Merge pull request #107 from zoq/timeout_decorator

Use python timeout decorator instead of process queues.

Author: rcurtin

libraries/dtimeout_install.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+#
+# Wrapper script to unpack and build dtimeout.
+#
+# Include files will be installed to ../include/.
+# Library files will be installed to ../lib/.
+#
+# One dtimeout.tar.gz file should be located in this directory.
+tars=`ls dtimeout.tar.gz | wc -l`;
+if [ "$tars" -eq "0" ];
+then
+  echo "No source dtimeout.tar.gz found in libraries/!"
+  exit 1
+fi
+
+# Remove any old directory.
+rm -rf dtimeout/
+mkdir dtimeout/
+tar -xzpf dtimeout.tar.gz --strip-components=1 -C dtimeout/
+
+cd dtimeout/
+python3 setup.py build
+PYVER=`python3 -c 'import sys; print("python" + sys.version[0:3])'`;
+mkdir -p ../lib/$PYVER/site-packages/
+PYTHONPATH=../lib/$PYVER/site-packages/ python3 setup.py install --prefix=../ -O2

libraries/install_all.sh

@@ -85,3 +85,9 @@ if [ "$?" -ne "0" ]; then
   echo "Error installing R!";
   exit 1;
 fi
+
+./dtimeout_install.sh $1
+if [ "$?" -ne "0" ]; then
+  echo "Error installing R!";
+  exit 1;
+fi

libraries/package-urls.txt

@@ -1,3 +1,4 @@
+dtimeout https://github.com/pnpnpn/timeout-decorator/archive/master.tar.gz
 ann https://www.cs.umd.edu/~mount/ANN/Files/1.1.2/ann_1.1.2.tar.gz
 flann https://github.com/mariusmuja/flann/archive/1.9.1.tar.gz
 HLearn https://github.com/mikeizbicki/HLearn/archive/2.0.0.0.tar.gz

methods/annoy/ann.py

@@ -7,6 +7,7 @@
 import os
 import sys
 import inspect
+import timeout_decorator
 
 # Import the util path, this method even works if the path contains symlinks to
 # modules.
@@ -46,7 +47,8 @@ def __init__(self, dataset, timeout=0, verbose=True):
   successful.
   '''
   def AnnAnnoy(self, options):
-    def RunAnnAnnoy(q):
+    @timeout_decorator.timeout(self.timeout)
+    def RunAnnAnnoy():
       totalTimer = Timer()
 
       # Load input dataset.
@@ -58,18 +60,15 @@ def RunAnnAnnoy(q):
       # Parse options.
       if not "k" in options:
         Log.Fatal("Required option: Number of furthest neighbors to find.")
-        q.put(-1)
         return -1
       else:
         k = int(options.pop("k"))
         if (k < 1 or k > referenceData.shape[0]):
           Log.Fatal("Invalid k: " + k.group(1) + "; must be greater than 0"
               + " and less or equal than " + str(referenceData.shape[0]))
-          q.put(-1)
           return -1
       if not "num_trees" in options:
         Log.Fatal("Required option: Number of trees to build")
-        q.put(-1)
         return -1
       else:
         n = int(options.pop("num_trees"))
@@ -91,13 +90,14 @@ def RunAnnAnnoy(q):
               v = t.get_nns_by_vector(queryData[i],k)
         except Exception as e:
           Log.Info(e)
-          q.put(e)
           return -1
       time = totalTimer.ElapsedTime()
-      q.put(time)
       return time
 
-    return timeout(RunAnnAnnoy, self.timeout)
+    try:
+      return RunAnnAnnoy()
+    except timeout_decorator.TimeoutError:
+      return -1
 
   '''
   Perform All K-Nearest-Neighbors. If the method has been successfully completed

methods/matlab/dtc.py

@@ -126,7 +126,7 @@ def parseTimer(self, data):
     pattern = re.compile(br"""
         .*?total_time: (?P<total_time>.*?)s.*?
         """, re.VERBOSE|re.MULTILINE|re.DOTALL)
- 
+
     match = pattern.match(data)
     if not match:
       Log.Fatal("Can't parse the data: wrong format")

methods/milk/adaboost.py

@@ -5,6 +5,7 @@
 import os
 import sys
 import inspect
+import timeout_decorator
 
 # Import the util path, this method even works if the path contains symlinks to
 # modules.
@@ -65,7 +66,8 @@ def BuildModel(self):
   successful.
   '''
   def ADABOOSTMilk(self, options):
-    def RunADABOOSTMilk(q):
+    @timeout_decorator.timeout(self.timeout)
+    def RunADABOOSTMilk():
       totalTimer = Timer()
 
       Log.Info("Loading dataset", self.verbose)
@@ -75,19 +77,18 @@ def RunADABOOSTMilk(q):
       try:
         self.model = self.BuildModel()
         with totalTimer:
-         self.model = self.model.train(trainData, labels)
-          
+          self.model = self.model.train(trainData, labels)
+
       except Exception as e:
-        Log.Debug(str(e))
-        q.put(-1)
         return -1
 
       time = totalTimer.ElapsedTime()
-      q.put(time)
-
       return time
 
-    return timeout(RunADABOOSTMilk, self.timeout)
+    try:
+      return RunADABOOSTMilk()
+    except timeout_decorator.TimeoutError:
+      return -1
 
