter_tot_constrain = l1_tot_constrain = r1_tot_constrain = r1_filter_tot_constrain = l_tot_constrain = r_tot_constrain = l_filter_rank_constrain = l_rank_constrain = l_filter_reci_rank_constrain = l_reci_rank_constrain = 0.0
+                    l3_filter_tot_constrain = l3_tot_constrain = r3_tot_constrain = r3_filter_tot_constrain = l_filter_tot_constrain = r_filter_tot_constrain = r_filter_rank_constrain = r_rank_constrain = r_filter_reci_rank_constrain = r_reci_rank_constrain = 0.0
+
+
+                    testTotal = self.lib.getTestTotal()
+                    triples_per_thread = int(testTotal / self.N_THREADS_LP)
+                    print("Number of test triples: {} Number of triples per test thread: {}".format(testTotal, triples_per_thread))
+                    threads_array = []
+
+                    lef = 0
+                    rig = 0
+                    for j in range(self.N_THREADS_LP):
+                        if j+1 == self.N_THREADS_LP:
+                            rig = testTotal
+                        else:
+                            rig += triples_per_thread
+                        threads_array.append(threading.Thread(target=self.test_lp_range, args=(j, lef, rig, )))
+                        lef = rig
+
+
+                    for t in threads_array:
+                        t.start()
+
+                    idx = 0
+                    for t in threads_array:
+                        t.join()
+
+                    for res in self.lp_res:
+                        #tail
+                        r_filter_tot += res['r_filter_tot']
+                        r_tot += res['r_tot']
+                        r3_filter_tot += res['r3_filter_tot']
+                        r3_tot += res['r3_tot']
+                        r1_filter_tot += res['r1_filter_tot']
+                        r1_tot += res['r1_tot']
+                        r_filter_rank += res['r_filter_rank']
+                        r_rank += res['r_rank']
+                        r_filter_reci_rank += res['r_filter_reci_rank']
+                        r_reci_rank += res['r_reci_rank']
+
+                        #tail TYPE_C
+                        r_filter_tot_constrain += res['r_filter_tot_constrain']
+                        r_tot_constrain += res['r_tot_constrain']
+                        r3_filter_tot_constrain += res['r3_filter_tot_constrain']
+                        r3_tot_constrain += res['r3_tot_constrain']
+                        r1_filter_tot_constrain += res['r1_filter_tot_constrain']
+                        r1_tot_constrain += res['r1_tot_constrain']
+                        r_filter_rank_constrain += res['r_filter_rank_constrain']
+                        r_rank_constrain += res['r_rank_constrain']
+                        r_filter_reci_rank_constrain += res['r_filter_reci_rank_constrain']
+                        r_reci_rank_constrain += res['r_reci_rank_constrain']
+
+                        if self.test_head != 0:
+                            #head
+                            l_filter_tot += res['l_filter_tot']
+                            l_tot += res['l_tot']
+                            l3_filter_tot += res['l3_filter_tot']
+                            l3_tot += res['l3_tot']
+                            l1_filter_tot += res['l1_filter_tot']
+                            l1_tot += res['l1_tot']
+                            l_filter_rank += res['l_filter_rank']
+                            l_rank += res['l_rank']
+                            l_filter_reci_rank += res['l_filter_reci_rank']
+                            l_reci_rank += res['l_reci_rank']
+
+                            #head TYPE_C
+                            l_filter_tot_constrain += res['l_filter_tot_constrain']
+                            l_tot_constrain += res['l_tot_constrain']
+                            l3_filter_tot_constrain += res['l3_filter_tot_constrain']
+                            l3_tot_constrain += res['l3_tot_constrain']
+                            l1_filter_tot_constrain += res['l1_filter_tot_constrain']
+                            l1_tot_constrain += res['l1_tot_constrain']
+                            l_filter_rank_constrain += res['l_filter_rank_constrain']
+                            l_rank_constrain += res['l_rank_constrain']
+                            l_filter_reci_rank_constrain += res['l_filter_reci_rank_constrain']
+                            l_reci_rank_constrain += res['l_reci_rank_constrain']
+
+
+                    #tail
+                    r_rank = np.divide(r_rank, testTotal)
+                    r_reci_rank = np.divide(r_reci_rank, testTotal)
+                    r_tot = np.divide(r_tot, testTotal)
+                    r3_tot = np.divide(r3_tot, testTotal)
+                    r1_tot = np.divide(r1_tot, testTotal)
+                    r_filter_rank = np.divide(r_filter_rank, testTotal)
+                    r_filter_reci_rank = np.divide(r_filter_reci_rank, testTotal)
+                    r_filter_tot = np.divide(r_filter_tot, testTotal)
+                    r3_filter_tot = np.divide(r3_filter_tot, testTotal)
+                    r1_filter_tot = np.divide(r1_filter_tot, testTotal)
+
+                    #TYPE_C
+                    r_rank_constrain = np.divide(r_rank_constrain, testTotal)
+                    r_reci_rank_constrain = np.divide(r_reci_rank_constrain, testTotal)
+                    r_tot_constrain = np.divide(r_tot_constrain, testTotal)
+                    r3_tot_constrain = np.divide(r3_tot_constrain, testTotal)
+                    r1_tot_constrain = np.divide(r1_tot_constrain, testTotal)
+                    r_filter_rank_constrain = np.divide(r_filter_rank_constrain, testTotal)
+                    r_filter_reci_rank_constrain = np.divide(r_filter_reci_rank_constrain, testTotal)
+                    r_filter_tot_constrain = np.divide(r_filter_tot_constrain, testTotal)
+                    r3_filter_tot_constrain = np.divide(r3_filter_tot_constrain, testTotal)
+                    r1_filter_tot_constrain = np.divide(r1_filter_tot_constrain, testTotal)
+
+                    if self.test_head != 0:
+                        #head
+                        l_rank = np.divide(l_rank, testTotal)
+                        l_reci_rank = np.divide(l_reci_rank, testTotal)
+                        l_tot = np.divide(l_tot, testTotal)
+                        l3_tot = np.divide(l3_tot, testTotal)
+                        l1_tot = np.divide(l1_tot, testTotal)
+                        l_filter_rank = np.divide(l_filter_rank, testTotal)
+                        l_filter_reci_rank = np.divide(l_filter_reci_rank, testTotal)
+                        l_filter_tot = np.divide(l_filter_tot, testTotal)
+                        l3_filter_tot = np.divide(l3_filter_tot, testTotal)
+                        l1_filter_tot = np.divide(l1_filter_tot, testTotal)
+
+                        #TYPE_C
+                        l_rank_constrain = np.divide(l_rank_constrain, testTotal)
+                        l_reci_rank_constrain = np.divide(l_reci_rank_constrain, testTotal)
+                        l_tot_constrain = np.divide(l_tot_constrain, testTotal)
+                        l3_tot_constrain = np.divide(l3_tot_constrain, testTotal)
+                        l1_tot_constrain = np.divide(l1_tot_constrain, testTotal)
+                        l_filter_rank_constrain = np.divide(l_filter_rank_constrain, testTotal)
+                        l_filter_reci_rank_constrain = np.divide(l_filter_reci_rank_constrain, testTotal)
+                        l_filter_tot_constrain = np.divide(l_filter_tot_constrain, testTotal)
+                        l3_filter_tot_constrain = np.divide(l3_filter_tot_constrain, testTotal)
+                        l1_filter_tot_constrain = np.divide(l1_filter_tot_constrain, testTotal)
+
+
+                    print("\n ========== LINK PREDICTION RESULTS ==========\nNo type constraint results:")
+                    print("{:<20}{:<20}{:<20}{:<20}{:<20}{:<20}".format("metric", "MRR", "MR", "hit@10", "hit@3", "hit@1"))
+                    if self.test_head != 0: print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}".format("l(raw):",l_reci_rank, l_rank, l_tot, l3_tot, l1_tot))
+                    print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}".format("r(raw):", r_reci_rank, r_rank, r_tot, r3_tot, r1_tot))
+                    if self.test_head != 0: print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}\n".format("mean(raw):", np.divide((l_reci_rank+r_reci_rank),2), np.divide((l_rank+r_rank),2), np.divide((l_tot+r_tot),2), np.divide((l3_tot+r3_tot),2), np.divide((l1_tot+r1_tot),2)))
+
+                    if self.test_head != 0: print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}".format("l(filter):", l_filter_reci_rank, l_filter_rank, l_filter_tot, l3_filter_tot, l1_filter_tot))
+                    print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}".format('r(filter):', r_filter_reci_rank, r_filter_rank, r_filter_tot, r3_filter_tot, r1_filter_tot))
+                    if self.test_head != 0: print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}\n".format('mean(filter):', np.divide((l_filter_reci_rank+r_filter_reci_rank),2), np.divide((l_filter_rank+r_filter_rank),2), np.divide((l_filter_tot+r_filter_tot),2), np.divide((l3_filter_tot+r3_filter_tot),2), np.divide((l1_filter_tot+r1_filter_tot),2)))
+
+                    print("Type constraint results:")
+                    print("{:<20}{:<20}{:<20}{:<20}{:<20}{:<20}".format("metric", "MRR", "MR", "hit@10", "hit@3", "hit@1"))
+                    if self.test_head != 0: print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}".format('l(raw):', l_reci_rank_constrain, l_rank_constrain, l_tot_constrain, l3_tot_constrain, l1_tot_constrain))
+                    print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}".format('r(raw):', r_reci_rank_constrain, r_rank_constrain, r_tot_constrain, r3_tot_constrain, r1_tot_constrain))
+                    if self.test_head != 0: print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}\n".format('mean(raw):', np.divide((l_reci_rank_constrain+r_reci_rank_constrain),2), np.divide((l_rank_constrain+r_rank_constrain),2), np.divide((l_tot_constrain+r_tot_constrain),2), np.divide((l3_tot_constrain+r3_tot_constrain),2), np.divide((l1_tot_constrain+r1_tot_constrain),2)))
+
+                    if self.test_head != 0: print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}".format('l(filter):', l_filter_reci_rank_constrain, l_filter_rank_constrain, l_filter_tot_constrain, l3_filter_tot_constrain, l1_filter_tot_constrain))
+                    print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}".format('r(filter):', r_filter_reci_rank_constrain, r_filter_rank_constrain, r_filter_tot_constrain, r3_filter_tot_constrain, r1_filter_tot_constrain))
+                    if self.test_head != 0: print("{:<20}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}{:<20.5f}\n".format('mean(filter):', np.divide((l_filter_reci_rank_constrain+r_filter_reci_rank_constrain),2), np.divide((l_filter_rank_constrain+r_filter_rank_constrain),2), np.divide((l_filter_tot_constrain+r_filter_tot_constrain),2), np.divide((l3_filter_tot_constrain+r3_filter_tot_constrain),2), np.divide((l1_filter_tot_constrain+r1_filter_tot_constrain),2)))
+
+                    #remove test checkpoint
+                    for index in range(0, self.N_THREADS_LP):
+                        try:
+                            os.remove(self.test_log_path+"thread"+str(index))
+                        except:
+                            print("File " + self.test_log_path+"thread"+str(index) + " not founded")
+
+
                 if self.test_triple_classification:
                     self.lib.getValidBatch(self.valid_pos_h_addr, self.valid_pos_t_addr, self.valid_pos_r_addr, self.valid_neg_h_addr, self.valid_neg_t_addr, self.valid_neg_r_addr)
                     res_pos = self.test_step(self.valid_pos_h, self.valid_pos_t, self.valid_pos_r)
@@ -521,9 +822,16 @@ def test(self):
 
                     res_pos = self.test_step(self.test_pos_h, self.test_pos_t, self.test_pos_r)
                     res_neg = self.test_step(self.test_neg_h, self.test_neg_t, self.test_neg_r)
-                    #EDIT
+
                     self.lib.test_triple_classification(self.relThresh_addr, res_pos.__array_interface__['data'][0], res_neg.__array_interface__['data'][0], self.acc_addr)
 
