def calc(self, inputData, inputMask=None, inputState=None, functionTable=None, performanceTable=None):
"""User interface to quickly make and return a plot.
This method is intended for interactive use, since it is more
laborious to construct a DataTable by hand.
This method modifies the input FunctionTable.
Note that PmmlCalculables return a DataTable from C{calc},
wheras PlotCanvas returns an SvgBinding.
@type inputData: dict
@param inputData: Dictionary from field names to data, as required by the DataTable constructor.
@type inputMask: dict or None
@param inputMask: Dictionary from field names to missing value masks, as required by the DataTable constructor.
@type inputState: DataTableState or None
@param inputState: Calculation state, used to continue a calculation over many C{calc} calls.
@type functionTable: FunctionTable or None
@param functionTable: A table of functions. Initially, it contains only the built-in functions, but any user functions defined in PMML would be added to it.
@type performanceTable: PerformanceTable or None
@param performanceTable: A PerformanceTable for measuring the efficiency of the calculation.
@rtype: SvgBinding
@return: A complete SVG image representing the fully drawn plot.
"""
if functionTable is None:
functionTable = FunctionTable()
if performanceTable is None:
performanceTable = FakePerformanceTable()
performanceTable.begin("make DataTable")
dataTable = DataTable(self, inputData, inputMask, inputState)
performanceTable.end("make DataTable")
return self.makePlot(dataTable, functionTable, performanceTable)
def calc(self, inputData, inputMask=None, performanceTable=None):
"""Build a DataTable from the input data and then run k-means
clustering on it to produce a ClusteringModel.
This method is intended for interactive use, since it is more
laborious to construct a DataTable by hand.
Modifies and returns C{self.clusteringModel}.
@type inputData: dict
@param inputData: Dictionary from field names to data, as required by the DataTable constructor.
@type inputMask: dict or None
@param inputMask: Dictionary from field names to missing value masks, as required by the DataTable constructor.
@type inputState: DataTableState or None
@param inputState: Calculation state, used to continue a calculation over many C{calc} calls.
@type performanceTable: PerformanceTable or None
@param performanceTable: A PerformanceTable for measuring the efficiency of the calculation.
@rtype: PmmlBinding
@return: The PMML model representing the result of the k-means clustering.
"""
if performanceTable is None:
performanceTable = FakePerformanceTable()
performanceTable.begin("make DataTable")
dataTable = DataTable(self.clusteringModel, inputData, inputMask, None)
performanceTable.end("make DataTable")
self.smallTrials(dataTable, performanceTable=performanceTable)
self.optimize([dataTable], performanceTable=performanceTable)
return self.clusteringModel
def calc(self, inputData, inputMask=None, performanceTable=None):
"""Build a DataTable from the input data and then run k-means
clustering on it to produce a ClusteringModel.
This method is intended for interactive use, since it is more
laborious to construct a DataTable by hand.
Modifies and returns C{self.clusteringModel}.
@type inputData: dict
@param inputData: Dictionary from field names to data, as required by the DataTable constructor.
@type inputMask: dict or None
@param inputMask: Dictionary from field names to missing value masks, as required by the DataTable constructor.
@type inputState: DataTableState or None
@param inputState: Calculation state, used to continue a calculation over many C{calc} calls.
@type performanceTable: PerformanceTable or None
@param performanceTable: A PerformanceTable for measuring the efficiency of the calculation.
@rtype: PmmlBinding
@return: The PMML model representing the result of the k-means clustering.
"""
if performanceTable is None:
performanceTable = FakePerformanceTable()
performanceTable.begin("make DataTable")
dataTable = DataTable(self.clusteringModel, inputData, inputMask, None)
performanceTable.end("make DataTable")
self.smallTrials(dataTable, performanceTable=performanceTable)
self.optimize([dataTable], performanceTable=performanceTable)
return self.clusteringModel
def calc(self, inputData, inputMask=None, inputState=None, functionTable=None, performanceTable=None):
"""Build a DataTable from the input data and then perform a
calculation.
This method is intended for interactive use, since it is more
laborious to construct a DataTable by hand.
This method modifies the input FunctionTable.
