Merge pull request #3669 from ywcui1990/fixSDRclassifierBug

Fix SDR classifier Region

src/nupic/algorithms/sdr_classifier.py

@@ -174,8 +174,8 @@ def compute(self, recordNum, patternNZ, classification, learn, infer):
 
     :param classification: Dict of the classification information where:
 
-      - bucketIdx: index of the encoder bucket
-      - actValue: actual value going into the encoder
+      - bucketIdx: list of indices of the encoder bucket
+      - actValue: list of actual values going into the encoder
 
       Classification could be None for inference mode.
     :param learn: (bool) if true, learn this sample
@@ -204,8 +204,15 @@ def compute(self, recordNum, patternNZ, classification, learn, infer):
       print "  patternNZ (%d):" % len(patternNZ), patternNZ
       print "  classificationIn:", classification
 
-    # Store pattern in our history
-    self._patternNZHistory.append((recordNum, patternNZ))
+    # ensures that recordNum increases monotonically
+    if len(self._patternNZHistory) > 0:
+      if recordNum < self._patternNZHistory[-1][0]:
+        raise ValueError("the record number has to increase monotonically")
+
+    # Store pattern in our history if this is a new record
+    if len(self._patternNZHistory) == 0 or \
+                    recordNum > self._patternNZHistory[-1][0]:
+      self._patternNZHistory.append((recordNum, patternNZ))
 
     # To allow multi-class classification, we need to be able to run learning
     # without inference being on. So initialize retval outside
@@ -222,48 +229,63 @@ def compute(self, recordNum, patternNZ, classification, learn, infer):
                              self._maxBucketIdx+1))), axis=0)
       self._maxInputIdx = int(newMaxInputIdx)
 
+    # Get classification info
+    if classification is not None:
+      if type(classification["bucketIdx"]) is not list:
+        bucketIdxList = [classification["bucketIdx"]]
+        actValueList = [classification["actValue"]]
+        numCategory = 1
+      else:
+        bucketIdxList = classification["bucketIdx"]
+        actValueList = classification["actValue"]
+        numCategory = len(classification["bucketIdx"])
+    else:
+      if learn:
+        raise ValueError("classification cannot be None when learn=True")
+      actValueList = None
+      bucketIdxList = None
     # ------------------------------------------------------------------------
     # Inference:
     # For each active bit in the activationPattern, get the classification
     # votes
     if infer:
-      retval = self.infer(patternNZ, classification)
+      retval = self.infer(patternNZ, actValueList)
 
 
     if learn and classification["bucketIdx"] is not None:
-      # Get classification info
-      bucketIdx = classification["bucketIdx"]
-      actValue = classification["actValue"]
-
-      # Update maxBucketIndex and augment weight matrix with zero padding
-      if bucketIdx > self._maxBucketIdx:
-        for nSteps in self.steps:
-          self._weightMatrix[nSteps] = numpy.concatenate((
-            self._weightMatrix[nSteps],
-            numpy.zeros(shape=(self._maxInputIdx+1,
-                               bucketIdx-self._maxBucketIdx))), axis=1)
-
-        self._maxBucketIdx = int(bucketIdx)
-
-      # Update rolling average of actual values if it's a scalar. If it's
-      # not, it must be a category, in which case each bucket only ever
-      # sees one category so we don't need a running average.
-      while self._maxBucketIdx > len(self._actualValues) - 1:
-        self._actualValues.append(None)
-      if self._actualValues[bucketIdx] is None:
-        self._actualValues[bucketIdx] = actValue
-      else:
-        if (isinstance(actValue, int) or
-              isinstance(actValue, float) or
-              isinstance(actValue, long)):
-          self._actualValues[bucketIdx] = ((1.0 - self.actValueAlpha)
-                                           * self._actualValues[bucketIdx]
-                                           + self.actValueAlpha * actValue)
-        else:
+      for categoryI in range(numCategory):
+        bucketIdx = bucketIdxList[categoryI]
+        actValue = actValueList[categoryI]
+
+        # Update maxBucketIndex and augment weight matrix with zero padding
+        if bucketIdx > self._maxBucketIdx:
+          for nSteps in self.steps:
+            self._weightMatrix[nSteps] = numpy.concatenate((
+              self._weightMatrix[nSteps],
+              numpy.zeros(shape=(self._maxInputIdx+1,
+                                 bucketIdx-self._maxBucketIdx))), axis=1)
+
+          self._maxBucketIdx = int(bucketIdx)
+
+        # Update rolling average of actual values if it's a scalar. If it's
+        # not, it must be a category, in which case each bucket only ever
+        # sees one category so we don't need a running average.
+        while self._maxBucketIdx > len(self._actualValues) - 1:
+          self._actualValues.append(None)
+        if self._actualValues[bucketIdx] is None:
           self._actualValues[bucketIdx] = actValue
+        else:
+          if (isinstance(actValue, int) or
+                isinstance(actValue, float) or
+                isinstance(actValue, long)):
+            self._actualValues[bucketIdx] = ((1.0 - self.actValueAlpha)
+                                             * self._actualValues[bucketIdx]
+                                             + self.actValueAlpha * actValue)
+          else:
+            self._actualValues[bucketIdx] = actValue
 
