def readRegressionSet(self, datasetFile, train, file_path):
try:
# Load in memory a dataset that contains a regression problem
self.instance_set.readSet(datasetFile, train, file_path)
self.ndata = self.instance_set.getNumInstances()
self.ninputs = Attributes.getInputNumAttributes(Attributes)
self.nvars = self.ninputs + Attributes.getOutputNumAttributes(
Attributes)
# print("In readRegressionSet , self.ndata is : " + str(self.ndata))
# print("In readRegressionSet , self.ninputs is : " + str(self.ninputs))
# print("In readRegressionSet , self.nvars is : " + str(self.nvars))
# outputIntegerheck that there is only one output variable
if Attributes.getOutputNumAttributes(Attributes) > 1:
# print("Out put attribute: ")
outPutAttHeader = Attributes.getOutputAttributesHeader(
Attributes)
# print(outPutAttHeader)
# print("This algorithm can not process MIMO datasets")
# print("All outputs but the first one will be removed")
exit(1)
noOutputs = False
if Attributes.getOutputNumAttributes(Attributes) < 1:
# print("This algorithm can not process datasets without outputs")
# print("Zero-valued output generated")
noOutputs = True
print("noOutputs = True, exit 1 !!!!!")
exit(1)
# Initialice and fill our own tables
self.x_array = [[0.0 for y in range(self.ninputs)]
for x in range(self.ndata)]
self.missing_array = [[False for y in range(self.ninputs)]
for x in range(self.ndata)]
self.output_integer_array = [0 for x in range(self.ndata)]
# Maximum and minimum of inputs
self.emax_array = [None for x in range(self.ninputs)]
self.emin_array = [None for x in range(self.ninputs)]
for i in range(0, self.ninputs):
self.emax_array[i] = Attributes.getAttributeByPos(
Attributes, i).getMaxAttribute()
self.emin_array[i] = Attributes.getAttributeByPos(
Attributes, i).getMinAttribute()
# All values are casted into double / integer
self.nclasses = 0
for i in range(0, self.ndata):
inst = self.instance_set.getInstance(i)
for j in range(0, self.ninputs):
self.x_array[i][
j] = self.instance_set.getInputNumericValue(
i, j) # inst.getInputRealValues(j);
self.missing_array[i][j] = inst.getInputMissingValues(j)
if self.missing_array[i][j]:
self.x_array[i][j] = self.emin_array[j] - 1
if noOutputs:
print("noOutputs self.output_real_array[i]" + str(i) +
"is 0 ")
self.output_real_array[i] = 0
self.output_integer_array[i] = 0
else:
print("noOutputs else part:")
self.output_real_array[
i] = self.instance_set.getOutputNumericValue(i, 0)
print("self.output_real_array[i]" + str(i) +
str(self.output_real_array[i]))
self.output_integer_array[i] = int(
self.output_real_array[i])
except OSError as error:
print("OS error: {0}".format(error))
except Exception as otherException:
# print("DBG: Exception in readSet:", sys.exc_info()[0])
print(" In readRegressionSet other Exception is :" +
str(otherException))
self.computeStatistics()
def read_classification_set(self, dataset_file, train, file_path):
try:
# Load in memory a dataset that contains a classification problem
print("Inside read_classification_set, datasetFile :" +
str(dataset_file))
# print("train is :" + str(train))
# print("object instanceSet is :" + str(self.instance_set))
if self.instance_set is None:
print("self.instance_set is Null")
else:
no_outputs = None
print("self.instance_set is not None, train = " + str(train))
self.instance_set.read_set(dataset_file, train, file_path)
print(
"begin getNumInstances ...... in read_classification_set ")
self.ndata = self.instance_set.getNumInstances()
print(
"In readCread_classification_setlassificationSet , self.ndata is : "
+ str(self.ndata))
self.ninputs = Attributes.getInputNumAttributes(Attributes)
print("In read_classification_set , self.ninputs is : " +
str(self.ninputs))
self.nvars = self.ninputs + Attributes.getOutputNumAttributes(
Attributes)
print("In read_classification_set , self.nvars is : " +
str(self.nvars))
# outputInteger check that there is only one output variable
if Attributes.getOutputNumAttributes(Attributes) > 1:
outAttrs = Attributes.getOutputAttributes(Attributes)
# print("Output Attributes number is bigger than 1")
i = 1
for outAtt in outAttrs:
# print("Att" + str(i) + str(outAtt.getName()))
i = i + 1
# print("" + Attributes.getOutputAttributesHeader(Attributes))
print(
"This algorithm can not process MIMO datasets !!! exit 1"
)
# print("All outputs but the first one will be removed")
exit(1)
no_outputs = False
if Attributes.getOutputNumAttributes(Attributes) < 1:
print(
"This algorithm can not process datasets without outputs !!!!!!"