@type inputData: dict
@param inputData: Dictionary from field names to data, as required by the DataTable constructor.
@type inputMask: dict or None
@param inputMask: Dictionary from field names to missing value masks, as required by the DataTable constructor.
@type inputState: DataTableState or None
@param inputState: Calculation state, used to continue a calculation over many C{calc} calls.
@type functionTable: FunctionTable or None
@param functionTable: A table of functions. Initially, it contains only the built-in functions, but any user functions defined in PMML would be added to it.
@type performanceTable: PerformanceTable or None
@param performanceTable: A PerformanceTable for measuring the efficiency of the calculation.
@rtype: DataTable
@return: A DataTable containing the result.
"""
if functionTable is None:
functionTable = FunctionTable()
if performanceTable is None:
performanceTable = FakePerformanceTable()
performanceTable.begin("make DataTable")
dataTable = DataTable(self, inputData, inputMask, inputState)
performanceTable.end("make DataTable")
self.calculate(dataTable, functionTable, performanceTable)
return dataTable
def makePlot(self, dataTable, functionTable=None, performanceTable=None):
"""Construct a plot from the data and return a complete SVG
image.
@type dataTable: DataTable
@param dataTable: Contains the data to plot.
@type functionTable: FunctionTable
@param functionTable: Defines functions that may be used to transform data for plotting.
@type performanceTable: PerformanceTable
@param performanceTable: Measures and records performance (time and memory consumption) of the drawing process.
@rtype: SvgBinding
@return: A complete SVG image representing the fully drawn plot.
"""
if functionTable is None:
functionTable = FunctionTable()
if performanceTable is None:
performanceTable = FakePerformanceTable()
svg = SvgBinding.elementMaker
performanceTable.begin("PlotCanvas")
width = self.get("width", defaultFromXsd=True, convertType=True)
height = self.get("height", defaultFromXsd=True, convertType=True)
style = self.get("style", defaultFromXsd=True)
attrib = self.globalAttrib.copy()
svgId = self.get("svgId")
if svgId is not None:
attrib["id"] = svgId
attrib["viewBox"] = "0 0 %d %d" % (width, height)
attrib["style"] = style
attrib["font-family"] = self.get("font-family", defaultFromXsd=True)
attrib["font-weight"] = self.get("font-weight", defaultFromXsd=True)
plotCoordinates = PlotCoordinates()
plotContentBox = PlotContentBox(0, 0, width, height)
plotDefinitions = PlotDefinitions()
performanceTable.pause("PlotCanvas")
content = [
x.frame(dataTable, functionTable, performanceTable,
plotCoordinates, plotContentBox, plotDefinitions)
for x in self.childrenOfClass(PmmlPlotFrame)
]
performanceTable.unpause("PlotCanvas")
content = [svg.defs(*plotDefinitions.values())] + content
performanceTable.end("PlotCanvas")
return svg.svg(*content, **attrib)
def makePlot(self, dataTable, functionTable=None, performanceTable=None):
"""Construct a plot from the data and return a complete SVG
image.
@type dataTable: DataTable
@param dataTable: Contains the data to plot.
@type functionTable: FunctionTable
@param functionTable: Defines functions that may be used to transform data for plotting.
@type performanceTable: PerformanceTable
@param performanceTable: Measures and records performance (time and memory consumption) of the drawing process.
@rtype: SvgBinding
@return: A complete SVG image representing the fully drawn plot.
"""
if functionTable is None:
functionTable = FunctionTable()
if performanceTable is None:
performanceTable = FakePerformanceTable()
svg = SvgBinding.elementMaker
performanceTable.begin("PlotCanvas")
width = self.get("width", defaultFromXsd=True, convertType=True)
height = self.get("height", defaultFromXsd=True, convertType=True)
style = self.get("style", defaultFromXsd=True)
attrib = self.globalAttrib.copy()
svgId = self.get("svgId")
if svgId is not None:
attrib["id"] = svgId
attrib["viewBox"] = "0 0 %d %d" % (width, height)
attrib["style"] = style
attrib["font-family"] = self.get("font-family", defaultFromXsd=True)
attrib["font-weight"] = self.get("font-weight", defaultFromXsd=True)
plotCoordinates = PlotCoordinates()
plotContentBox = PlotContentBox(0, 0, width, height)
plotDefinitions = PlotDefinitions()
performanceTable.pause("PlotCanvas")
content = [x.frame(dataTable, functionTable, performanceTable, plotCoordinates, plotContentBox, plotDefinitions) for x in self.childrenOfClass(PmmlPlotFrame)]
performanceTable.unpause("PlotCanvas")
content = [svg.defs(*plotDefinitions.values())] + content
performanceTable.end("PlotCanvas")
return svg.svg(*content, **attrib)
def calculate(self, dataTable, functionTable=None, performanceTable=None):
"""Calculate a DerivedField.