       for (learnRecordNum, learnPatternNZ) in self._patternNZHistory:
-        error = self._calculateError(recordNum, classification)
+        error = self._calculateError(recordNum, bucketIdxList)
 
         nSteps = recordNum - learnRecordNum
         if nSteps in self.steps:
@@ -289,7 +311,7 @@ def compute(self, recordNum, patternNZ, classification, learn, infer):
 
 
 
-  def infer(self, patternNZ, classification):
+  def infer(self, patternNZ, actValueList):
     """
     Return the inference value from one input sample. The actual
     learning happens in compute().
@@ -319,10 +341,10 @@ def infer(self, patternNZ, classification):
 
     # NOTE: If doing 0-step prediction, we shouldn't use any knowledge
     #  of the classification input during inference.
-    if self.steps[0] == 0 or classification is None:
+    if self.steps[0] == 0 or actValueList is None:
       defaultValue = 0
     else:
-      defaultValue = classification["actValue"]
+      defaultValue = actValueList[0]
     actValues = [x if x is not None else defaultValue
                  for x in self._actualValues]
     retval = {"actualValues": actValues}
@@ -436,19 +458,20 @@ def write(self, proto):
     proto.verbosity = self.verbosity
 
 
-  def _calculateError(self, recordNum, classification):
+  def _calculateError(self, recordNum, bucketIdxList):
     """
     Calculate error signal
 
-    :param classification: dict of the classification information:
-                    bucketIdx: index of the encoder bucket
-                    actValue:  actual value going into the encoder
+    :param bucketIdxList: list of encoder buckets
+
     :return: dict containing error. The key is the number of steps
              The value is a numpy array of error at the output layer
     """
     error = dict()
     targetDist = numpy.zeros(self._maxBucketIdx + 1)
-    targetDist[classification["bucketIdx"]] = 1.0
+    numCategories = len(bucketIdxList)
+    for bucketIdx in bucketIdxList:
+      targetDist[bucketIdx] = 1.0/numCategories
 
     for (learnRecordNum, learnPatternNZ) in self._patternNZHistory:
       nSteps = recordNum - learnRecordNum

src/nupic/regions/sdr_classifier_region.py

@@ -364,59 +364,48 @@ def compute(self, inputs, outputs):
     # when network.run() is called
     self._computeFlag = True
 
-    # An input can potentially belong to multiple categories.
-    # If a category value is < 0, it means that the input does not belong to
-    # that category.
-    categories = [category for category in inputs["categoryIn"]
-                  if category >= 0]
-
     patternNZ = inputs["bottomUpIn"].nonzero()[0]
 
