def train(self, trainSet: InstanceList, parameters: Parameter):
"""
Training algorithm for the quadratic discriminant analysis classifier (Introduction to Machine Learning,
Alpaydin, 2015).
PARAMETERS
----------
trainSet : InstanceList
Training data given to the algorithm.
"""
w0 = {}
w = {}
W = {}
classLists = Partition(trainSet)
priorDistribution = trainSet.classDistribution()
for i in range(classLists.size()):
Ci = classLists.get(i).getClassLabel()
averageVector = Vector(classLists.get(i).continuousAverage())
classCovariance = classLists.get(i).covariance(averageVector)
determinant = classCovariance.determinant()
classCovariance.inverse()
Wi = deepcopy(classCovariance)
Wi.multiplyWithConstant(-0.5)
W[Ci] = Wi
wi = classCovariance.multiplyWithVectorFromLeft(averageVector)
w[Ci] = wi
w0i = -0.5 * (wi.dotProduct(averageVector) + math.log(determinant)) + math.log(priorDistribution.
getProbability(Ci))
w0[Ci] = w0i
self.model = QdaModel(priorDistribution, W, w, w0)
def train(self, trainSet: InstanceList, parameters: Parameter):
"""
Training algorithm for the linear discriminant analysis classifier (Introduction to Machine Learning, Alpaydin,
2015).
PARAMETERS
----------
trainSet : InstanceList
Training data given to the algorithm.
parameters : Parameter
Parameter of the Lda algorithm.
"""
w0 = {}
w = {}
priorDistribution = trainSet.classDistribution()
classLists = Partition(trainSet)
covariance = Matrix(trainSet.get(0).continuousAttributeSize(), trainSet.get(0).continuousAttributeSize())
for i in range(classLists.size()):
averageVector = Vector(classLists.get(i).continuousAverage())
classCovariance = classLists.get(i).covariance(averageVector)
classCovariance.multiplyWithConstant(classLists.get(i).size() - 1)
covariance.add(classCovariance)
covariance.divideByConstant(trainSet.size() - classLists.size())
covariance.inverse()
for i in range(classLists.size()):
Ci = classLists.get(i).getClassLabel()
averageVector = Vector(classLists.get(i).continuousAverage())
wi = covariance.multiplyWithVectorFromRight(averageVector)
w[Ci] = wi
w0i = -0.5 * wi.dotProduct(averageVector) + math.log(priorDistribution.getProbability(Ci))
w0[Ci] = w0i
self.model = LdaModel(priorDistribution, w, w0)
def train(self, trainSet: InstanceList, parameters: KMeansParameter):
priorDistribution = trainSet.classDistribution()
classMeans = InstanceList()
classLists = Partition(trainSet)
for i in range(classLists.size()):
classMeans.add(classLists.get(i).average())
self.model = KMeansModel(priorDistribution, classMeans,
parameters.getDistanceMetric())
def __init__(self, trainSet: InstanceList):
"""
Constructor which sets the distribution using the given InstanceList.
PARAMETERS
----------
trainSet : InstanceList
InstanceList which is used to get the class distribution.
"""
self.distribution = trainSet.classDistribution()
def train(self, trainSet: InstanceList, parameters: Parameter):
"""
Training algorithm for random classifier.
PARAMETERS
----------
trainSet : InstanceList
Training data given to the algorithm.
"""
self.model = RandomModel(list(trainSet.classDistribution().keys()),
parameters.getSeed())
class DataSet(object):
__instances: InstanceList
__definition: DataDefinition
def __init__(self,
definition: DataDefinition = None,
separator: str = None,
fileName: str = None):
"""
Constructor for generating a new DataSet with given DataDefinition.
PARAMETERS
----------
definition : DataDefinition
Data definition of the data set.
separator : str
Separator character which separates the attribute values in the data file.
fileName : str
Name of the data set file.
"""
self.__definition = definition
if separator is None:
self.__instances = InstanceList()
else:
self.__instances = InstanceList(definition, separator, fileName)
def initWithFile(self, fileName: str):
"""
Constructor for generating a new DataSet from given File.
PARAMETERS
----------
fileName : str
File to generate DataSet from.