+                test_time_elapsed = time.time() - test_time_start
+                print("\nElapsed test time (seconds): {}".format(test_time_elapsed))
+
+
+
+
+
     #EDIT
     def valid(self):
         with self.graph.as_default():
diff --git a/OpenKE_new_Spark.iml b/OpenKEonSpark.iml
similarity index 87%
rename from OpenKE_new_Spark.iml
rename to OpenKEonSpark.iml
index 8021953..ad3c0a3 100644
--- a/OpenKE_new_Spark.iml
+++ b/OpenKEonSpark.iml
@@ -1,5 +1,5 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<module type="WEB_MODULE" version="4">
+<module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager" inherit-compiler-output="true">
     <exclude-output />
     <content url="file://$MODULE_DIR$" />
diff --git a/__pycache__/Config.cpython-36.pyc b/__pycache__/Config.cpython-36.pyc
index 5955a9b6ef33f4dcab319e8f053f1f873315b029..29f68de91cf8bdaa92028abbebbcd82131e34e7b 100644
GIT binary patch
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diff --git a/__pycache__/distribute_training.cpython-36.pyc b/__pycache__/distribute_training.cpython-36.pyc
index 11404a037fc65a7986d6980ebf501973e987b490..a98f3332c69300497bde0bf87ad1292e12852ca7 100644
GIT binary patch
delta 2457
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delta 2310
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diff --git a/base/Base.cpp b/base/Base.cpp
index dd21094..914e71f 100644
--- a/base/Base.cpp
+++ b/base/Base.cpp
@@ -48,10 +48,6 @@ INT getTrainTotal_();
 extern "C"
 INT getBatchTotal();
 
-//EDIT
-extern "C"
-void setConstrainFlag(INT con);
-
 extern "C"
 void randReset();
 
@@ -130,7 +126,6 @@ void* getBatch(void* con) {
 
 		}
 
-
 		for (INT times = 0; times < negRelRate; times++) {
 			batch_h[batch + last] = trainList_no[i].h;
 			batch_t[batch + last] = trainList_no[i].t;
diff --git a/base/Setting.h b/base/Setting.h
index 012e500..53207d7 100644
--- a/base/Setting.h
+++ b/base/Setting.h
@@ -108,20 +108,11 @@ INT getValidTotal() {
 */
 
 INT bernFlag = 0;
-//EDIT
-INT constrainFlag = 0;
 
 extern "C"
 void setBern(INT con) {
 	bernFlag = con;
 }
 
-//EDIT
-extern "C"
-void setConstrainFlag(INT con) {
-	constrainFlag = con;
-}
-
-
 
 #endif
diff --git a/base/Test.h b/base/Test.h
index 5de05a2..3218c10 100644
--- a/base/Test.h
+++ b/base/Test.h
@@ -7,66 +7,41 @@
 /*=====================================================================================
 link prediction
 ======================================================================================*/
-INT lastHead = 0;
-INT lastTail = 0;
-REAL l1_filter_tot = 0, l1_tot = 0, r1_tot = 0, r1_filter_tot = 0, l_tot = 0, r_tot = 0, l_filter_rank = 0, l_rank = 0, l_filter_reci_rank = 0, l_reci_rank = 0;
-REAL l3_filter_tot = 0, l3_tot = 0, r3_tot = 0, r3_filter_tot = 0, l_filter_tot = 0, r_filter_tot = 0, r_filter_rank = 0, r_rank = 0, r_filter_reci_rank = 0, r_reci_rank = 0;
-
-//TYPE_C
-REAL l1_filter_tot_constrain = 0, l1_tot_constrain = 0, r1_tot_constrain = 0, r1_filter_tot_constrain = 0, l_tot_constrain = 0, r_tot_constrain = 0, l_filter_rank_constrain = 0, l_rank_constrain = 0, l_filter_reci_rank_constrain = 0, l_reci_rank_constrain = 0;
-REAL l3_filter_tot_constrain = 0, l3_tot_constrain = 0, r3_tot_constrain = 0, r3_filter_tot_constrain = 0, l_filter_tot_constrain = 0, r_filter_tot_constrain = 0, r_filter_rank_constrain = 0, r_rank_constrain = 0, r_filter_reci_rank_constrain = 0, r_reci_rank_constrain = 0;
-
 extern "C"
-void initTest() {
-    lastHead = 0;
-    lastTail = 0;
-
-    l1_filter_tot = 0, l1_tot = 0, r1_tot = 0, r1_filter_tot = 0, l_tot = 0, r_tot = 0, l_filter_rank = 0, l_rank = 0, l_filter_reci_rank = 0, l_reci_rank = 0;
-    l3_filter_tot = 0, l3_tot = 0, r3_tot = 0, r3_filter_tot = 0, l_filter_tot = 0, r_filter_tot = 0, r_filter_rank = 0, r_rank = 0, r_filter_reci_rank = 0, r_reci_rank = 0;
-
-    //TYPE_C
-    l1_filter_tot_constrain = 0, l1_tot_constrain = 0, r1_tot_constrain = 0, r1_filter_tot_constrain = 0, l_tot_constrain = 0, r_tot_constrain = 0, l_filter_rank_constrain = 0, l_rank_constrain = 0, l_filter_reci_rank_constrain = 0, l_reci_rank_constrain = 0;
-    l3_filter_tot_constrain = 0, l3_tot_constrain = 0, r3_tot_constrain = 0, r3_filter_tot_constrain = 0, l_filter_tot_constrain = 0, r_filter_tot_constrain = 0, r_filter_rank_constrain = 0, r_rank_constrain = 0, r_filter_reci_rank_constrain = 0, r_reci_rank_constrain = 0;
-}
-
-
-extern "C"
-void getHeadBatch(INT *ph, INT *pt, INT *pr) {
+void getHeadBatch(INT index, INT *ph, INT *pt, INT *pr) {
     for (INT i = 0; i < entityTotal; i++) {
         ph[i] = i;
-        pt[i] = testList[lastHead].t;
-        pr[i] = testList[lastHead].r;
+        pt[i] = testList[index].t;
+        pr[i] = testList[index].r;
     }
 }
 
 extern "C"
-void getTailBatch(INT *ph, INT *pt, INT *pr) {
+void getTailBatch(INT index, INT *ph, INT *pt, INT *pr) {
     for (INT i = 0; i < entityTotal; i++) {
-        ph[i] = testList[lastTail].h;
+        ph[i] = testList[index].h;
         pt[i] = i;
-        pr[i] = testList[lastTail].r;
+        pr[i] = testList[index].r;
     }
 }
 
 extern "C"
-void testHead(REAL *con) {
-    INT h = testList[lastHead].h;
-    INT t = testList[lastHead].t;
-    INT r = testList[lastHead].r;
+INT* testHead(INT index, REAL *con) {
+    INT h = testList[index].h;
+    INT t = testList[index].t;
+    INT r = testList[index].r;
     REAL minimal = con[h];
+
+    INT* l_arr = new INT[4];
     INT l_s = 0;
     INT l_filter_s = 0;
-
-
-    //TYPE_C
     INT l_s_constrain = 0;
     INT l_filter_s_constrain = 0;
+
     INT lef = 0;
     INT rig = 0;
-    if (constrainFlag != 0){
-        lef = head_lef[r];
-        rig = head_rig[r];
-    }
+    lef = head_lef[r];
+    rig = head_rig[r];
 
     for (INT j = 0; j < entityTotal; j++) {
         if (j != h) {
@@ -79,72 +54,46 @@ void testHead(REAL *con) {
             }
 
             //TYPE_C
-            if (constrainFlag != 0){
-                while (lef < rig && head_type[lef] < j) lef ++;
-                if (lef < rig && j == head_type[lef]) {
-                    if (value < minimal) {
-                        l_s_constrain += 1;
-                        if (not _find(j, t, r)) {
-                            l_filter_s_constrain += 1;
-                        }
+            while (lef < rig && head_type[lef] < j) lef ++;
+            if (lef < rig && j == head_type[lef]) {
+                if (value < minimal) {
+                    l_s_constrain += 1;
+                    if (not _find(j, t, r)) {
+                        l_filter_s_constrain += 1;
                     }
                 }
             }
-
         }
     }
 
-    if (l_filter_s < 10) l_filter_tot += 1;
-    if (l_s < 10) l_tot += 1;
-    if (l_filter_s < 3) l3_filter_tot += 1;
-    if (l_s < 3) l3_tot += 1;
-    if (l_filter_s < 1) l1_filter_tot += 1;
-    if (l_s < 1) l1_tot += 1;
-
-    //TYPE_C
-    if (constrainFlag != 0){
-        if (l_filter_s_constrain < 10) l_filter_tot_constrain += 1;
-        if (l_s_constrain < 10) l_tot_constrain += 1;
-        if (l_filter_s_constrain < 3) l3_filter_tot_constrain += 1;
-        if (l_s_constrain < 3) l3_tot_constrain += 1;
-        if (l_filter_s_constrain < 1) l1_filter_tot_constrain += 1;
-        if (l_s_constrain < 1) l1_tot_constrain += 1;
-    }
+    l_arr[0] = l_s;
+    l_arr[1] = l_filter_s;
+    l_arr[2] = l_s_constrain;
+    l_arr[3] = l_filter_s_constrain;
 