This method modifies the input DataTable.
If the data types between the DerivedField and its EXPRESSION
are not matched, the DerivedField will need to cast the output.
This is a potentially expensive and often unwanted operation.
When a DerivedField casts, it reports the cast in the
PerformanceTable with DerivedField name, to help the user
debug their PMML.
@type dataTable: DataTable
@param dataTable: The pre-built DataTable.
@type functionTable: FunctionTable or None
@param functionTable: A table of functions. Initially, it contains only the built-in functions, but any user functions defined in PMML would be added to it.
@type performanceTable: PerformanceTable or None
@param performanceTable: A PerformanceTable for measuring the efficiency of the calculation.
@rtype: DataTable
@return: A DataTable containing the result, usually a modified version of the input.
"""
if functionTable is None:
functionTable = FunctionTable()
if performanceTable is None:
performanceTable = FakePerformanceTable()
dataColumn = self.childOfClass(PmmlExpression).evaluate(dataTable, functionTable, performanceTable)
performanceTable.begin("DerivedField")
dataType = dataColumn.fieldType.dataType
optype = dataColumn.fieldType.optype
if self.get("dataType", dataType) == dataType and self.get("optype", optype) == optype and len(self.childrenOfTag("Value")) == 0:
dataTable.fields[self.name] = dataColumn
else:
performanceTable.begin("cast (\"%s\")" % self.name)
dataTable.fields[self.name] = FieldCastMethods.cast(FieldType(self), dataColumn)
performanceTable.end("cast (\"%s\")" % self.name)
performanceTable.end("DerivedField")
return dataTable.fields[self.name]
def calc(self,
inputData,
inputMask=None,
inputState=None,
functionTable=None,
performanceTable=None):
"""User interface to quickly make and return a plot.
This method is intended for interactive use, since it is more
laborious to construct a DataTable by hand.
This method modifies the input FunctionTable.
Note that PmmlCalculables return a DataTable from C{calc},
wheras PlotCanvas returns an SvgBinding.
@type inputData: dict
@param inputData: Dictionary from field names to data, as required by the DataTable constructor.
@type inputMask: dict or None
@param inputMask: Dictionary from field names to missing value masks, as required by the DataTable constructor.
@type inputState: DataTableState or None
@param inputState: Calculation state, used to continue a calculation over many C{calc} calls.
@type functionTable: FunctionTable or None
@param functionTable: A table of functions. Initially, it contains only the built-in functions, but any user functions defined in PMML would be added to it.
@type performanceTable: PerformanceTable or None
@param performanceTable: A PerformanceTable for measuring the efficiency of the calculation.
@rtype: SvgBinding
@return: A complete SVG image representing the fully drawn plot.
"""
if functionTable is None:
functionTable = FunctionTable()
if performanceTable is None:
performanceTable = FakePerformanceTable()
performanceTable.begin("make DataTable")
dataTable = DataTable(self, inputData, inputMask, inputState)
performanceTable.end("make DataTable")
return self.makePlot(dataTable, functionTable, performanceTable)
def smallTrials(self,
dataTable,
numberOfTrials=5,
recordsPerTrial=100,
performanceTable=None):
"""Improve the initial seed with a few small trials on random subsets of the data.
Modifies C{self.clusteringModel}.
@type dataTable: DataTable
@param dataTable: The input data.
@type numberOfTrials: int
@param numberOfTrials: The number of independent trials with the same number of C{recordsPerTrial}. The trial with the smallest sum of in-cluster variances wins.
