Fix OPF prediction models

docs/examples/network/complete-network-example.py

@@ -118,9 +118,8 @@ def runHotgym(numRecords):
   numRecords = min(numRecords, dataSource.getDataRowCount())
   network = createNetwork(dataSource)
 
-  # Set predicted field index. It needs to be the same index as the data source.
-  predictedIdx = dataSource.getFieldNames().index("consumption")
-  network.regions["sensor"].setParameter("predictedFieldIdx", predictedIdx)
+  # Set predicted field
+  network.regions["sensor"].setParameter("predictedField", "consumption")
 
   # Enable learning for all regions.
   network.regions["SP"].setParameter("learningMode", 1)

docs/examples/network/example-set-predicted-field.py

@@ -1,3 +1 @@
-predictedIdx = dataSource.getFieldNames().index("consumption")
-
-network.regions["sensor"].setParameter("predictedFieldIdx", predictedIdx)
+network.regions["sensor"].setParameter("predictedField", "consumption")

examples/network/hierarchy_network_demo.py

@@ -149,10 +149,7 @@ def createRecordSensor(network, name, dataSource):
   sensorRegion.encoder = createEncoder()
 
   # Specify which sub-encoder should be used for "actValueOut"
-  predictedFieldIdx = dataSource.getFieldNames().index("consumption")
-  network.regions[name].setParameter("predictedFieldIdx",
-                                     numpy.array([predictedFieldIdx],
-                                                dtype="uint32"))
+  network.regions[name].setParameter("predictedField", "consumption")
 
   # Specify the dataSource as a file record stream instance
   sensorRegion.dataSource = dataSource

src/nupic/frameworks/opf/htm_prediction_model.py

@@ -309,6 +309,13 @@ def setFieldStatistics(self, fieldStats):
     encoder.setFieldStats('',fieldStats)
 
 
+  def enableInference(self, inferenceArgs=None):
+    super(HTMPredictionModel, self).enableInference(inferenceArgs)
+    if inferenceArgs is not None and "predictedField" in inferenceArgs:
+      self._getSensorRegion().setParameter("predictedField",
+                                           inferenceArgs["predictedField"])
+
+
   def enableLearning(self):
     super(HTMPredictionModel, self).enableLearning()
     self.setEncoderLearning(True)
@@ -1165,6 +1172,10 @@ def __createCLANetwork(self, sensorParams, spEnable, spParams, tmEnable,
                                                       clParams))
       n.addRegion("Classifier", "py.%s" % str(clRegionName), json.dumps(clParams))
 
+      n.link("sensor", "Classifier", "UniformLink", "", srcOutput="actValueOut",
+             destInput="actValueIn")
+      n.link("sensor", "Classifier", "UniformLink", "", srcOutput="bucketIdxOut",
+             destInput="bucketIdxIn")
       n.link("sensor", "Classifier", "UniformLink", "", srcOutput="categoryOut",
              destInput="categoryIn")
 

src/nupic/regions/record_sensor.py

@@ -111,7 +111,7 @@ def getSpec(cls):
         actValueOut=dict(
           description="Actual value of the field to predict. The index of the "
                       "field to predict must be specified via the parameter "
-                      "predictedFieldIdx. If this parameter is not set, then "
+                      "predictedField. If this parameter is not set, then "
                       "actValueOut won't be populated.",
           dataType="Real32",
           count=0,
@@ -121,7 +121,7 @@ def getSpec(cls):
           description="Active index of the encoder bucket for the "
                       "actual value of the field to predict. The index of the "
                       "field to predict must be specified via the parameter "
-                      "predictedFieldIdx. If this parameter is not set, then "
+                      "predictedField. If this parameter is not set, then "
                       "actValueOut won't be populated.",
           dataType="UInt64",
           count=0,
@@ -206,15 +206,13 @@ def getSpec(cls):
           accessMode="ReadWrite",
           count=1,
           constraints=""),
-        predictedFieldIdx=dict(
-          description="Index of the field to be predicted. Needs to be "
-                      "consistent with the data source indexing. "
-                      "Default value is < 0 which means that no particular "
-                      "field is selected. This will result in the outputs "
-                      "actValueOut and bucketIdxOut not being populated.",
-          dataType="UInt32",
+        predictedField=dict(
+          description="The field to be predicted. This will result in the "
+                      "outputs actValueOut and bucketIdxOut not being "
+                      "populated.",
+          dataType="Byte",
           accessMode="ReadWrite",
-          count=1,
+          count=0,
           defaultValue=-1,
           constraints=""),
         topDownMode=dict(
@@ -246,10 +244,10 @@ def __init__(self, verbosity=0, numCategories=1):
     self.numCategories = numCategories
     self._iterNum = 0
 