"""
self.__instances = InstanceList()
self.__definition = DataDefinition()
inputFile = open(fileName, 'r', encoding='utf8')
lines = inputFile.readlines()
i = 0
for line in lines:
attributes = line.split(",")
if i == 0:
for j in range(len(attributes) - 1):
try:
float(attributes[j])
self.__definition.addAttribute(
AttributeType.CONTINUOUS)
except:
self.__definition.addAttribute(AttributeType.DISCRETE)
else:
if len(attributes) != self.__definition.attributeCount() + 1:
continue
if ";" not in attributes[len(attributes) - 1]:
instance = Instance(attributes[len(attributes) - 1])
else:
labels = attributes[len(attributes) - 1].split(";")
instance = CompositeInstance(labels[0], None, labels)
for j in range(len(attributes) - 1):
if self.__definition.getAttributeType(
j) is AttributeType.CONTINUOUS:
instance.addAttribute(
ContinuousAttribute(float(attributes[j])))
elif self.__definition.getAttributeType(
j) is AttributeType.DISCRETE:
instance.addAttribute(DiscreteAttribute(attributes[j]))
if instance.attributeSize() == self.__definition.attributeCount():
self.__instances.add(instance)
i = i + 1
def __checkDefinition(self, instance: Instance) -> bool:
"""
Checks the correctness of the attribute type, for instance, if the attribute of given instance is a Binary
attribute, and the attribute type of the corresponding item of the data definition is also a Binary attribute,
it then returns true, and false otherwise.
PARAMETERS
----------
instance : Instance
Instance to checks the attribute type.
RETURNS
-------
bool
true if attribute types of given Instance and data definition matches.
"""
for i in range(instance.attributeSize()):
if isinstance(instance.getAttribute(i), BinaryAttribute):
if self.__definition.getAttributeType(
i) is not AttributeType.BINARY:
return False
elif isinstance(instance.getAttribute(i),
DiscreteIndexedAttribute):
if self.__definition.getAttributeType(
i) is not AttributeType.DISCRETE_INDEXED:
return False
elif isinstance(instance.getAttribute(i), DiscreteAttribute):
if self.__definition.getAttributeType(
i) is not AttributeType.DISCRETE:
return False
elif isinstance(instance.getAttribute(i), ContinuousAttribute):
if self.__definition.getAttributeType(
i) is not AttributeType.CONTINUOUS:
return False
return True
def __setDefinition(self, instance: Instance):
"""
Adds the attribute types according to given Instance. For instance, if the attribute type of given Instance
is a Discrete type, it than adds a discrete attribute type to the list of attribute types.
PARAMETERS
----------
instance : Instance
Instance input.
"""
attributeTypes = []
for i in range(instance.attributeSize()):
if isinstance(instance.getAttribute(i), BinaryAttribute):
attributeTypes.append(AttributeType.BINARY)
elif isinstance(instance.getAttribute(i),
DiscreteIndexedAttribute):
attributeTypes.append(AttributeType.DISCRETE_INDEXED)
elif isinstance(instance.getAttribute(i), DiscreteAttribute):
attributeTypes.append(AttributeType.DISCRETE)
elif isinstance(instance.getAttribute(i), ContinuousAttribute):
attributeTypes.append(AttributeType.CONTINUOUS)
self.__definition = DataDefinition(attributeTypes)
def sampleSize(self) -> int:
"""
Returns the size of the InstanceList.
RETURNS
-------
int
Size of the InstanceList.
"""
return self.__instances.size()
def classCount(self) -> int:
"""
Returns the size of the class label distribution of InstanceList.
RETURNS
-------
int
Size of the class label distribution of InstanceList.
"""
return len(self.__instances.classDistribution())
def attributeCount(self) -> int:
"""
Returns the number of attribute types at DataDefinition list.
RETURNS
-------
int
The number of attribute types at DataDefinition list.
"""
return self.__definition.attributeCount()
def discreteAttributeCount(self) -> int:
"""
Returns the number of discrete attribute types at DataDefinition list.
RETURNS
-------
int
The number of discrete attribute types at DataDefinition list.
"""
return self.__definition.discreteAttributeCount()
def continuousAttributeCount(self) -> int:
"""
Returns the number of continuous attribute types at DataDefinition list.
RETURNS
-------
int
The number of continuous attribute types at DataDefinition list.
"""
return self.__definition.continuousAttributeCount()
def getClasses(self) -> str:
"""
Returns the accumulated String of class labels of the InstanceList.
RETURNS
-------
str
The accumulated String of class labels of the InstanceList.
"""
classLabels = self.__instances.getDistinctClassLabels()
result = classLabels[0]
for i in range(1, len(classLabels)):
result = result + ";" + classLabels[i]
return result
def info(self, dataSetName: str) -> str:
"""
Returns the general information about the given data set such as the number of instances, distinct class labels,
attributes, discrete and continuous attributes.
PARAMETERS
----------
dataSetName : str
Data set name.
RETURNS
-------
str
General information about the given data set.
"""
result = "DATASET: " + dataSetName + "\n"
result = result + "Number of instances: " + self.sampleSize().__str__(
) + "\n"
result = result + "Number of distinct class labels: " + self.classCount(
).__str__() + "\n"
result = result + "Number of attributes: " + self.attributeCount(
).__str__() + "\n"
result = result + "Number of discrete attributes: " + self.discreteAttributeCount(
).__str__() + "\n"
result = result + "Number of continuous attributes: " + self.continuousAttributeCount(
).__str__() + "\n"
result = result + "Class labels: " + self.getClasses()
return result
def addInstance(self, current: Instance):
"""
Adds a new instance to the InstanceList.