-    l_filter_rank += (l_filter_s+1);
-    l_rank += (1+l_s);
-    l_filter_reci_rank += 1.0/(l_filter_s+1);
-    l_reci_rank += 1.0/(l_s+1);
-
-    //TYPE_C
-    if (constrainFlag != 0){
-        l_filter_rank_constrain += (l_filter_s_constrain+1);
-        l_rank_constrain += (1+l_s_constrain);
-        l_filter_reci_rank_constrain += 1.0/(l_filter_s_constrain+1);
-        l_reci_rank_constrain += 1.0/(l_s_constrain+1);
-    }
+    return l_arr;
 
-    lastHead++;
 }
 
+
+
 extern "C"
-void testTail(REAL *con) {
-    INT h = testList[lastTail].h;
-    INT t = testList[lastTail].t;
-    INT r = testList[lastTail].r;
+INT* testTail(INT index, REAL *con) {
+    INT h = testList[index].h;
+    INT t = testList[index].t;
+    INT r = testList[index].r;
     REAL minimal = con[t];
+
+    INT* r_arr = new INT[4];
     INT r_s = 0;
     INT r_filter_s = 0;
-
-    //TYPE_C
     INT r_s_constrain = 0;
     INT r_filter_s_constrain = 0;
+
     INT lef = 0;
     INT rig = 0;
-    if (constrainFlag != 0){
-        lef = tail_lef[r];
-        rig = tail_rig[r];
-    }
+    lef = tail_lef[r];
+    rig = tail_rig[r];
 
     for (INT j = 0; j < entityTotal; j++) {
         if (j != t) {
@@ -157,139 +106,29 @@ void testTail(REAL *con) {
             }
 
             //TYPE_C
-            if (constrainFlag != 0){
-                while (lef < rig && tail_type[lef] < j) lef ++;
-                if (lef < rig && j == tail_type[lef]) {
-                        if (value < minimal) {
-                            r_s_constrain += 1;
-                            if (not _find(h, j ,r)) {
-                                r_filter_s_constrain += 1;
-                            }
+            while (lef < rig && tail_type[lef] < j) lef ++;
+            if (lef < rig && j == tail_type[lef]) {
+                    if (value < minimal) {
+                        r_s_constrain += 1;
+                        if (not _find(h, j ,r)) {
+                            r_filter_s_constrain += 1;
                         }
-                }
+                    }
             }
 
-        }
-    }
 
-    if (r_filter_s < 10) r_filter_tot += 1;
-    if (r_s < 10) r_tot += 1;
-    if (r_filter_s < 3) r3_filter_tot += 1;
-    if (r_s < 3) r3_tot += 1;
-    if (r_filter_s < 1) r1_filter_tot += 1;
-    if (r_s < 1) r1_tot += 1;
-
-    //TYPE_C
-    if (constrainFlag != 0){
-        if (r_filter_s_constrain < 10) r_filter_tot_constrain += 1;
-        if (r_s_constrain < 10) r_tot_constrain += 1;
-        if (r_filter_s_constrain < 3) r3_filter_tot_constrain += 1;
-        if (r_s_constrain < 3) r3_tot_constrain += 1;
-        if (r_filter_s_constrain < 1) r1_filter_tot_constrain += 1;
-        if (r_s_constrain < 1) r1_tot_constrain += 1;
+        }
     }
 
-    r_filter_rank += (1+r_filter_s);
-    r_rank += (1+r_s);
-    r_filter_reci_rank += 1.0/(1+r_filter_s);
-    r_reci_rank += 1.0/(1+r_s);
-
-    //TYPE_C
-    if (constrainFlag != 0){
-        r_filter_rank_constrain += (1+r_filter_s_constrain);
-        r_rank_constrain += (1+r_s_constrain);
-        r_filter_reci_rank_constrain += 1.0/(1+r_filter_s_constrain);
-        r_reci_rank_constrain += 1.0/(1+r_s_constrain);
-    }
+    r_arr[0] = r_s;
+    r_arr[1] = r_filter_s;
+    r_arr[2] = r_s_constrain;
+    r_arr[3] = r_filter_s_constrain;
 
-    lastTail++;
+    return r_arr;
 }
 
-extern "C"
-void test_link_prediction() {
-    l_rank /= testTotal;
-    r_rank /= testTotal;
-    l_reci_rank /= testTotal;
-    r_reci_rank /= testTotal;
- 
-    l_tot /= testTotal;
-    l3_tot /= testTotal;
-    l1_tot /= testTotal;
- 
-    r_tot /= testTotal;
-    r3_tot /= testTotal;
-    r1_tot /= testTotal;
-
-    // with filter
-    l_filter_rank /= testTotal;
-    r_filter_rank /= testTotal;
-    l_filter_reci_rank /= testTotal;
-    r_filter_reci_rank /= testTotal;
-
-    l_filter_tot /= testTotal;
-    l3_filter_tot /= testTotal;
-    l1_filter_tot /= testTotal;
-
-    r_filter_tot /= testTotal;
-    r3_filter_tot /= testTotal;
-    r1_filter_tot /= testTotal;
-
-    printf("no type constraint results:\n");
-    
-    printf("metric:\t\t\t MRR \t\t MR \t\t hit@10 \t hit@3  \t hit@1 \n");
-    printf("l(raw):\t\t\t %f \t %f \t %f \t %f \t %f \n", l_reci_rank, l_rank, l_tot, l3_tot, l1_tot);
-    printf("r(raw):\t\t\t %f \t %f \t %f \t %f \t %f \n", r_reci_rank, r_rank, r_tot, r3_tot, r1_tot);
-    printf("averaged(raw):\t\t %f \t %f \t %f \t %f \t %f \n",
-            (l_reci_rank+r_reci_rank)/2, (l_rank+r_rank)/2, (l_tot+r_tot)/2, (l3_tot+r3_tot)/2, (l1_tot+r1_tot)/2);
-    printf("\n");
-    printf("l(filter):\t\t %f \t %f \t %f \t %f \t %f \n", l_filter_reci_rank, l_filter_rank, l_filter_tot, l3_filter_tot, l1_filter_tot);
-    printf("r(filter):\t\t %f \t %f \t %f \t %f \t %f \n", r_filter_reci_rank, r_filter_rank, r_filter_tot, r3_filter_tot, r1_filter_tot);
-    printf("averaged(filter):\t %f \t %f \t %f \t %f \t %f \n",
-            (l_filter_reci_rank+r_filter_reci_rank)/2, (l_filter_rank+r_filter_rank)/2, (l_filter_tot+r_filter_tot)/2, (l3_filter_tot+r3_filter_tot)/2, (l1_filter_tot+r1_filter_tot)/2);
-
-    //TYPE_C
-    if (constrainFlag != 0){
-        l_rank_constrain /= testTotal;
-        r_rank_constrain /= testTotal;
-        l_reci_rank_constrain /= testTotal;
-        r_reci_rank_constrain /= testTotal;
-
-        l_tot_constrain /= testTotal;
-        l3_tot_constrain /= testTotal;
-        l1_tot_constrain /= testTotal;
-
-        r_tot_constrain /= testTotal;
-        r3_tot_constrain /= testTotal;
-        r1_tot_constrain /= testTotal;
-
-        //TYPE_C with filter
-        l_filter_rank_constrain /= testTotal;
-        r_filter_rank_constrain /= testTotal;
-        l_filter_reci_rank_constrain /= testTotal;
-        r_filter_reci_rank_constrain /= testTotal;
-
-        l_filter_tot_constrain /= testTotal;
-        l3_filter_tot_constrain /= testTotal;
-        l1_filter_tot_constrain /= testTotal;
-
-        r_filter_tot_constrain /= testTotal;
-        r3_filter_tot_constrain /= testTotal;
-        r1_filter_tot_constrain /= testTotal;
-
-        printf("type constraint results:\n");
-
-        printf("metric:\t\t\t MRR \t\t MR \t\t hit@10 \t hit@3  \t hit@1 \n");
-        printf("l(raw):\t\t\t %f \t %f \t %f \t %f \t %f \n", l_reci_rank_constrain, l_rank_constrain, l_tot_constrain, l3_tot_constrain, l1_tot_constrain);
-        printf("r(raw):\t\t\t %f \t %f \t %f \t %f \t %f \n", r_reci_rank_constrain, r_rank_constrain, r_tot_constrain, r3_tot_constrain, r1_tot_constrain);
-        printf("averaged(raw):\t\t %f \t %f \t %f \t %f \t %f \n",
-                (l_reci_rank_constrain+r_reci_rank_constrain)/2, (l_rank_constrain+r_rank_constrain)/2, (l_tot_constrain+r_tot_constrain)/2, (l3_tot_constrain+r3_tot_constrain)/2, (l1_tot_constrain+r1_tot_constrain)/2);
-        printf("\n");
-        printf("l(filter):\t\t %f \t %f \t %f \t %f \t %f \n", l_filter_reci_rank_constrain, l_filter_rank_constrain, l_filter_tot_constrain, l3_filter_tot_constrain, l1_filter_tot_constrain);
-        printf("r(filter):\t\t %f \t %f \t %f \t %f \t %f \n", r_filter_reci_rank_constrain, r_filter_rank_constrain, r_filter_tot_constrain, r3_filter_tot_constrain, r1_filter_tot_constrain);
-        printf("averaged(filter):\t %f \t %f \t %f \t %f \t %f \n",
-                (l_filter_reci_rank_constrain+r_filter_reci_rank_constrain)/2, (l_filter_rank_constrain+r_filter_rank_constrain)/2, (l_filter_tot_constrain+r_filter_tot_constrain)/2, (l3_filter_tot_constrain+r3_filter_tot_constrain)/2, (l1_filter_tot_constrain+r1_filter_tot_constrain)/2);
-    }
-}
+
 
