Merge pull request #1928 from breznak/bench_tools

Bench tools

scripts/profiling/enc_profile.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+# ----------------------------------------------------------------------
+# Numenta Platform for Intelligent Computing (NuPIC)
+# Copyright (C) 2015, Numenta, Inc.  Unless you have an agreement
+# with Numenta, Inc., for a separate license for this software code, the
+# following terms and conditions apply:
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License version 3 as
+# published by the Free Software Foundation.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see http://www.gnu.org/licenses.
+#
+# http://numenta.org/licenses/
+# ----------------------------------------------------------------------
+
+## run python -m cProfile --sort cumtime $NUPIC/scripts/profiling/enc_profile.py [nMaxValue nEpochs]
+
+import sys
+import numpy
+# chose desired Encoder implementations to compare:
+from nupic.encoders.scalar import ScalarEncoder
+from nupic.encoders.random_distributed_scalar import RandomDistributedScalarEncoder as RDSE
+
+
+def profileEnc(maxValue, nRuns):
+  minV=0
+  maxV=nRuns
+  # generate input data
+  data=numpy.random.randint(minV, maxV+1, nRuns)
+  # instantiate measured encoders
+  encScalar = ScalarEncoder(w=21, minval=minV, maxval=maxV, resolution=1)
+  encRDSE = RDSE(resolution=1)
+
+  # profile!  
+  for d in data:
+    encScalar.encode(d)
+    encRDSE.encode(d)
+
+  print "Scalar n=",encScalar.n," RDSE n=",encRDSE.n
+
+
+
+if __name__ == "__main__":
+  maxV=500
+  epochs=10000
+  if len(sys.argv) == 3: # 2 args + name
+    columns=int(sys.argv[1])
+    epochs=int(sys.argv[2])
+
+  profileEnc(maxV, epochs)

scripts/profiling/sp_profile.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python
+# ----------------------------------------------------------------------
+# Numenta Platform for Intelligent Computing (NuPIC)
+# Copyright (C) 2015, Numenta, Inc.  Unless you have an agreement
+# with Numenta, Inc., for a separate license for this software code, the
+# following terms and conditions apply:
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License version 3 as
+# published by the Free Software Foundation.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see http://www.gnu.org/licenses.
+#
+# http://numenta.org/licenses/
+# ----------------------------------------------------------------------
+
+## run python -m cProfile --sort cumtime $NUPIC/scripts/profiling/sp_profile.py [nColumns nEpochs]
+
+import sys
+import numpy
+# chose desired SP implementation to compare:
+from nupic.research.spatial_pooler import SpatialPooler as PySP
+from nupic.bindings.algorithms import SpatialPooler as CppSP
+
+
+def profileSP(spClass, spDim, nRuns):
+  """
+  profiling performance of SpatialPooler (SP)
+  using the python cProfile module and ordered by cumulative time, 
+  see how to run on command-line above.
+
+  @param spClass implementation of SP (cpp, py, ..)
+  @param spDim number of columns in SP (in 1D, for 2D see colDim in code)
+  @param nRuns number of calls of the profiled code (epochs)
+  """
+  # you can change dimensionality here, eg to 2D
+  inDim = [10000, 1, 1]
+  colDim = [spDim, 1, 1]
+
+
+  # create SP instance to measure
+  # changing the params here affects the performance
+  sp = spClass(
+        inputDimensions=inDim,
+        columnDimensions=colDim,
+        potentialRadius=3,
+        potentialPct=0.5,
+        globalInhibition=False,
+        localAreaDensity=-1.0,
+        numActiveColumnsPerInhArea=3,
+        stimulusThreshold=1,
+        synPermInactiveDec=0.01,
+        synPermActiveInc=0.1,
+        synPermConnected=0.10,
+        minPctOverlapDutyCycle=0.1,
+        minPctActiveDutyCycle=0.1,
+        dutyCyclePeriod=10,
+        maxBoost=10.0,
+        seed=42,
+        spVerbosity=0)
+
+
+  # generate input data
+  dataDim = inDim
+  dataDim.append(nRuns)
+  data = numpy.random.randint(0, 2, dataDim).astype('float32')
+
+  for i in xrange(nRuns):
+    # new data every time, this is the worst case performance
+    # real performance would be better, as the input data would not be completely random
+    d = data[:,:,:,i]
+    activeArray = numpy.zeros(colDim)
+
+    # the actual function to profile!
+    sp.compute(d, True, activeArray)
+
+
+
+if __name__ == "__main__":
+  columns=2048
+  epochs=10000
+  # read params from command line
+  if len(sys.argv) == 3: # 2 args + name
+    columns=int(sys.argv[1])
+    epochs=int(sys.argv[2])
+
+  profileSP(CppSP, columns, epochs)

scripts/profiling/tp_profile.py

@@ -0,0 +1,67 @@
+#!/usr/bin/env python
+# ----------------------------------------------------------------------
+# Numenta Platform for Intelligent Computing (NuPIC)
+# Copyright (C) 2015, Numenta, Inc.  Unless you have an agreement
+# with Numenta, Inc., for a separate license for this software code, the
+# following terms and conditions apply:
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License version 3 as
+# published by the Free Software Foundation.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see http://www.gnu.org/licenses.
+#
+# http://numenta.org/licenses/
+# ----------------------------------------------------------------------
+
+## run python -m cProfile --sort cumtime $NUPIC/scripts/profiling/tp_profile.py [nColumns nEpochs]
+
+import sys
+import numpy
+# chose desired TP implementation to compare:
+from nupic.research.TP10X2 import TP10X2 as CppTP 
+from nupic.research.TP import TP as PyTP
+
+
+def profileTP(tpClass, tpDim, nRuns):
+  """
+  profiling performance of TemporalPooler (TP)
+  using the python cProfile module and ordered by cumulative time, 
+  see how to run on command-line above.
+
+  @param tpClass implementation of TP (cpp, py, ..)
+  @param tpDim number of columns in TP
+  @param nRuns number of calls of the profiled code (epochs)
+  """
+
+  # create TP instance to measure
+  tp = tpClass(numberOfCols=tpDim)
+
+  # generate input data
+  data = numpy.random.randint(0, 2, [tpDim, nRuns]).astype('float32')
+
+  for i in xrange(nRuns):
+    # new data every time, this is the worst case performance
+    # real performance would be better, as the input data would not be completely random
+    d = data[:,i]
+
+    # the actual function to profile!
+    tp.compute(d, True)
+
+
+
+if __name__ == "__main__":
+  columns=2048
+  epochs=10000
+  # read command line params
+  if len(sys.argv) == 3: # 2 args + name
+    columns=int(sys.argv[1])
+    epochs=int(sys.argv[2])
+
+  profileTP(CppTP, columns, epochs)