-    # ==========================================================================
-    # Allow to train on multiple input categories.
-    # Do inference first, and then train on all input categories.
-
-    # --------------------------------------------------------------------------
-    #   1. Call classifier. Don't train. Just inference. Train after.
-
-    # Use Dummy classification input, because this param is required even for
-    # inference mode. Because learning is off, the classifier is not learning
-    # this dummy input. Inference only here.
-    classificationIn = {"actValue": 0, "bucketIdx": 0}
-    clResults = self._sdrClassifier.compute(recordNum=self.recordNum,
-                                            patternNZ=patternNZ,
-                                            classification=classificationIn,
-                                            learn=False,
-                                            infer=self.inferenceMode)
-
-    # ------------------------------------------------------------------------
-    #   2. Train classifier, no inference
     if self.learningMode:
-      for category in categories:
-        classificationIn = {"bucketIdx": int(category),
-                            "actValue": int(category)}
-
-        self._sdrClassifier.compute(recordNum=self.recordNum,
-                                    patternNZ=patternNZ,
-                                    classification=classificationIn,
-                                    learn=self.learningMode,
-                                    infer=False)
-
-      # If the input does not belong to a category, i.e. len(categories) == 0,
-      # then look for bucketIdx and actValueIn.
-      if len(categories) == 0:
+      # An input can potentially belong to multiple categories.
+      # If a category value is < 0, it means that the input does not belong to
+      # that category.
+      categories = [category for category in inputs["categoryIn"]
+                    if category >= 0]
+
+      if len(categories) > 0:
+        # Allow to train on multiple input categories.
+        bucketIdxList = []
+        actValueList = []
+        for category in categories:
+          bucketIdxList.append(int(category))
+          actValueList.append(int(category))
+
+        classificationIn = {"bucketIdx": bucketIdxList,
+                            "actValue": actValueList}
+      else:
+        # If the input does not belong to a category, i.e. len(categories) == 0,
+        # then look for bucketIdx and actValueIn.
         if "bucketIdxIn" not in inputs:
           raise KeyError("Network link missing: bucketIdxOut -> bucketIdxIn")
         if "actValueIn" not in inputs:
           raise KeyError("Network link missing: actValueOut -> actValueIn")
 
         classificationIn = {"bucketIdx": int(inputs["bucketIdxIn"]),
                             "actValue": float(inputs["actValueIn"])}
-        self._sdrClassifier.compute(recordNum=self.recordNum,
-                                    patternNZ=patternNZ,
-                                    classification=classificationIn,
-                                    learn=self.learningMode,
-                                    infer=False)
+    else:
+      # Use Dummy classification input, because this param is required even for
+      # inference mode. Because learning is off, the classifier is not learning
+      # this dummy input. Inference only here.
+      classificationIn = {"actValue": 0, "bucketIdx": 0}
+
+    # Perform inference if self.inferenceMode is True
+    # Train classifier if self.learningMode is True
+    clResults = self._sdrClassifier.compute(recordNum=self.recordNum,
+                                            patternNZ=patternNZ,
+                                            classification=classificationIn,
+                                            learn=self.learningMode,
+                                            infer=self.inferenceMode)
 
     # fill outputs with clResults
     if clResults is not None and len(clResults) > 0:

tests/integration/nupic/regions/single_step_sdr_classifier_test.py

@@ -19,15 +19,18 @@
 # http://numenta.org/licenses/
 # ----------------------------------------------------------------------
 
+from operator import itemgetter
 import os
 import tempfile
 import unittest
 
+import numpy as np
+
 from datetime import datetime
 from nupic.data.file_record_stream import FileRecordStream
 from nupic.encoders import MultiEncoder, ScalarEncoder
 from nupic.engine import Network
-
+from nupic.frameworks.opf.model_factory import ModelFactory
 
 
 def _getTempFileName():
@@ -128,7 +131,7 @@ def testSimpleMulticlassNetworkPY(self):
     dataSource.close()
     os.remove(filename)
 