 /*=====================================================================================
 triple classification
diff --git a/commands.txt b/commands.txt
new file mode 100644
index 0000000..2f75c2a
--- /dev/null
+++ b/commands.txt
@@ -0,0 +1,46 @@
+#commands to run OpenKEonSpark on Google Colaboratory platform
+
+#install
+!sudo apt-get update 
+!sudo apt-get upgrade
+!sudo apt-get install w3m w3m-img
+!sudo apt-get install net-tools
+!apt-get install openjdk-8-jdk-headless -qq > /dev/null
+import os
+os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
+!sudo apt-get install scala
+!wget -q https://archive.apache.org/dist/spark/spark-2.1.1/spark-2.1.1-bin-hadoop2.7.tgz 
+!tar xf spark-2.1.1-bin-hadoop2.7.tgz
+!python3 -m pip install tensorflowonspark py4j pyspark
+
+#connect Google Drive
+from google.colab import drive
+drive.mount('/content/drive')
+
+#unzip project
+!unzip /content/drive/My\ Drive/DBpedia/OpenKEonSpark.zip
+!mv OpenKEonSpark/gpu_info.py /usr/local/lib/python3.6/dist-packages/tensorflowonspark/gpu_info.py
+
+#unzip dataset
+!unzip /content/drive/My\ Drive/DBpedia/5.zip -d /content/drive/My\ Drive/DBpedia/
+!unzip /content/drive/My\ Drive/DBpedia/10.zip -d /content/drive/My\ Drive/DBpedia/
+!unzip /content/drive/My\ Drive/DBpedia/15.zip -d /content/drive/My\ Drive/DBpedia/
+
+#set env vars
+import os
+os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
+os.environ['SPARK_WORKER_INSTANCES'] = '3'
+os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
+os.environ["CUDA_VISIBLE_DEVICES"]="0"
+os.environ["CORES_PER_WORKER"] = "1"
+os.environ["MEMORY_PER_WORKER"] = "4g"
+os.environ["LIB_CUDA"] = "/usr/local/cuda-10.0/lib64"
+os.environ["WORK_DIR_PREFIX"] = "/content/OpenKEonSpark"
+os.environ["SPARK_HOME"] = "/content/spark-2.1.1-bin-hadoop2.7"
+
+#execute
+!bash $WORK_DIR_PREFIX/run_dbpedia_5.sh
+
+
+
+
diff --git a/distribute_training.py b/distribute_training.py
index caa5b46..67f74b1 100644
--- a/distribute_training.py
+++ b/distribute_training.py
@@ -7,9 +7,9 @@
 from tensorflowonspark import TFNode
 import os
 import numpy as np
-from tensorflow.python.training import session_run_hook
 from tensorflow.contrib.training.python.training.device_setter import GreedyLoadBalancingStrategy, byte_size_load_fn
 import sys
+import time
 
 
 #if set to Ture prints additional debug information
@@ -33,11 +33,10 @@ def get_conf_to_update_model(output_path):
 def get_conf(argv=None):
     if argv == None: argv = sys.argv
 
-    con = Config()
+    con = Config(cpp_lib_path=argv.cpp_lib_path)
     con.set_in_path(argv.input_path)
     con.set_export_files(argv.output_path)
     con.set_valid_triple_classification(True)        #to perform early stop on validation accuracy
-    # con.set_work_threads(argv.working_threads)
     con.set_train_times(argv.train_times)
     con.set_nbatches(argv.n_batches)
     con.set_alpha(argv.alpha)
@@ -91,6 +90,8 @@ def create_model(con):
         else:
             optimizer = tf.train.GradientDescentOptimizer(con.alpha)
 
+
+
         global_step = tf.train.get_or_create_global_step()
         train_op = optimizer.minimize(trainModel.loss, global_step=global_step)
         init_op = tf.initialize_all_variables()
@@ -122,6 +123,8 @@ def get_last_step():
         return last_global_step
 
 
+
+
 def main_fun(argv, ctx):
     job_name = ctx.job_name
     task_index = ctx.task_index
@@ -160,127 +163,139 @@ def main_fun(argv, ctx):
 
 
 
+
         if DEBUG: print("Creating Hooks, Scaffold, FileWriter...")
 
         iterations = con.train_times * con.nbatches + last_global_step
+
         hooks=[tf.train.StopAtStepHook(last_step=iterations)]
         scaffold = tf.train.Scaffold(init_op=init_op, saver=saver, summary_op=summary_op)
         tf.summary.FileWriter("tensorboard_%d" % ctx.worker_num, graph=tf.get_default_graph())
 
 
+        print("Num gpus: " + str(argv.num_gpus))
+        gpu_options = tf.GPUOptions(allow_growth = True, visible_device_list = "0")
+        config_monitored = tf.ConfigProto(gpu_options=gpu_options)
+
+
         if DEBUG: print("Starting MonitoredTrainingSession...")
-        with tf.train.MonitoredTrainingSession(master=server.target,
+        sess = tf.train.MonitoredTrainingSession(master=server.target,
                                                is_chief=(task_index == 0),
                                                scaffold=scaffold,
+                                               config=config_monitored,
                                                checkpoint_dir=argv.output_path,
                                                save_summaries_steps=1,
                                                save_checkpoint_steps=1*con.nbatches,
                                                hooks=hooks,
                                                summary_dir="tensorboard_%d" % ctx.worker_num
-                                               ) as sess:
-            if DEBUG:
-                print("Monitoring training sessions started")
-                print("Task index is: {}".format(task_index))
-
-            #init local worker vars
-            best_acc = np.finfo('float32').min
-            best_loss = np.finfo('float32').max
-            wait_steps_acc = 0
-            wait_steps_loss = 0
-            best_model_global_step_loss = 0
-            best_model_global_step_acc = 0
-            patience = sys.argv.early_stop_patience
-            stopping_step = sys.argv.early_stop_stopping_step * con.nbatches
-            to_reach_step = sys.argv.early_stop_start_step * con.nbatches + last_global_step
-            con.lib.getValidBatch(con.valid_pos_h_addr, con.valid_pos_t_addr, con.valid_pos_r_addr, con.valid_neg_h_addr, con.valid_neg_t_addr, con.valid_neg_r_addr)
-
-            while not sess.should_stop():
-                con.sampling()
-
-                feed_dict = {
-                    trainModel.batch_h: con.batch_h,
-                    trainModel.batch_t: con.batch_t,
-                    trainModel.batch_r: con.batch_r,
-                    trainModel.batch_y: con.batch_y
-                }
-
-                _, loss, g = sess.run([train_op, trainModel.loss, global_step], feed_dict)
-                print('Global step: {} Epoch: {} Batch: {} loss: {}'.format(g, int( (g-last_global_step) /con.nbatches), int( (g - last_global_step) % con.nbatches), loss))
-
-
-                ################## EARLY STOP ##################
-                if (task_index != 0) and (g >= to_reach_step):
-                    to_reach_step += stopping_step
-                    if os.path.exists(sys.argv.output_path+"/stop.txt"):
-                        print('\nEarly stop happened in chief worker\n')
-                        break
-
-
-                if (task_index == 0) and (not sess.should_stop()) and (g >= to_reach_step):
-                    to_reach_step += stopping_step
-
-                    ################## ACCURACY ##################
-                    feed_dict[trainModel.predict_h] = con.valid_pos_h
-                    feed_dict[trainModel.predict_t] = con.valid_pos_t
-                    feed_dict[trainModel.predict_r] = con.valid_pos_r
-                    res_pos = sess.run(trainModel.predict, feed_dict)
-
-                    feed_dict[trainModel.predict_h] = con.valid_neg_h
-                    feed_dict[trainModel.predict_t] = con.valid_neg_t
-                    feed_dict[trainModel.predict_r] = con.valid_neg_r
-                    res_neg = sess.run(trainModel.predict, feed_dict)
-
-                    con.lib.getBestThreshold(con.relThresh_addr, res_pos.__array_interface__['data'][0], res_neg.__array_interface__['data'][0])
-                    con.lib.test_triple_classification(con.relThresh_addr, res_pos.__array_interface__['data'][0], res_neg.__array_interface__['data'][0], con.acc_addr)
-                    acc = con.acc[0]
-
-                    if DEBUG:
-                        print("\n[ Early Stop Check (Accuracy) ]")
-                        print("Best Accuracy = %.10f" %(best_acc))
-                        print("Accuracy after run  =  %.10f" %(acc))
-
-                    if acc > best_acc:
-                        best_acc = acc
-                        wait_steps_acc = 0
-                        if DEBUG: print("New best Accuracy founded. Wait steps reset.")
-                        best_model_global_step_acc = g
-
-                    elif wait_steps_acc < patience:
-                        wait_steps_acc += 1
-                        if DEBUG: print("Wait steps Accuracy incremented: {}\n".format(wait_steps_acc))
-
-
-                    if wait_steps_acc >= patience:
-                        print('Accuracy early stop. Accuracy has not been improved enough in {} times'.format(patience))
-                        with open(sys.argv.output_path+"/stop.txt", "w") as stop_file:
-                            stop_file.write(str(best_model_global_step_acc)+"\n")
-                        break
-
-
-                    ################## LOSS ##################
-                    if DEBUG:
-                        print("\n[ Early Stop Checking (Loss) ]")
-                        print("Best loss = %.10f" %(best_loss))
-                        print("Loss after run  =  %.10f" %(loss))
-
-                    if loss < best_loss:
-                        best_loss = loss
-                        wait_steps_loss = 0
-                        if DEBUG: print("New best loss founded. Wait steps reset.")
-                        best_model_global_step_loss = g
-
-                    elif wait_steps_loss < patience:
-                        wait_steps_loss += 1
-                        if DEBUG: print("Wait steps loss incremented: {}\n".format(wait_steps_loss))
-
-
-                    if wait_steps_loss >= patience:
-                        print('Loss early stop. Losses has not been improved enough in {} times'.format(patience))
-                        with open(sys.argv.output_path+"/stop.txt", "w") as stop_file:
-                            stop_file.write(str(best_model_global_step_loss)+"\n")
-                        break
-
-        if DEBUG: print("Monitoring training sessions should stop now")
+                                               )
+        if DEBUG:
+            print("Monitoring training sessions started")
+            print("Task index is: {}".format(task_index))
+
+        #init local worker vars
+        best_acc = np.finfo('float32').min
+        best_loss = np.finfo('float32').max
+        wait_steps_acc = 0
+        wait_steps_loss = 0
+        best_model_global_step_loss = 0
+        best_model_global_step_acc = 0
+        patience = sys.argv.early_stop_patience
+        stopping_step = sys.argv.early_stop_stopping_step * con.nbatches
+        to_reach_step = sys.argv.early_stop_start_step * con.nbatches + last_global_step
+        con.lib.getValidBatch(con.valid_pos_h_addr, con.valid_pos_t_addr, con.valid_pos_r_addr, con.valid_neg_h_addr, con.valid_neg_t_addr, con.valid_neg_r_addr)
+        local = 0
+
+        while not sess.should_stop():
+            if task_index == 0 and local == 0:
+                time.sleep(60)
+            local += 1
+
+            con.sampling()
+
+            feed_dict = {
+                trainModel.batch_h: con.batch_h,
+                trainModel.batch_t: con.batch_t,
+                trainModel.batch_r: con.batch_r,
+                trainModel.batch_y: con.batch_y
+            }
+
+            _, loss, g = sess.run([train_op, trainModel.loss, global_step], feed_dict)
+            print('Global step: {} Epoch: {} Batch: {} loss: {}'.format(g, int( (g-last_global_step) /con.nbatches), int( (g - last_global_step) % con.nbatches), loss))
+
+
+            ################## EARLY STOP ##################
+            if (task_index != 0) and (g >= to_reach_step):
+                to_reach_step += stopping_step
+                if os.path.exists(sys.argv.output_path+"/stop.txt"):
+                    print('\nEarly stop happened in chief worker\n')
+                    break
+
+
+            if (task_index == 0) and (not sess.should_stop()) and (g >= to_reach_step):
+                to_reach_step += stopping_step
+
+                ################## ACCURACY ##################
+                feed_dict[trainModel.predict_h] = con.valid_pos_h
+                feed_dict[trainModel.predict_t] = con.valid_pos_t
+                feed_dict[trainModel.predict_r] = con.valid_pos_r
+                res_pos = sess.run(trainModel.predict, feed_dict)
+
+                feed_dict[trainModel.predict_h] = con.valid_neg_h
+                feed_dict[trainModel.predict_t] = con.valid_neg_t
+                feed_dict[trainModel.predict_r] = con.valid_neg_r
+                res_neg = sess.run(trainModel.predict, feed_dict)
+
+                con.lib.getBestThreshold(con.relThresh_addr, res_pos.__array_interface__['data'][0], res_neg.__array_interface__['data'][0])
+                con.lib.test_triple_classification(con.relThresh_addr, res_pos.__array_interface__['data'][0], res_neg.__array_interface__['data'][0], con.acc_addr)
+                acc = con.acc[0]
+
+                if DEBUG:
+                    print("\n[ Early Stop Check (Accuracy) ]")
+                    print("Best Accuracy = %.10f" %(best_acc))
+                    print("Accuracy after run  =  %.10f" %(acc))
+
+                if acc > best_acc:
+                    best_acc = acc
+                    wait_steps_acc = 0
+                    if DEBUG: print("New best Accuracy founded. Wait steps reset.")
+                    best_model_global_step_acc = g
+
+                elif wait_steps_acc < patience:
+                    wait_steps_acc += 1
+                    if DEBUG: print("Wait steps Accuracy incremented: {}\n".format(wait_steps_acc))
+
+
+                if wait_steps_acc >= patience:
+                    print('Accuracy early stop. Accuracy has not been improved enough in {} times'.format(patience))
+                    with open(sys.argv.output_path+"/stop.txt", "w") as stop_file:
+                        stop_file.write(str(best_model_global_step_acc)+"\n")
+                    break
+
+                ################## LOSS ##################
+                if DEBUG:
+                    print("\n[ Early Stop Checking (Loss) ]")
+                    print("Best loss = %.10f" %(best_loss))
+                    print("Loss after run  =  %.10f" %(loss))
+
+                if loss < best_loss:
+                    best_loss = loss
+                    wait_steps_loss = 0
+                    if DEBUG: print("New best loss founded. Wait steps reset.")
+                    best_model_global_step_loss = g
+
+                elif wait_steps_loss < patience:
+                    wait_steps_loss += 1
+                    if DEBUG: print("Wait steps loss incremented: {}\n".format(wait_steps_loss))
+
+
+                if wait_steps_loss >= patience:
+                    print('Loss early stop. Losses has not been improved enough in {} times'.format(patience))
+                    with open(sys.argv.output_path+"/stop.txt", "w") as stop_file:
+                        stop_file.write(str(best_model_global_step_loss)+"\n")
+                    break
+
+        return
 