)
# print("Zero-valued output generated")
no_outputs = True
exit(1)
# print("define all the array in MyDataSet class......")
# Initialice and fill our own tables
# print("The two dimension array X, dimension 1 is :" + str(self.ndata) + " ,Dimension 2 is :" + str(self.ninputs))
ndata_length = self.ndata
ninput_length = self.ninputs
print("nDataLength = " + str(ndata_length))
# print("nInputLength = " + str(nInputLength))
# [[0 for j in range(m)] for i in range(n)] first column, then row
self.x_array = [[0.0 for y in range(ninput_length)]
for x in range(ndata_length)]
self.missing_array = [[True for y in range(ninput_length)]
for x in range(ndata_length)]
self.nominal_array = [True for x in range(ninput_length)]
self.integer_array = [True for x in range(ninput_length)]
self.output_integer_array = [0 for x in range(ndata_length)]
self.output_real_array = [0.0 for x in range(ndata_length)]
self.output_array = ["" for x in range(ndata_length)]
# Maximum and minimum of inputs
self.emax = [0.0 for x in range(ninput_length)]
self.emin = [0.0 for x in range(ninput_length)]
for i in range(0, ninput_length):
attribute_instance: Attribute = Attributes.getInputAttribute(
Attributes, i)
if attribute_instance.getNumNominalValues() > 0:
self.emin[i] = 0
self.emax[i] = Attributes.getInputAttribute(
i).getNumNominalValues() - 1
else:
self.emax[i] = Attributes.getAttributeByPos(
Attributes, i).getMaxAttribute()
self.emin[i] = Attributes.getAttributeByPos(
Attributes, i).getMinAttribute()
if attribute_instance.getType() == Attribute.NOMINAL:
self.nominal_array[i] = True
self.integer_array[i] = False
elif attribute_instance.getType() == Attribute.INTEGER:
self.nominal_array[i] = False
self.integer_array[i] = True
else:
self.nominal_array[i] = False
self.integer_array[i] = False
# print("self.emax[n]:" + str(self.emax[n]))
# print("self.emin[n]:" + str(self.emin[n]))
# All values are casted into double/integer
self.nclasses = 0
for i in range(0, ndata_length):
inst = self.instance_set.getInstance(i)
for j in range(0, ninput_length):
input_Numeric_Value = self.instance_set.getInputNumericValue(
i, j)
# # print("self.x_array [i] = " + str(i) + ",[j] = " + str(j) + ",input_Numeric_Value:" + str(
# input_Numeric_Value))
self.x_array[i][
j] = input_Numeric_Value # inst.getInputRealValues(j);
# # print("after get self.x_array[i][j]")
self.missing_array[i][
j] = inst.getInputMissingValuesWithPos(j)
# # print("after self.missing_array[i][j]")
if self.missing_array[i][j]:
self.x_array[i][j] = self.emin[j] - 1
if no_outputs:
# print("no_outputs==True")
self.output_integer_array[i] = 0
# elf.output_real_array[i] = 0.0
self.output_array[i] = ""
else:
# print("no_outputs==False")
self.output_integer_array[
i] = self.instance_set.getOutputNumericValue(i, 0)
# print(" 202001-1 self.output_integer_array[ "+str(i)+"]"+ str( self.output_integer_array[i]))
# self.output_real_array[i] = self.instance_set.getOutputNumericValue(i, 0)
# print("self.output_integer_array[" + str(i) + "] = " + str(self.output_integer_array[i]))
self.output_array[
i] = self.instance_set.getOutputNominalValue(i, 0)
# print(" 202001-1 self.output_integer_array[ " + str(i) + "]" + str(self.output_integer_array[i]))
if self.output_integer_array[i] > self.nclasses:
self.nclasses = self.output_integer_array[i]
self.nclasses = self.nclasses + 1
print('Number of classes=' + str(self.nclasses))
except Exception as error:
print(