   '''
   Perform the AdaBoost classifier. If the method has been

methods/milk/dtc.py

@@ -6,6 +6,7 @@
 import os
 import sys
 import inspect
+import timeout_decorator
 
 # Import the util path, this method even works if the path contains symlinks to
 # modules.
@@ -66,7 +67,8 @@ def BuildModel(self):
   successful.
   '''
   def DTCMilk(self, options):
-    def RunDTCMilk(q):
+    @timeout_decorator.timeout(self.timeout)
+    def RunDTCMilk():
       totalTimer = Timer()
 
       Log.Info("Loading dataset", self.verbose)
@@ -85,15 +87,15 @@ def RunDTCMilk(q):
         with totalTimer:
           self.model = self.model.train(trainData, labels)
       except Exception as e:
-        Log.Debug(e)
-        q.put(-1)
         return -1
 
       time = totalTimer.ElapsedTime()
-      q.put(time)
       return time
 
-    return timeout(RunDTCMilk, self.timeout)
+    try:
+      return RunDTCMilk()
+    except timeout_decorator.TimeoutError:
+      return -1
 
   '''
   Perform the Decision Tree Classifier. If the method has been

methods/milk/kmeans.py

@@ -7,6 +7,7 @@
 import os
 import sys
 import inspect
+import timeout_decorator
 
 # Import the util path, this method even works if the path contains symlinks to
 # modules.
@@ -44,7 +45,8 @@ def __init__(self, dataset, timeout=0, verbose=True):
   successful.
   '''
   def KMeansMilk(self, options):
-    def RunKMeansMilk(q):
+    @timeout_decorator.timeout(self.timeout)
+    def RunKMeansMilk():
       totalTimer = Timer()
 
       # Load input dataset.
@@ -71,12 +73,10 @@ def RunKMeansMilk(q):
       # Now do validation of options.
       if not clusters and len(self.dataset) != 2:
         Log.Fatal("Required option: Number of clusters or cluster locations.")
-        q.put(-1)
         return -1
       elif (not clusters or int(clusters) < 1) and len(self.dataset) != 2:
         Log.Fatal("Invalid number of clusters requested! Must be greater than"
             + " or equal to 1.")
-        q.put(-1)
         return -1
 
       m = 1000 if not maxIterations else int(maxIterations)
@@ -97,14 +97,15 @@ def RunKMeansMilk(q):
 
       except Exception as e:
         Log.Fatal("Exception: " + str(e))
-        q.put(-1)
         return -1
 
       time = totalTimer.ElapsedTime()
-      q.put(time)
       return time
 
-    return timeout(RunKMeansMilk, self.timeout)
+    try:
+      return RunKMeansMilk()
+    except timeout_decorator.TimeoutError:
+      return -1
 
   '''
   Perform K-Means Clustering. If the method has been successfully completed

methods/milk/logistic_regression.py

@@ -6,6 +6,7 @@
 import os
 import sys
 import inspect
+import timeout_decorator
 
 # Import the util path, this method even works if the path contains symlinks to
 # modules.
@@ -62,7 +63,8 @@ def BuildModel(self):
   successful.
   '''
   def LogisticRegressionMilk(self, options):
-    def RunLogisticRegressionMilk(q):
+    @timeout_decorator.timeout(self.timeout)
+    def RunLogisticRegressionMilk():
       totalTimer = Timer()
 
       Log.Info("Loading dataset", self.verbose)
@@ -79,14 +81,15 @@ def RunLogisticRegressionMilk(q):
         with totalTimer:
           self.model = self.model.train(trainData, labels)
       except Exception as e:
-        q.put(-1)
         return -1
 
       time = totalTimer.ElapsedTime()
-      q.put(time)
       return time
 
-    return timeout(RunLogisticRegressionMilk, self.timeout)
+    try:
+      return RunLogisticRegressionMilk()
+    except timeout_decorator.TimeoutError:
+      return -1
 
   '''
   Perform the Logistic Regression Classifier. If the method has been

methods/milk/random_forest.py

@@ -6,6 +6,7 @@
 import os
 import sys
 import inspect
+import timeout_decorator
 
 # Import the util path, this method even works if the path contains symlinks to
 # modules.
@@ -66,7 +67,8 @@ def BuildModel(self):
   successful.
   '''
   def RANDOMFORESTMilk(self, options):
-    def RunRANDOMFORESTMilk(q):
+    @timeout_decorator.timeout(self.timeout)
+    def RunRANDOMFORESTMilk():
       totalTimer = Timer()
 
       Log.Info("Loading dataset", self.verbose)
@@ -85,14 +87,15 @@ def RunRANDOMFORESTMilk(q):
         with totalTimer:
           self.model = self.model.train(trainData, labels)
       except Exception as e:
-        q.put(-1)
         return -1
 
       time = totalTimer.ElapsedTime()
-      q.put(time)
       return time
 
-    return timeout(RunRANDOMFORESTMilk, self.timeout)
+    try:
+      return RunRANDOMFORESTMilk()
+    except timeout_decorator.TimeoutError:
+      return -1
 
   '''
   Perform the Random Forest Classifier. If the method has been