 
 
diff --git a/gpu_info.py b/gpu_info.py
new file mode 100644
index 0000000..fdcaaed
--- /dev/null
+++ b/gpu_info.py
@@ -0,0 +1,186 @@
+# Copyright 2017 Yahoo Inc.
+# Licensed under the terms of the Apache 2.0 license.
+# Please see LICENSE file in the project root for terms.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import nested_scopes
+from __future__ import print_function
+
+import ctypes as ct
+import logging
+import platform
+import random
+import subprocess
+import time
+
+MAX_RETRIES = 3           #: Maximum retries to allocate GPUs
+
+
+def _get_gpu():
+  """*DEPRECATED*. Allocates first available GPU using cudaSetDevice(), or returns 0 otherwise."""
+  # Note: this code executes, but Tensorflow subsequently complains that the "current context was not created by the StreamExecutor cuda_driver API"
+  system = platform.system()
+  if system == "Linux":
+    libcudart = ct.cdll.LoadLibrary("libcudart.so")
+  elif system == "Darwin":
+    libcudart = ct.cdll.LoadLibrary("libcudart.dylib")
+  elif system == "Windows":
+    libcudart = ct.windll.LoadLibrary("libcudart.dll")
+  else:
+    raise NotImplementedError("Cannot identify system.")
+
+  device_count = ct.c_int()
+  libcudart.cudaGetDeviceCount(ct.byref(device_count))
+  gpu = 0
+  for i in range(device_count.value):
+    if (0 == libcudart.cudaSetDevice(i) and 0 == libcudart.cudaFree(0)):
+      gpu = i
+      break
+  return gpu
+
+
+def get_gpus(num_gpu=1, worker_index=-1):
+  """Get list of free GPUs according to nvidia-smi.
+
+  This will retry for ``MAX_RETRIES`` times until the requested number of GPUs are available.
+
+  Args:
+    :num_gpu: number of GPUs desired.
+    :worker_index: index "hint" for allocation of available GPUs.
+
+  Returns:
+    Comma-delimited string of GPU ids, or raises an Exception if the requested number of GPUs could not be found.
+  """
+  # get list of gpus (index, uuid)
+  list_gpus = subprocess.check_output(["nvidia-smi", "--list-gpus"]).decode()
+  logging.debug("all GPUs:\n{0}".format(list_gpus))
+
+  # parse index and guid
+  gpus = [x for x in list_gpus.split('\n') if len(x) > 0]
+
+  def parse_gpu(gpu_str):
+    cols = gpu_str.split(' ')
+    return cols[5].split(')')[0], cols[1].split(':')[0]
+  gpu_list = [parse_gpu(gpu) for gpu in gpus]
+
+  free_gpus = []
+  retries = 0
+  while len(free_gpus) < num_gpu and retries < MAX_RETRIES:
+    #EDIT
+    #smi_output = subprocess.check_output(["nvidia-smi", "--format=csv,noheader,nounits", "--query-compute-apps=gpu_uuid"]).decode()
+    smi_output = subprocess.check_output(["nvidia-smi", "--format=csv,noheader,nounits", "--query-gpu=gpu_uuid,memory.free"]).decode()
+    
+    #logging.debug("busy GPUs:\n{0}".format(smi_output))
+    print("Smi output: {}".format(smi_output))
+    
+    #busy_uuids = [x for x in smi_output.split('\n') if len(x) > 0]
+    smi_output_arr = [x for x in smi_output.split('\n') if len(x) > 0]
+    busy_uuids = [x.split(",")[0].strip() for x in smi_output_arr if int(x.split(",")[1].strip()) == 0]
+    
+    for uuid, index in gpu_list:
+      if uuid not in busy_uuids:
+        free_gpus.append(index)
+
+    if len(free_gpus) < num_gpu:
+      logging.warn("Unable to find available GPUs: requested={0}, available={1}".format(num_gpu, len(free_gpus)))
+      retries += 1
+      time.sleep(30 * retries)
+      free_gpus = []
+
+  logging.info("Available GPUs: {}".format(free_gpus))
+
+  # if still can't find available GPUs, raise exception
+  if len(free_gpus) < num_gpu:
+    smi_output = subprocess.check_output(["nvidia-smi", "--format=csv", "--query-compute-apps=gpu_uuid,pid,process_name,used_gpu_memory"]).decode()
+    logging.info(": {0}".format(smi_output))
+    raise Exception("Unable to find {} free GPU(s)\n{}".format(num_gpu, smi_output))
+
+  # Get logical placement
+  num_available = len(free_gpus)
+  if worker_index == -1:
+    # use original random placement
+    random.shuffle(free_gpus)
+    proposed_gpus = free_gpus[:num_gpu]
+  else:
+    # ordered by worker index
+    if worker_index * num_gpu + num_gpu > num_available:
+      worker_index = worker_index * num_gpu % num_available
+    proposed_gpus = free_gpus[worker_index * num_gpu:(worker_index * num_gpu + num_gpu)]
+  logging.info("Proposed GPUs: {}".format(proposed_gpus))
+
+  return ','.join(str(x) for x in proposed_gpus)
+
+
+# Function to get the gpu information
+def _get_free_gpu(max_gpu_utilization=40, min_free_memory=0.5, num_gpu=1):
+  """Get available GPUs according to utilization thresholds.
+
+  Args:
+    :max_gpu_utilization: percent utilization threshold to consider a GPU "free"
+    :min_free_memory: percent free memory to consider a GPU "free"
+    :num_gpu: number of requested GPUs
+
+  Returns:
+    A tuple of (available_gpus, minimum_free_memory), where available_gpus is a comma-delimited string of GPU ids, and minimum_free_memory
+    is the lowest amount of free memory available on the available_gpus.
+
+  """
+  def get_gpu_info():
+    # Get the gpu information
+    gpu_info = subprocess.check_output(["nvidia-smi", "--format=csv,noheader,nounits", "--query-gpu=index,memory.total,memory.free,memory.used,utilization.gpu"]).decode()
+    gpu_info = gpu_info.split('\n')
+
+    gpu_info_array = []
+
+    # Check each gpu
+    for line in gpu_info:
+      if len(line) > 0:
+        gpu_id, total_memory, free_memory, used_memory, gpu_util = line.split(',')
+        gpu_memory_util = float(used_memory) / float(total_memory)
+        gpu_info_array.append((float(gpu_util), gpu_memory_util, gpu_id))
+
+    return(gpu_info_array)
+
+  # Read the gpu information multiple times
+  num_times_to_average = 5
+  current_array = []
+  for ind in range(num_times_to_average):
+    current_array.append(get_gpu_info())
+    time.sleep(1)
+
+  # Get number of gpus
+  num_gpus = len(current_array[0])
+
+  # Average the gpu information
+  avg_array = [(0, 0, str(x)) for x in range(num_gpus)]
+  for ind in range(num_times_to_average):
+    for gpu_ind in range(num_gpus):
+      avg_array[gpu_ind] = (avg_array[gpu_ind][0] + current_array[ind][gpu_ind][0], avg_array[gpu_ind][1] + current_array[ind][gpu_ind][1], avg_array[gpu_ind][2])
+
+  for gpu_ind in range(num_gpus):
+    avg_array[gpu_ind] = (float(avg_array[gpu_ind][0]) / num_times_to_average, float(avg_array[gpu_ind][1]) / num_times_to_average, avg_array[gpu_ind][2])
+
+  avg_array.sort()
+
+  gpus_found = 0
+  gpus_to_use = ""
+  free_memory = 1.0
+  # Return the least utilized GPUs if it's utilized less than max_gpu_utilization and amount of free memory is at least min_free_memory
+  # Otherwise, run in cpu only mode
+  for current_gpu in avg_array:
+    if current_gpu[0] < max_gpu_utilization and (1 - current_gpu[1]) > min_free_memory:
+      if gpus_found == 0:
+        gpus_to_use = current_gpu[2]
+        free_memory = 1 - current_gpu[1]
+      else:
+        gpus_to_use = gpus_to_use + "," + current_gpu[2]
+        free_memory = min(free_memory, 1 - current_gpu[1])
+
+      gpus_found = gpus_found + 1
+
+    if gpus_found == num_gpu:
+      break
+
+  return gpus_to_use, free_memory
+
diff --git a/main_spark.py b/main_spark.py
index b59e059..7c88530 100644
--- a/main_spark.py
+++ b/main_spark.py
@@ -8,10 +8,9 @@
 import os
 import traceback
 import time
-import shutil
 from os import path
 