-    # Optional index of the field for which we want to populate bucketIdxOut
-    # and actValueOut. If predictedFieldIdx < 0, then bucketIdxOut and
+    # Optional field for which we want to populate bucketIdxOut
+    # and actValueOut. If predictedField is None, then bucketIdxOut and
     # actValueOut won't be populated.
-    self.predictedFieldIdx = -1
+    self.predictedField = None
 
     # lastRecord is the last record returned. Used for debugging only
     self.lastRecord = None
@@ -380,14 +378,8 @@ def compute(self, inputs, outputs):
       self.encoder.encodeIntoArray(data, outputs["dataOut"])
 
       # If there is a field to predict, set bucketIdxOut and actValueOut.
-      if self.predictedFieldIdx >= 0:
-        fields = self.dataSource.getFieldNames()
-        if self.predictedFieldIdx >= len(fields):
-          raise ValueError("predictedFieldIdx (%s) must be strictly less than "
-                           "the number of fields (%s). Fields: %s."
-                           % (self.predictedFieldIdx, len(fields), fields))
-        predictedField = fields[self.predictedFieldIdx]
-        encoders = [e for e in self.encoder.encoders if e[0] == predictedField]
+      if self.predictedField is not None:
+        encoders = [e for e in self.encoder.encoders if e[0] == self.predictedField]
         if len(encoders) == 0:
           raise ValueError("There is no encoder for set for the predicted "
                            "field: %s" % predictedField)
@@ -397,9 +389,12 @@ def compute(self, inputs, outputs):
         else:
           encoder = encoders[0][1]
 
-        actualValue = data[predictedField]
+        actualValue = data[self.predictedField]
         outputs["bucketIdxOut"][:] = encoder.getBucketIndices(actualValue)
-        outputs["actValueOut"][:] = actualValue
+        if isinstance(actualValue, str):
+          outputs["actValueOut"][:] = encoder.getBucketIndices(actualValue)
+        else:
+          outputs["actValueOut"][:] = actualValue
 
       # Write out the scalar values obtained from they data source.
       outputs["sourceOut"][:] = self.encoder.getScalars(data)
@@ -594,8 +589,8 @@ def setParameter(self, parameterName, index, parameterValue):
     """
     if parameterName == 'topDownMode':
       self.topDownMode = parameterValue
-    elif parameterName == 'predictedFieldIdx':
-      self.predictedFieldIdx = parameterValue
+    elif parameterName == 'predictedField':
+      self.predictedField = parameterValue
     else:
       raise Exception('Unknown parameter: ' + parameterName)
 

tests/unit/nupic/regions/record_sensor_region_test.py

@@ -54,8 +54,7 @@ def _createNetwork():
   sensorRegion.dataSource = dataSource
 
   # Get and set what field index we want to predict.
-  predictedIdx = dataSource.getFieldNames().index('consumption')
-  network.regions['sensor'].setParameter('predictedFieldIdx', predictedIdx)
+  network.regions['sensor'].setParameter('predictedField', 'consumption')
 
   return network
 

tests/unit/nupic/regions/sdr_classifier_region_test.py

@@ -56,8 +56,7 @@ def _createNetwork():
   sensorRegion.dataSource = dataSource
 
   # Get and set what field index we want to predict.
-  predictedIdx = dataSource.getFieldNames().index('consumption')
-  network.regions['sensor'].setParameter('predictedFieldIdx', predictedIdx)
+  network.regions['sensor'].setParameter('predictedField', 'consumption')
 
   return network