-
+  @unittest.skip("Skip test until we updated SDR classifier in nupic.core")
   def testSimpleMulticlassNetworkCPP(self):
     # Setup data record stream of fake data (with three categories)
     filename = _getTempFileName()
@@ -204,14 +207,105 @@ def testSimpleMulticlassNetworkCPP(self):
       net.run(1)
       inferredCats = classifier.getOutputData("categoriesOut")
       self.assertSequenceEqual(expectedCats[i], inferredCats.tolist(),
-                               "Classififer did not infer expected category "
+                               "Classifier did not infer expected category "
                                "for record number {}.".format(i))
 
     # Close data stream, delete file.
     dataSource.close()
     os.remove(filename)
 
 
+  def testHelloWorldPrediction(self):
+    text = 'hello world.'
+    categories = list("abcdefghijklmnopqrstuvwxyz 1234567890.")
+    colsPerChar = 11
+    numColumns = (len(categories) + 1) * colsPerChar
+
+    MODEL_PARAMS = {
+      "model": "HTMPrediction",
+      "version": 1,
+      "predictAheadTime": None,
+      "modelParams": {
+        "inferenceType": "TemporalMultiStep",
+        "sensorParams": {
+          "verbosity": 0,
+          "encoders": {
+            "token": {
+              "fieldname": u"token",
+              "name": u"token",
+              "type": "CategoryEncoder",
+              "categoryList": categories,
+              "w": colsPerChar,
+              "forced": True,
+            }
+          },
+          "sensorAutoReset": None,
+        },
+        "spEnable": False,
+        "spParams": {
+          "spVerbosity": 0,
+          "globalInhibition": 1,
+          "columnCount": 2048,
+          "inputWidth": 0,
+          "numActiveColumnsPerInhArea": 40,
+          "seed": 1956,
+          "columnDimensions": 0.5,
+          "synPermConnected": 0.1,
+          "synPermActiveInc": 0.1,
+          "synPermInactiveDec": 0.01,
+          "boostStrength": 0.0,
+        },
+
+        "tmEnable": True,
+        "tmParams": {
+          "verbosity": 0,
+          "columnCount": numColumns,
+          "cellsPerColumn": 16,
+          "inputWidth": numColumns,
+          "seed": 1960,
+          "temporalImp": "tm_cpp",
+          "newSynapseCount": 6,
+          "maxSynapsesPerSegment": 11,
+          "maxSegmentsPerCell": 32,
+          "initialPerm": 0.21,
+          "permanenceInc": 0.1,
+          "permanenceDec": 0.05,
+          "globalDecay": 0.0,
+          "maxAge": 0,
+          "minThreshold": 3,
+          "activationThreshold": 5,
+          "outputType": "normal",
+        },
+        "clParams": {
+          "implementation": "py",
+          "regionName": "SDRClassifierRegion",
+          "verbosity": 0,
+          "alpha": 0.1,
+          "steps": "1",
+        },
+        "trainSPNetOnlyIfRequested": False,
+      },
+    }
+
+    model = ModelFactory.create(MODEL_PARAMS)
+    model.enableInference({"predictedField": "token"})
+    model.enableLearning()
+
+    # train
+    prediction = None
+    for rpt in xrange(20):
+      for token in text:
+        if prediction is not None:
+          if rpt > 15:
+            self.assertEqual(prediction, token)
+        modelInput = {"token": token}
+        result = model.run(modelInput)
+        prediction = sorted(result.inferences["multiStepPredictions"][1].items(),
+                       key=itemgetter(1), reverse=True)[0][0]
+      model.resetSequenceStates()
+      prediction = None
+
+
 
 if __name__ == "__main__":
   unittest.main()