-os.environ["CUDA_VISIBLE_DEVICES"]="-1"
+# os.environ["CUDA_VISIBLE_DEVICES"]="-1"
 
 DEBUG = distribute_training.DEBUG
 
@@ -40,7 +39,7 @@ def update_entities_and_model():
         n_new_entities = int(f.readline().strip())
         if n_new_entities > 0:
             for _ in range(n_new_entities):
-                batch_entities.append((f.readline()))
+                batch_entities.append(f.readline())
 
 
     ######### READ OLD ENTITIES #########
@@ -52,48 +51,49 @@ def update_entities_and_model():
 
 
     ######### UPDATE THE MODEL #########
-    con, ckpt = distribute_training.get_conf_to_update_model(sys.argv.output_path)
-    vars = []
-
-    if DEBUG: print("\nGLOBAL VARS FOUNDED IN CHECKPOINT:\n")
-    with con.graph.as_default():
-        with con.sess.as_default():
-            for v in tf.global_variables():
-                if DEBUG: print(str(v.name) + " " + str(v.shape))
-                vars.append(v)
-    if DEBUG: print('\n')
-
-
-    if DEBUG: print("NEW GLOBAL VARIABLES")
-    graph = tf.Graph()
-    with graph.as_default():
-        sess = tf.Session()
-        with sess.as_default():
-            for v in vars:
-                current_name = v.name.split(':')[0]
-
-                if (current_name == ENTITY_EMBEDDING_TENSOR_NAME) and (final_entity_size > n_entities):
-                    tmp = tf.get_variable(name=current_name, shape=[final_entity_size, v.shape[1]], initializer=tf.contrib.layers.xavier_initializer(uniform = False), dtype=v.dtype)
-                    sess.run(tf.initialize_variables([tmp]))
-                    tmp_value = con.sess.run(v)
-                    sess.run(tf.scatter_update(tmp, [i for i in range(0, n_entities)], tmp_value))
-
-                elif (current_name in [ENTITY_EMBEDDING_TENSOR_NAME+'/Adam', ENTITY_EMBEDDING_TENSOR_NAME+'/Adam_1']) and (final_entity_size > n_entities):
-                    tmp = tf.get_variable(name=current_name, shape=[final_entity_size, v.shape[1]], initializer=tf.zeros_initializer(), dtype=v.dtype)
-                    sess.run(tf.initialize_variables([tmp]))
-                    tmp_value = con.sess.run(v)
-                    sess.run(tf.scatter_update(tmp, [i for i in range(0, n_entities)], tmp_value))
-
-                else:
-                    tmp = tf.get_variable(name=current_name, shape=v.shape, dtype=v.dtype)
-                    tmp_value = con.sess.run(v)
-                    sess.run(tf.assign(tmp, tmp_value))
-
-            for v in tf.global_variables():
-                print(str(v.name) + " " + str(v.shape))
-
-            saver = tf.train.Saver()
-            saver.save(sess, ckpt, write_state=False)
+    if n_new_entities > 0:
+        con, ckpt = distribute_training.get_conf_to_update_model(sys.argv.output_path)
+        vars = []
+
+        if DEBUG: print("\nGLOBAL VARS FOUNDED IN CHECKPOINT:\n")
+        with con.graph.as_default():
+            with con.sess.as_default():
+                for v in tf.global_variables():
+                    if DEBUG: print(str(v.name) + " " + str(v.shape))
+                    vars.append(v)
+        if DEBUG: print('\n')
+
+
+        if DEBUG: print("NEW GLOBAL VARIABLES")
+        graph = tf.Graph()
+        with graph.as_default():
+            sess = tf.Session()
+            with sess.as_default():
+                for v in vars:
+                    current_name = v.name.split(':')[0]
+
+                    if current_name == ENTITY_EMBEDDING_TENSOR_NAME:
+                        tmp = tf.get_variable(name=current_name, shape=[final_entity_size, v.shape[1]], initializer=tf.contrib.layers.xavier_initializer(uniform = False), dtype=v.dtype)
+                        sess.run(tf.initialize_variables([tmp]))
+                        tmp_value = con.sess.run(v)
+                        sess.run(tf.scatter_update(tmp, [i for i in range(0, n_entities)], tmp_value))
+
+                    elif current_name in [ENTITY_EMBEDDING_TENSOR_NAME+'/Adam', ENTITY_EMBEDDING_TENSOR_NAME+'/Adam_1']:
+                        tmp = tf.get_variable(name=current_name, shape=[final_entity_size, v.shape[1]], initializer=tf.zeros_initializer(), dtype=v.dtype)
+                        sess.run(tf.initialize_variables([tmp]))
+                        tmp_value = con.sess.run(v)
+                        sess.run(tf.scatter_update(tmp, [i for i in range(0, n_entities)], tmp_value))
+
+                    else:
+                        tmp = tf.get_variable(name=current_name, shape=v.shape, dtype=v.dtype)
+                        tmp_value = con.sess.run(v)
+                        sess.run(tf.assign(tmp, tmp_value))
+
+                for v in tf.global_variables():
+                    print(str(v.name) + " " + str(v.shape))
+
+                saver = tf.train.Saver()
+                saver.save(sess, ckpt, write_state=False)
 