PARAMETERS
----------
current : Instance
Instance to add.
"""
if self.__definition is None:
self.__setDefinition(current)
self.__instances.add(current)
elif self.__checkDefinition(current):
self.__instances.add(current)
def addInstanceList(self, instanceList: list):
"""
Adds all the instances of given instance list to the InstanceList.
PARAMETERS
----------
instanceList : list
InstanceList to add instances from.
"""
for instance in instanceList:
self.addInstance(instance)
def getInstances(self) -> list:
"""
Returns the instances of InstanceList.
RETURNS
-------
list
The instances of InstanceList.
"""
return self.__instances.getInstances()
def getClassInstances(self) -> list:
"""
Returns instances of the items at the list of instance lists from the partitions.
RETURNS
-------
list
Instances of the items at the list of instance lists from the partitions.
"""
return Partition(self.__instances).getLists()
def getInstanceList(self) -> InstanceList:
"""
Accessor for the InstanceList.
RETURNS
-------
InstanceList
The InstanceList.
"""
return self.__instances
def getDataDefinition(self) -> DataDefinition:
"""
Accessor for the data definition.
RETURNS
-------
DataDefinition
The data definition.
"""
return self.__definition
def getSubSetOfFeatures(self, featureSubSet: FeatureSubSet) -> DataSet:
"""
Return a subset generated via the given FeatureSubSet.
PARAMETERS
----------
featureSubSet : FeatureSubSet
FeatureSubSet input.
RETURNS
-------
FeatureSubSet
Subset generated via the given FeatureSubSet.
"""
result = DataSet(self.__definition.getSubSetOfFeatures(featureSubSet))
for i in range(self.__instances.size()):
result.addInstance(
self.__instances.get(i).getSubSetOfFeatures(featureSubSet))
return result
def writeToFile(self, outFileName: str):
"""
Print out the instances of InstanceList as a String.
PARAMETERS
----------
outFileName : str
File name to write the output.
"""
outfile = open(outFileName, "w")
for i in range(self.__instances.size()):
outfile.write(self.__instances.get(i).__str__() + "\n")
outfile.close()
def __init__(self, instanceList: InstanceList = None, ratio=None, seed=None, stratified: bool = None):
"""
Divides the instances in the instance list into partitions so that all instances of a class are grouped in a
single partition.
PARAMETERS
----------
ratio
Ratio of the stratified partition. Ratio is between 0 and 1. If the ratio is 0.2, then 20 percent of the
instances are put in the first group, 80 percent of the instances are put in the second group.
seed
seed is used as a random number.
"""
self.__multilist = []
if instanceList is not None:
if ratio is None:
classLabels = instanceList.getDistinctClassLabels()
for classLabel in classLabels:
self.add(InstanceListOfSameClass(classLabel))
for instance in instanceList.getInstances():
self.get(classLabels.index(instance.getClassLabel())).add(instance)
else:
if isinstance(ratio, float):
self.add(InstanceList())
self.add(InstanceList())
if stratified:
distribution = instanceList.classDistribution()
counts = [0] * len(distribution)
randomArray = [i for i in range(instanceList.size())]
random.seed(seed)
random.shuffle(randomArray)
for i in range(instanceList.size()):
instance = instanceList.get(randomArray[i])
classIndex = distribution.getIndex(instance.getClassLabel())
if counts[classIndex] < instanceList.size() * ratio * \
distribution.getProbability(instance.getClassLabel()):
self.get(0).add(instance)
else:
self.get(1).add(instance)
counts[classIndex] = counts[classIndex] + 1
else:
instanceList.shuffle(seed)
for i in range(self.size()):
instance = instanceList.get(i)
if i < instanceList.size() * ratio:
self.get(0).add(instance)
else:
self.get(1).add(instance)
elif isinstance(ratio, int):
attributeIndex = ratio
if seed is None:
valueList = instanceList.getAttributeValueList(attributeIndex)
for _ in valueList:
self.add(InstanceList())
for instance in instanceList.getInstances():
self.get(valueList.index(instance.getAttribute(attributeIndex).getValue())).add(instance)
elif isinstance(seed, int):
attributeValue = seed
self.add(InstanceList())
self.add(InstanceList())
for instance in instanceList.getInstances():
if instance.getAttribute(attributeIndex).getIndex() == attributeValue:
self.get(0).add(instance)
else:
self.get(1).add(instance)
elif isinstance(seed, float):
splitValue = seed
self.add(InstanceList())
self.add(InstanceList())
for instance in instanceList.getInstances():
if instance.getAttribute(attributeIndex).getValue() < splitValue:
self.get(0).add(instance)
else:
self.get(1).add(instance)