@type recordsPerTrial: int
@param recordsPerTrial: The number of rows to randomly select from the DataTable in each trial.
@type performanceTable: PerformanceTable or None
@param performanceTable: A PerformanceTable for measuring the efficiency of the calculation.
"""
if performanceTable is None:
performanceTable = FakePerformanceTable()
performanceTable.begin("smallTrials")
mapReduce = self.mapReduce()
self.KMeansMapReduceApplication.metadata[
"ClusteringModel"] = copy.deepcopy(
self.KMeansMapReduceApplication.metadata["ClusteringModel"])
bestVariance = None
bestSeed = None
for trialNumber in xrange(numberOfTrials):
indexes = random.sample(xrange(len(dataTable)), recordsPerTrial)
subTable = dataTable.subTable(
NP("array", indexes, dtype=NP.dtype(int)))
self.randomSeeds(dataTable)
mapReduce.metadata["ClusteringModel"] = self.clusteringModel
outputRecords, outputKeyValues, numberOfIterations = mapReduce.run(
[subTable],
parallel=False,
frozenClass=False,
numberOfMappers=1,
numberOfReducers=1,
iterationLimit=self.iterationLimit)
for extension in self.clusteringModel.xpath(
"pmml:Extension[@name='iterations.smallTrials']"):
extension["value"] = repr(
int(extension["value"]) + numberOfIterations)
mapReduce.metadata["ClusteringModel"]["modelName"] = "smallTrials"
mapReduce.metadata["ClusteringModel"].subFields = dict(
mapReduce.metadata["ClusteringModel"].subFields)
mapReduce.metadata["ClusteringModel"].subFields.update(
{"affinity": True})
mapReduce.metadata["ClusteringModel"].calculate(subTable)
data = subTable.fields["smallTrials.affinity"].data
mask = subTable.fields["smallTrials.affinity"].mask
if mask is None:
variance = NP(data**2).sum() / float(len(subTable))
else:
selection = NP(mask == defs.VALID)
denom = NP("count_nonzero", selection)
if denom > 0:
variance = NP(data[selection]**2).sum() / float(denom)
else:
variance = None
if variance is not None and (bestVariance is None
or variance < bestVariance):
bestVariance = variance
bestSeed = mapReduce.metadata["clusterVectors"]
if bestSeed is not None:
self.explicitSeeds(bestSeed)
performanceTable.end("smallTrials")
def smallTrials(self, dataTable, numberOfTrials=5, recordsPerTrial=100, performanceTable=None):
"""Improve the initial seed with a few small trials on random subsets of the data.
Modifies C{self.clusteringModel}.
@type dataTable: DataTable
@param dataTable: The input data.
@type numberOfTrials: int
@param numberOfTrials: The number of independent trials with the same number of C{recordsPerTrial}. The trial with the smallest sum of in-cluster variances wins.
@type recordsPerTrial: int
@param recordsPerTrial: The number of rows to randomly select from the DataTable in each trial.
@type performanceTable: PerformanceTable or None
@param performanceTable: A PerformanceTable for measuring the efficiency of the calculation.