 
     ######### UPDATE ENTITY FILE #########
@@ -272,9 +272,9 @@ def n_n():
     parser.add_argument("--cluster_size", help="number of nodes in the cluster", type=int, default=int(sc._conf.get("spark.executor.instances")))
     parser.add_argument("--num_ps", help="number of ps nodes", type=int, default=1)
     parser.add_argument("--num_gpus", help="number of gpus to use", type=int, default=0)
-    parser.add_argument("--input_path", help="dataset absolute path", type=str, default="/home/")
-    parser.add_argument("--output_path", help="model output absolute path", type=str, default="/home/")
-    # parser.add_argument("--working_threads", help="no. threads used during sampling", type=int, default=8)
+    parser.add_argument("--cpp_lib_path", help="cpp lib.so absolute path", type=str, default=None)
+    parser.add_argument("--input_path", help="dataset absolute path", type=str, default="drive/My\ Drive/DBpedia/5/0/")
+    parser.add_argument("--output_path", help="model output absolute path", type=str, default="OpenKE_new_Spark/res_spark")
     parser.add_argument("--train_times", help="no. epochs", type=int, default=1000)
     parser.add_argument("--n_batches", help="no. batches", type=int, default=0)
     parser.add_argument("--alpha", help="learning rate", type=float, default=0.001)
diff --git a/release/Base.so b/release/Base.so
index 18db0000be66d0b6293ae01f051a43a73c9e4f92..e773d100e85727c0cb677bf50f8ed55d8287c161 100755
GIT binary patch
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diff --git a/run.sh b/run.sh
deleted file mode 100644
index a9aa39c..0000000
--- a/run.sh
+++ /dev/null
@@ -1,249 +0,0 @@
-#!/bin/bash
-# On ps0.example.com:
-python3 example_train_transe_d.py \
-     --ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
-     --worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
-     --job_name=ps --task_index=0
-# On ps1.example.com:
-python3 example_train_transe_d.py \
-     --ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
-     --worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
-     --job_name=ps --task_index=1
-# On worker0.example.com:
-python3 example_train_transe_d.py \
-     --ps_hosts=ps0.example.com:2222,ps1.example.com:2222 \
-     --worker_hosts=worker0.example.com:2222,worker1.example.com:2222 \
-     --job_name=worker --task_index=0
-# On worker1.example.com:
-python3 example_train_transe_d.py \
-     --ps_hosts=ps0.example.com:2223,ps1.example.com:2223 \
-     --worker_hosts=worker0.example.com:2223,worker1.example.com:2223 \
-     --job_name=worker --task_index=1
-
-
-python3 example_train_transe_d.py \
-     --ps_hosts=ps0.example.com:2225 \
-     --worker_hosts=worker0.example.com:2225 \
-     --job_name=ps --task_index=0
-
-python3 example_train_transe_d.py \
-     --ps_hosts=ps0.example.com:2224,ps1.example.com:2224 \
-     --worker_hosts=worker0.example.com:2224,worker1.example.com:2224 \
-     --job_name=worker --task_index=0
-
-lsof -i -P -n | grep LISTEN | grep python
-
-
-
-python3 example_train_transe_d.py --job_name "ps" --task_index 0 &
-python3 example_train_transe_d.py --job_name "worker" --task_index 0 &
-
-
-python3 example_train_transe_d.py --ps_hosts=localhost:2222,localhost:2223 --worker_hosts=localhost:2224,localhost:2225 --job_name=ps --task_index=0 &
-python3 example_train_transe_d.py --ps_hosts=localhost:2222,localhost:2223 --worker_hosts=localhost:2224,localhost:2225 --job_name=ps --task_index=1 &
-python3 example_train_transe_d.py --ps_hosts=localhost:2222,localhost:2223 --worker_hosts=localhost:2224,localhost:2225 --job_name=worker --task_index=0 &
-python3 example_train_transe_d.py --ps_hosts=localhost:2222,localhost:2223 --worker_hosts=localhost:2224,localhost:2225 --job_name=worker --task_index=1 &
-
-
-python3 example_train_transe_d.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223 --job_name=ps --task_index=0 &
-python3 example_train_transe_d.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223 --job_name=ps --task_index=1 &
-python3 example_train_transe_d.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223 --job_name=worker --task_index=0 &
-python3 example_train_transe_d.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223 --job_name=worker --task_index=1 &
-
-
-
-python3 example_train_transe_d.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223 --job_name=ps --task_index=0 &
-python3 example_train_transe_d.py --ps_hosts=localhost:2222 --worker_hosts=localhost:2223 --job_name=worker --task_index=0 &
-
-
-
-${SPARK_HOME}/bin/spark-submit \
---master ${MASTER} \
---py-files /home/luigi/IdeaProjects/OpenKE_Spark/example_train_transe_spark.py \
---conf spark.cores.max=${TOTAL_CORES} \
---conf spark.task.cpus=${CORES_PER_WORKER} \
---conf spark.executorEnv.JAVA_HOME="${JAVA_HOME}" \
-/home/luigi/IdeaProjects/OpenKE_Spark/example_train_transe_spark.py \
---cluster_size ${SPARK_WORKER_INSTANCES}
-
-
-
-
-${SPARK_HOME}/bin/spark-submit \
---master ${MASTER} \
---conf spark.cores.max=${TOTAL_CORES} \
---conf spark.task.cpus=${CORES_PER_WORKER} \
-/home/luigi/IdeaProjects/OpenKE_Spark/example_train_transe_spark.py \
---cluster_size ${SPARK_EXECUTOR_INSTANCES}
-
-
-
-##################################################################################################################
-
-
-${SPARK_HOME}/sbin/start-master.sh; ${SPARK_HOME}/sbin/start-slave.sh -c ${CORES_PER_WORKER} -m ${MEMORY_PER_WORKER} ${MASTER}
-
-
-${SPARK_HOME}/sbin/stop-slave.sh; ${SPARK_HOME}/sbin/stop-master.sh
-
-#CPU
-${SPARK_HOME}/bin/spark-submit --master ${MASTER} \
---py-files /home/luigi/IdeaProjects/OpenKE_new_Spark/distribute_training.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Config.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransE.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransH.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransR.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransD.py \
---conf spark.dynamicAllocation.enabled=false \
---conf spark.cores.max=${TOTAL_CORES} --conf spark.task.cpus=${CORES_PER_WORKER} --executor-memory ${MEMORY_PER_WORKER} \
---num-executors ${SPARK_WORKER_INSTANCES} \
-/home/luigi/IdeaProjects/OpenKE_new_Spark/main_spark.py \
---cluster_size ${SPARK_WORKER_INSTANCES} --num_ps 1 \
---input_path /home/luigi/IdeaProjects/OpenKE_new_Spark/benchmarks/DBpedia/5/0/ \
---output_path /home/luigi/IdeaProjects/OpenKE_new_Spark/res_spark \
---alpha 0.0001 --optimizer SGD --train_times 50 --ent_neg_rate 1 --embedding_dimension 64 --margin 1.0 --model TransE
-
-
-
-${SPARK_HOME}/bin/spark-submit --master ${MASTER} \
---py-files /home/luigi/IdeaProjects/OpenKE_new_Spark/distribute_training.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Config.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransE.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransH.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransR.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransD.py \
---conf spark.cores.max=${TOTAL_CORES} --conf spark.task.cpus=${CORES_PER_WORKER} --executor-memory ${MEMORY_PER_WORKER} \
---num-executors ${SPARK_WORKER_INSTANCES} \
-/home/luigi/IdeaProjects/OpenKE_new_Spark/main_spark.py \
---cluster_size ${SPARK_WORKER_INSTANCES} --num_ps 1 \
---input_path /home/luigi/IdeaProjects/OpenKE_new_Spark/benchmarks/superuser_1/5/ \
---output_path /home/luigi/IdeaProjects/OpenKE_new_Spark/res_spark \
---early_stop_patience 10 --early_stop_tolerance 0 \
---working_threads 8 --alpha 0.0001 --optimizer Adam --train_times 50 --ent_neg_rate 5 --embedding_dimension 64 --margin 1.0 --model TransE
-
-
-#GPU
-${SPARK_HOME}/bin/spark-submit \
---master ${MASTER} \
---py-files /home/luigi/IdeaProjects/OpenKE_new_Spark/distribute_training.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Config.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransE.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransH.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransR.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_new_Spark/TransD.py \
---conf spark.executorEnv.LD_LIBRARY_PATH=${LIB_CUDA} \
---driver-library-path=${LIB_CUDA} \
---num-executors ${SPARK_WORKER_INSTANCES} \
-/home/luigi/IdeaProjects/OpenKE_new_Spark/main_spark.py \
---cluster_size ${SPARK_WORKER_INSTANCES} \
---num_ps 1 \
---input_path /home/luigi/IdeaProjects/OpenKE_new_Spark/benchmarks/superuser_1/5/ \
---output_path /home/luigi/IdeaProjects/OpenKE_new_Spark/res_spark \
---working_threads 8 \
---alpha 0.0001 \
---early_stop_patience 10 --early_stop_tolerance 0 --optimizer Adam \
---train_times 200 --ent_neg_rate 5 --embedding_dimension 64 --margin 1.0 --model TransE
-
-
-
-
---conf spark.driver.memory ${MEMORY_PER_WORKER} \
---conf spark.executor.memory ${TOTAL_MEMORY} \
-
-
-
-
-${SPARK_HOME}/bin/spark-submit \
---master ${MASTER} \
---py-files /home/luigi/IdeaProjects/OpenKE_Spark/distribute_training.py,/home/luigi/IdeaProjects/OpenKE_Spark/Config.py,/home/luigi/IdeaProjects/OpenKE_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_Spark/TransE.py,/home/luigi/IdeaProjects/OpenKE_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_Spark/TransH.py,/home/luigi/IdeaProjects/OpenKE_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_Spark/TransR.py,/home/luigi/IdeaProjects/OpenKE_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_Spark/TransD.py \
---conf spark.cores.max=${TOTAL_CORES} \
---conf spark.task.cpus=${CORES_PER_WORKER} \
---num-executors ${SPARK_WORKER_INSTANCES} \
-/home/luigi/IdeaProjects/OpenKE_Spark/main_spark.py \
---cluster_size ${SPARK_WORKER_INSTANCES} \
---num_ps 1 \
---input_path /home/luigi/IdeaProjects/OpenKE_Spark/benchmarks/superuser/ \
---output_path /home/luigi/IdeaProjects/OpenKE_Spark/res_spark \
---working_threads 8 \
---train_times 100 --n_batches 100 \
---alpha 0.0001 --margin 1.0 --embedding_dimension 100 \
---ent_neg_rate 10 --rel_neg_rate 0 --optimizer Adam --model TransE
-
-
-
-
-
-${SPARK_HOME}/sbin/stop-slave.sh; ${SPARK_HOME}/sbin/stop-master.sh
-
-
-##################################################################################################################
-
-
-
-${SPARK_HOME}/bin/spark-submit \
---master ${MASTER} \
---num-executors 2 \
---queue default \
---py-files /home/luigi/IdeaProjects/OpenKE_Spark/example_train_transe_spark.py,/home/luigi/IdeaProjects/OpenKE_Spark/Config.py,/home/luigi/IdeaProjects/OpenKE_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_Spark/TransE.py \
---conf spark.cores.max=${TOTAL_CORES} \
---conf spark.task.cpus=${CORES_PER_WORKER} \
-/home/luigi/IdeaProjects/OpenKE_Spark/main_spark.py \
---cluster_size ${SPARK_WORKER_INSTANCES} \
---num_ps 1
-
-
-
-
-
-
-
-
-
-${SPARK_HOME}/bin/spark-submit \
---master ${MASTER} \
---py-files /home/luigi/IdeaProjects/OpenKE_Spark/example_train_transe_spark.py,/home/luigi/IdeaProjects/OpenKE_Spark/Config.py,/home/luigi/IdeaProjects/OpenKE_Spark/Model.py,/home/luigi/IdeaProjects/OpenKE_Spark/TransE.py \
---conf spark.cores.max=${TOTAL_CORES} \
---conf spark.task.cpus=${CORES_PER_WORKER} \
---conf spark.executorEnv.JAVA_HOME="$JAVA_HOME" \
---conf spark.task.maxFailures=1 \
---conf spark.stage.maxConsecutiveAttempts=1 \
---executor-cores 1 \
---num-executors ${SPARK_WORKER_INSTANCES} \
-/home/luigi/IdeaProjects/OpenKE_Spark/main_spark.py \
---cluster_size ${SPARK_WORKER_INSTANCES} \
---num_ps 1
-
-
-
-
-
- \
---images examples/mnist/csv/train/images \
---labels examples/mnist/csv/train/labels \
---format csv \
---mode train \
---model mnist_model
-
-
-
-JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/jre
-export PYSPARK_PYTHON=/usr/bin/python3 
-
-
-
- echo 0 | sudo tee /sys/devices/system/cpu/cpu1/online
- echo 0 | sudo tee /sys/devices/system/cpu/cpu2/online
- echo 0 | sudo tee /sys/devices/system/cpu/cpu3/online
- echo 0 | sudo tee /sys/devices/system/cpu/cpu4/online
- echo 0 | sudo tee /sys/devices/system/cpu/cpu5/online
- echo 0 | sudo tee /sys/devices/system/cpu/cpu6/online
- echo 0 | sudo tee /sys/devices/system/cpu/cpu7/online
-
-
- echo 1 | sudo tee /sys/devices/system/cpu/cpu1/online
- echo 1 | sudo tee /sys/devices/system/cpu/cpu2/online
- echo 1 | sudo tee /sys/devices/system/cpu/cpu3/online
- echo 1 | sudo tee /sys/devices/system/cpu/cpu4/online
- echo 1 | sudo tee /sys/devices/system/cpu/cpu5/online
- echo 1 | sudo tee /sys/devices/system/cpu/cpu6/online
- echo 1 | sudo tee /sys/devices/system/cpu/cpu7/online
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/run_dbpedia_10.sh b/run_dbpedia_10.sh
new file mode 100644
index 0000000..715b204
--- /dev/null
+++ b/run_dbpedia_10.sh
@@ -0,0 +1,43 @@
+echo "====================================== Stopping Spark Master & slaves ======================================"
+$SPARK_HOME/sbin/stop-slave.sh
+$SPARK_HOME/sbin/stop-master.sh
+
+
+echo "====================================== Starting Spark Master & slaves ======================================"
+$SPARK_HOME/sbin/start-master.sh; $SPARK_HOME/sbin/start-slave.sh -c $CORES_PER_WORKER -m $MEMORY_PER_WORKER spark://$(hostname):7077
+n=10
+m=$((n-1))
+
+for i in `seq 0 $m`
+do
+  if [ -f /content/drive/My\ Drive/DBpedia/$n/$i/res.txt ]; then
+    echo "Skipping batch $i"
+	  continue
+  fi
+
+	echo "====================================== Starting Training for batch $i ======================================"
+	$SPARK_HOME/bin/spark-submit --master spark://$(hostname):7077 \
+	--py-files $WORK_DIR_PREFIX/distribute_training.py,$WORK_DIR_PREFIX/Config.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransE.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransH.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransR.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransD.py \
+    --driver-library-path=$LIB_CUDA \
+    --conf spark.dynamicAllocation.enabled=false \
+    --conf spark.task.cpus=$CORES_PER_WORKER --executor-memory $MEMORY_PER_WORKER \
+    --num-executors $SPARK_WORKER_INSTANCES \
+	$WORK_DIR_PREFIX/main_spark.py \
+    --cluster_size $SPARK_WORKER_INSTANCES --num_ps 1 --num_gpus 1 --cpp_lib_path $WORK_DIR_PREFIX/release/Base.so \
+	--input_path /content/drive/My\ Drive/DBpedia/$n/$i/ \
+    --output_path /content/drive/My\ Drive/DBpedia/$n/$i/model \
+    --alpha 0.0001 --optimizer SGD --train_times 50 --ent_neg_rate 1 --embedding_dimension 64 --margin 1.0 --model TransE
+
+
+	echo "====================================== Copying data into new batch folder for batch $i ======================================"
+	j=$((i+1))
+	if [ $j != $n ]; then
+		cp /content/drive/My\ Drive/DBpedia/$n/$i/*.txt /content/drive/My\ Drive/DBpedia/$n/$j/
+		cp /content/drive/My\ Drive/DBpedia/$n/$i/model/* /content/drive/My\ Drive/DBpedia/$n/$j/model/
+	fi
+	
+	echo "====================================== Test for batch $i ======================================"
+	python3 $WORK_DIR_PREFIX/test.py $i $n 64 | tee /content/drive/My\ Drive/DBpedia/$n/$i/res.txt
+
+done
+
diff --git a/run_dbpedia_15.sh b/run_dbpedia_15.sh
new file mode 100644
index 0000000..cc76d35
--- /dev/null
+++ b/run_dbpedia_15.sh
@@ -0,0 +1,43 @@
+echo "====================================== Stopping Spark Master & slaves ======================================"
+$SPARK_HOME/sbin/stop-slave.sh
+$SPARK_HOME/sbin/stop-master.sh
+
+
+echo "====================================== Starting Spark Master & slaves ======================================"
+$SPARK_HOME/sbin/start-master.sh; $SPARK_HOME/sbin/start-slave.sh -c $CORES_PER_WORKER -m $MEMORY_PER_WORKER spark://$(hostname):7077
+n=15
+m=$((n-1))
+
+for i in `seq 0 $m`
+do
+  if [ -f /content/drive/My\ Drive/DBpedia/$n/$i/res.txt ]; then
+    echo "Skipping batch $i"
+	  continue
+  fi
+
+	echo "====================================== Starting Training for batch $i ======================================"
+	$SPARK_HOME/bin/spark-submit --master spark://$(hostname):7077 \
+	--py-files $WORK_DIR_PREFIX/distribute_training.py,$WORK_DIR_PREFIX/Config.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransE.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransH.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransR.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransD.py \
+    --driver-library-path=$LIB_CUDA \
+    --conf spark.dynamicAllocation.enabled=false \
+    --conf spark.task.cpus=$CORES_PER_WORKER --executor-memory $MEMORY_PER_WORKER \
+    --num-executors $SPARK_WORKER_INSTANCES \
+	$WORK_DIR_PREFIX/main_spark.py \
+    --cluster_size $SPARK_WORKER_INSTANCES --num_ps 1 --num_gpus 1 --cpp_lib_path $WORK_DIR_PREFIX/release/Base.so \
+	--input_path /content/drive/My\ Drive/DBpedia/$n/$i/ \
+    --output_path /content/drive/My\ Drive/DBpedia/$n/$i/model \
+    --alpha 0.0001 --optimizer SGD --train_times 50 --ent_neg_rate 1 --embedding_dimension 64 --margin 1.0 --model TransE
+
+
+	echo "====================================== Copying data into new batch folder for batch $i ======================================"
+	j=$((i+1))
+	if [ $j != $n ]; then
+		cp /content/drive/My\ Drive/DBpedia/$n/$i/*.txt /content/drive/My\ Drive/DBpedia/$n/$j/
+		cp /content/drive/My\ Drive/DBpedia/$n/$i/model/* /content/drive/My\ Drive/DBpedia/$n/$j/model/
+	fi
+
+	echo "====================================== Test for batch $i ======================================"
+	python3 $WORK_DIR_PREFIX/test.py $i $n 64 | tee /content/drive/My\ Drive/DBpedia/$n/$i/res.txt
+
+done
+
diff --git a/run_dbpedia_5.sh b/run_dbpedia_5.sh
new file mode 100644
index 0000000..9896237
--- /dev/null
+++ b/run_dbpedia_5.sh
@@ -0,0 +1,43 @@
+echo "====================================== Stopping Spark Master & slaves ======================================"
+$SPARK_HOME/sbin/stop-slave.sh
+$SPARK_HOME/sbin/stop-master.sh
+
+
+echo "====================================== Starting Spark Master & slaves ======================================"
+$SPARK_HOME/sbin/start-master.sh; $SPARK_HOME/sbin/start-slave.sh -c $CORES_PER_WORKER -m $MEMORY_PER_WORKER spark://$(hostname):7077
+n=5
+m=$((n-1))
+
+for i in `seq 0 $m`
+do
+  if [ -f /content/drive/My\ Drive/DBpedia/$n/$i/res.txt ]; then
+    echo "Skipping batch $i"
+	  continue
+  fi
+
+	echo "====================================== Starting Training for batch $i ======================================"
+	$SPARK_HOME/bin/spark-submit --master spark://$(hostname):7077 \
+	--py-files $WORK_DIR_PREFIX/distribute_training.py,$WORK_DIR_PREFIX/Config.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransE.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransH.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransR.py,$WORK_DIR_PREFIX/Model.py,$WORK_DIR_PREFIX/TransD.py \
+    --driver-library-path=$LIB_CUDA \
+    --conf spark.dynamicAllocation.enabled=false \
+    --conf spark.task.cpus=$CORES_PER_WORKER --executor-memory $MEMORY_PER_WORKER \
+    --num-executors $SPARK_WORKER_INSTANCES \
+	$WORK_DIR_PREFIX/main_spark.py \
+    --cluster_size $SPARK_WORKER_INSTANCES --num_ps 1 --num_gpus 1 --cpp_lib_path $WORK_DIR_PREFIX/release/Base.so \
+	--input_path /content/drive/My\ Drive/DBpedia/$n/$i/ \
+    --output_path /content/drive/My\ Drive/DBpedia/$n/$i/model \
+    --alpha 0.0001 --optimizer SGD --train_times 50 --ent_neg_rate 1 --embedding_dimension 64 --margin 1.0 --model TransE
+
+
+	echo "====================================== Copying data into new batch folder for batch $i ======================================"
+	j=$((i+1))
+	if [ $j != $n ]; then
+		cp /content/drive/My\ Drive/DBpedia/$n/$i/*.txt /content/drive/My\ Drive/DBpedia/$n/$j/
+		cp /content/drive/My\ Drive/DBpedia/$n/$i/model/* /content/drive/My\ Drive/DBpedia/$n/$j/model/
+	fi
+	
+	echo "====================================== Test for batch $i ======================================"
+	python3 $WORK_DIR_PREFIX/test.py $i $n 64 | tee /content/drive/My\ Drive/DBpedia/$n/$i/res.txt
+
+done
+
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..9da087a
--- /dev/null
+++ b/test.py
@@ -0,0 +1,31 @@
+from Config import Config
+from TransE import TransE
+import sys
+
+n = sys.argv[1]
+max = sys.argv[2]
+dim = sys.argv[3]
+
+def get_ckpt(p):
+    ckpt = None
+    with open(p + "checkpoint", 'r') as f:
+        first_line = f.readline()
+        ckpt = first_line.split(':')[1].strip().replace('"', '').split('/')
+        ckpt = ckpt[len(ckpt) - 1]
+    return ckpt
+
+dataset_path = '/content/drive/My Drive/DBpedia/{}/{}/'.format(max, n)
+path = dataset_path + 'model/'
+ckpt = get_ckpt(path)
+
+con = Config(cpp_lib_path='/content/OpenKEonSpark/release/Base.so')
+con.set_in_path(dataset_path)
+con.set_test_link_prediction(True)
+con.set_test_triple_classification(True)
+con.set_dimension(int(dim))
+con.init()
+con.set_model_and_session(TransE)
+con.set_import_files(path+ckpt)
+con.set_test_log_path(path)
+con.test()
+print(con.acc)
diff --git a/test_1.py b/test_1.py
new file mode 100644
index 0000000..88ff6b3
--- /dev/null
+++ b/test_1.py
@@ -0,0 +1,32 @@
+from Config import Config
+from TransE import TransE
+import sys
+# import os
+
+
+def get_ckpt(p):
+    ckpt = None
+    with open(p + "checkpoint", 'r') as f:
+        first_line = f.readline()
+        ckpt = first_line.split(':')[1].strip().replace('"', '').split('/')
+        ckpt = ckpt[len(ckpt) - 1]
+    return ckpt
+
+
+#/home/luigi/IdeaProjects/OpenKE_new_Spark/benchmarks/DBpedia
+dataset_path = '/home/luigi/files/stuff/superuser/9/1/'
+path = dataset_path + 'model/'
+ckpt = get_ckpt(path)
+
+
+con = Config()
+con.set_in_path(dataset_path)
+con.set_test_link_prediction(True)
+# con.set_test_triple_classification(True)
+con.set_dimension(int(100))
+con.init()
+con.set_model_and_session(TransE)
+con.set_import_files(path+ckpt)
+con.set_test_log_path(path)
+con.test()
+