"""
if performanceTable is None:
performanceTable = FakePerformanceTable()
performanceTable.begin("smallTrials")
mapReduce = self.mapReduce()
self.KMeansMapReduceApplication.metadata["ClusteringModel"] = copy.deepcopy(self.KMeansMapReduceApplication.metadata["ClusteringModel"])
bestVariance = None
bestSeed = None
for trialNumber in xrange(numberOfTrials):
indexes = random.sample(xrange(len(dataTable)), recordsPerTrial)
subTable = dataTable.subTable(NP("array", indexes, dtype=NP.dtype(int)))
self.randomSeeds(dataTable)
mapReduce.metadata["ClusteringModel"] = self.clusteringModel
outputRecords, outputKeyValues, numberOfIterations = mapReduce.run([subTable], parallel=False, frozenClass=False, numberOfMappers=1, numberOfReducers=1, iterationLimit=self.iterationLimit)
for extension in self.clusteringModel.xpath("pmml:Extension[@name='iterations.smallTrials']"):
extension["value"] = repr(int(extension["value"]) + numberOfIterations)
mapReduce.metadata["ClusteringModel"]["modelName"] = "smallTrials"
mapReduce.metadata["ClusteringModel"].subFields = dict(mapReduce.metadata["ClusteringModel"].subFields)
mapReduce.metadata["ClusteringModel"].subFields.update({"affinity": True})
mapReduce.metadata["ClusteringModel"].calculate(subTable)
data = subTable.fields["smallTrials.affinity"].data
mask = subTable.fields["smallTrials.affinity"].mask
if mask is None:
variance = NP(data**2).sum() / float(len(subTable))
else:
selection = NP(mask == defs.VALID)
denom = NP("count_nonzero", selection)
if denom > 0:
variance = NP(data[selection]**2).sum() / float(denom)
else:
variance = None
if variance is not None and (bestVariance is None or variance < bestVariance):
bestVariance = variance
bestSeed = mapReduce.metadata["clusterVectors"]
if bestSeed is not None:
self.explicitSeeds(bestSeed)
performanceTable.end("smallTrials")
def verify(self, showSuccess=False, performanceTable=None):
"""Run the model verification tests defined by this element.
The output is a list of results (all results or only failures,
depending on C{showSuccess}), each of which is a dictionary of
field names to values. Fields are:
- "success": was the comparison successful?
- "expectedMissing", "observedMissing": is the
expected/observed value missing?
- "expectedValue", "observedValue": result as an internal
value.
- "expectedPythonValue", "observedPythonValue": result as a
Python value.
- "expectedDisplayValue", "observedDisplayValue": result as
a string displayValue.
Only "success", "expectedMissing", and "observedMissing" appear
if the "is missing?" comparison was unsuccessful.
@type showSuccess: bool
@param showSuccess: If True, emit output even if the tests are successful.
@type performanceTable: PerformanceTable
@param performanceTable: A PerformanceTable for measuring the efficiency of the calculation.
@rtype: JSON-like list of dicts
@return: As described above.
"""
verificationFields = {}
for verificationField in self.xpath("pmml:VerificationFields/pmml:VerificationField"):
verificationField.column = verificationField.get("column", verificationField["field"])
verificationField.precision = verificationField.get("precision", defaultFromXsd=True, convertType=True)
verificationField.zeroThreshold = verificationField.get("zeroThreshold", defaultFromXsd=True, convertType=True)
verificationField.data = []
verificationField.mask = []
verificationFields[verificationField.column] = verificationField
inputData = {}
inputMask = {}
for index, row in enumerate(self.childOfClass(TableInterface).iterate()):
for columnName, columnValue in row.items():
verificationField = verificationFields.get(columnName)
if verificationField is not None:
while len(verificationField.data) < index:
verificationField.data.append(defs.PADDING)
verificationField.mask.append(True)
verificationField.data.append(columnValue)
verificationField.mask.append(False)
else:
inputDataField = inputData.get(columnName)
if inputDataField is None:
inputDataField = []
inputData[columnName] = inputDataField
inputMask[columnName] = []
inputMaskField = inputMask[columnName]
while len(inputDataField) < index:
inputDataField.append(defs.PADDING)
inputMaskField.append(True)
inputDataField.append(columnValue)
inputMaskField.append(False)
for verificationField in verificationFields.values():
while len(verificationField.data) < index:
verificationField.data.append(defs.PADDING)
verificationField.mask.append(True)
for columnName in inputData:
inputDataField = inputData[columnName]
inputMaskField = inputMask[columnName]
while len(inputDataField) < index:
inputDataField.append(defs.PADDING)
inputMaskField.append(True)
for columnName, verificationField in verificationFields.items():
inputData[columnName] = verificationField.data
inputMask[columnName] = verificationField.mask
model = self.getparent()
if performanceTable is None:
performanceTable = FakePerformanceTable()
performanceTable.begin("make DataTable")
dataTable = DataTable(model, inputData, inputMask, inputState=None)
performanceTable.end("make DataTable")
functionTable = FunctionTable()
for miningField in model.xpath("pmml:MiningSchema/pmml:MiningField"):
miningField.replaceField(dataTable, functionTable, performanceTable)
for calculable in model.calculableTrans():
calculable.calculate(dataTable, functionTable, performanceTable)
score = model.calculateScore(dataTable, functionTable, performanceTable)
dataTable.score = score[None]
if model.name is not None:
for key, value in score.items():
if key is None:
dataTable.fields[model.name] = value
else:
dataTable.fields["%s.%s" % (model.name, key)] = value
for outputField in self.xpath("../pmml:Output/pmml:OutputField"):
displayName = outputField.get("displayName", outputField["name"])
outputField.format(dataTable, functionTable, performanceTable, score)
output = []
for verificationField in verificationFields.values():
observedOutput = dataTable.fields.get(verificationField["field"])
if observedOutput is None:
raise defs.PmmlValidationError("VerificationField references field \"%s\" but it was not produced by the model")
fieldType = observedOutput.fieldType
if fieldType.dataType == "object":
try:
newArray = [float(x) for x in observedOutput.data]
except ValueError:
pass
else:
fieldType = FakeFieldType("double", "continuous")
observedOutput._data = newArray
for index in xrange(len(dataTable)):
record = {"field": verificationField["field"], "index": index}
record["expectedMissing"] = verificationField.mask[index]
record["observedMissing"] = (observedOutput.mask is not None and observedOutput.mask[index] != defs.VALID)
if record["expectedMissing"] != record["observedMissing"]:
record["success"] = False
output.append(record)
elif not record["expectedMissing"]:
record["expectedValue"] = fieldType.stringToValue(verificationField.data[index])
record["observedValue"] = observedOutput.data[index]
record["expectedPythonValue"] = fieldType.valueToPython(record["expectedValue"])
record["observedPythonValue"] = fieldType.valueToPython(record["observedValue"])
record["expectedDisplayValue"] = fieldType.valueToString(record["expectedValue"])
record["observedDisplayValue"] = fieldType.valueToString(record["observedValue"])
if fieldType.optype == "continuous":
if (abs(record["expectedValue"]) <= verificationField.zeroThreshold) and (abs(record["observedValue"]) <= verificationField.zeroThreshold):
record["success"] = True
else:
record["success"] = ((record["expectedValue"] * (1.0 - verificationField.precision)) <= record["observedValue"] <= (record["expectedValue"] * (1.0 + verificationField.precision)))
if not record["success"] or showSuccess:
output.append(record)
else:
if record["expectedValue"] != record["observedValue"]:
record["success"] = False
output.append(record)
else:
record["success"] = True
if showSuccess:
output.append(record)
return output
def verify(self, showSuccess=False, performanceTable=None):
"""Run the model verification tests defined by this element.
The output is a list of results (all results or only failures,
depending on C{showSuccess}), each of which is a dictionary of
field names to values. Fields are:
- "success": was the comparison successful?
- "expectedMissing", "observedMissing": is the
expected/observed value missing?
- "expectedValue", "observedValue": result as an internal
value.
- "expectedPythonValue", "observedPythonValue": result as a
Python value.
- "expectedDisplayValue", "observedDisplayValue": result as
a string displayValue.
Only "success", "expectedMissing", and "observedMissing" appear
if the "is missing?" comparison was unsuccessful.
@type showSuccess: bool
@param showSuccess: If True, emit output even if the tests are successful.
@type performanceTable: PerformanceTable
@param performanceTable: A PerformanceTable for measuring the efficiency of the calculation.
@rtype: JSON-like list of dicts
@return: As described above.
"""
verificationFields = {}
for verificationField in self.xpath(
"pmml:VerificationFields/pmml:VerificationField"):
verificationField.column = verificationField.get(
"column", verificationField["field"])
verificationField.precision = verificationField.get(
"precision", defaultFromXsd=True, convertType=True)
verificationField.zeroThreshold = verificationField.get(
"zeroThreshold", defaultFromXsd=True, convertType=True)
verificationField.data = []
verificationField.mask = []
verificationFields[verificationField.column] = verificationField
inputData = {}
inputMask = {}
for index, row in enumerate(
self.childOfClass(TableInterface).iterate()):
for columnName, columnValue in row.items():
verificationField = verificationFields.get(columnName)
if verificationField is not None:
while len(verificationField.data) < index:
verificationField.data.append(defs.PADDING)
verificationField.mask.append(True)
verificationField.data.append(columnValue)
verificationField.mask.append(False)
else:
inputDataField = inputData.get(columnName)
if inputDataField is None:
inputDataField = []
inputData[columnName] = inputDataField
inputMask[columnName] = []
inputMaskField = inputMask[columnName]
while len(inputDataField) < index:
inputDataField.append(defs.PADDING)
inputMaskField.append(True)
inputDataField.append(columnValue)
inputMaskField.append(False)
for verificationField in verificationFields.values():
while len(verificationField.data) < index:
verificationField.data.append(defs.PADDING)
verificationField.mask.append(True)
for columnName in inputData:
inputDataField = inputData[columnName]
inputMaskField = inputMask[columnName]
while len(inputDataField) < index:
inputDataField.append(defs.PADDING)
inputMaskField.append(True)
for columnName, verificationField in verificationFields.items():
inputData[columnName] = verificationField.data
inputMask[columnName] = verificationField.mask
model = self.getparent()
if performanceTable is None:
performanceTable = FakePerformanceTable()
performanceTable.begin("make DataTable")
dataTable = DataTable(model, inputData, inputMask, inputState=None)
performanceTable.end("make DataTable")
functionTable = FunctionTable()
for miningField in model.xpath("pmml:MiningSchema/pmml:MiningField"):
miningField.replaceField(dataTable, functionTable,
performanceTable)
for calculable in model.calculableTrans():
calculable.calculate(dataTable, functionTable, performanceTable)
score = model.calculateScore(dataTable, functionTable,
performanceTable)
dataTable.score = score[None]
if model.name is not None:
for key, value in score.items():
if key is None:
dataTable.fields[model.name] = value
else:
dataTable.fields["%s.%s" % (model.name, key)] = value
for outputField in self.xpath("../pmml:Output/pmml:OutputField"):
displayName = outputField.get("displayName", outputField["name"])
outputField.format(dataTable, functionTable, performanceTable,
score)
output = []
for verificationField in verificationFields.values():
observedOutput = dataTable.fields.get(verificationField["field"])
if observedOutput is None:
raise defs.PmmlValidationError(
"VerificationField references field \"%s\" but it was not produced by the model"
)
fieldType = observedOutput.fieldType
if fieldType.dataType == "object":
try:
newArray = [float(x) for x in observedOutput.data]
except ValueError:
pass
else:
fieldType = FakeFieldType("double", "continuous")
observedOutput._data = newArray
for index in xrange(len(dataTable)):
record = {"field": verificationField["field"], "index": index}
record["expectedMissing"] = verificationField.mask[index]
record["observedMissing"] = (
observedOutput.mask is not None
and observedOutput.mask[index] != defs.VALID)
if record["expectedMissing"] != record["observedMissing"]:
record["success"] = False
output.append(record)
elif not record["expectedMissing"]:
record["expectedValue"] = fieldType.stringToValue(
verificationField.data[index])
record["observedValue"] = observedOutput.data[index]
record["expectedPythonValue"] = fieldType.valueToPython(
record["expectedValue"])
record["observedPythonValue"] = fieldType.valueToPython(
record["observedValue"])
record["expectedDisplayValue"] = fieldType.valueToString(
record["expectedValue"])
record["observedDisplayValue"] = fieldType.valueToString(
record["observedValue"])
if fieldType.optype == "continuous":
if (abs(record["expectedValue"]) <=
verificationField.zeroThreshold) and (
abs(record["observedValue"]) <=
verificationField.zeroThreshold):
record["success"] = True
else:
record["success"] = (
(record["expectedValue"] *
(1.0 - verificationField.precision)) <=
record["observedValue"] <=
(record["expectedValue"] *
(1.0 + verificationField.precision)))
if not record["success"] or showSuccess:
output.append(record)
else:
if record["expectedValue"] != record["observedValue"]:
record["success"] = False
output.append(record)
else:
record["success"] = True
if showSuccess:
output.append(record)
return output