"read_classification_set: Exception in readSet, in read_classification_set:"
+ str(error))
# self.computeStatistics()
self.compute_instances_per_class()
def readClassificationSet(self, datasetFile, train):
try:
# Load in memory a dataset that contains a classification problem
print("Inside readClassificationSet, datasetFile :" +
str(datasetFile))
print("train is :" + str(train))
print("object instanceSet is :" + str(self.__instanceSet))
if (self.__instanceSet is None):
print("self.__instanceSet is Null")
else:
print("self.__instanceSet is not None, train = " + str(train))
self.__instanceSet.readSet(datasetFile, train)
print("begin getNumInstances ...... in readClassificationSet ")
self.__nData = self.__instanceSet.getNumInstances()
print("In readClassificationSet , self.__nData is : " +
str(self.__nData))
self.__nInputs = Attributes.getInputNumAttributes(Attributes)
print("In readClassificationSet , self.__nInputs is : " +
str(self.__nInputs))
self.__nVars = self.__nInputs + Attributes.getOutputNumAttributes(
Attributes)
print("In readClassificationSet , self.__nVars is : " +
str(self.__nVars))
# outputIntegerheck that there is only one output variable
if (Attributes.getOutputNumAttributes(Attributes) > 1):
outAttrs = Attributes.getOutputAttributes(Attributes)
print("Output Attributes number is bigger than 1")
for outAtt in outAttrs:
i = 1
print("Att" + str(i) + str(outAtt.getName()))
i = i + 1
print("" +
Attributes.getOutputAttributesHeader(Attributes))
print("This algorithm can not process MIMO datasets")
print("All outputs but the first one will be removed")
exit(1)
noOutputs = False
if (Attributes.getOutputNumAttributes(Attributes) < 1):
print(
"This algorithm can not process datasets without outputs"
)
print("Zero-valued output generated")
noOutputs = True
exit(1)
print("define all the array in MyDataSet class......")
#Initialice and fill our own tables
print("The two dimension array X, dimension 1 is :" +
str(self.__nData) + " ,Dimension 2 is :" +
str(self.__nInputs))
nDataLength = self.__nData
nInputLength = self.__nInputs
print("nDataLength = " + str(nDataLength))
print("nInputLength = " + str(nInputLength))
#[[0 for j in range(m)] for i in range(n)] first column, then row
self.__X = [[None for y in range(nInputLength)]
for x in range(nDataLength)]
self.__nominal = [None for x in range(nInputLength)]
self.__integer = [None for x in range(nInputLength)]
# boolean array
self.__missing = [[None for y in range(nInputLength)]
for x in range(nDataLength)]
# boolean array
self.__outputInteger = [None for x in range(nDataLength)]
# boolean array
self.__outputReal = [None for x in range(nDataLength)]
self.__output = ["" for x in range(nDataLength)]
# Maximum and minimum of inputs
self.emax = [None for x in range(nInputLength)]
self.emin = [None for x in range(nInputLength)]
for i in range(0, nInputLength):
print("inside loop to get each min and max")
if Attributes.getInputAttribute(
Attributes, i).getNumNominalValues() > 0:
print(
"getInputAttribute(self,i).getNumNominalValues() > 0"
)
self.emin[i] = 0
self.emax[i] = Attributes.getInputAttribute(
Attributes, i).getNumNominalValues() - 1
else:
print(
"getInputAttribute(self,i).getNumNominalValues() < 0"
)
self.emin[i] = Attributes.getInputAttribute(
Attributes, i).getMinAttribute()
self.emax[i] = Attributes.getInputAttribute(
Attributes, i).getMaxAttribute()
if Attributes.getAttributes(
Attributes)[i].getType() == Attribute.NOMINAL:
print("Attribute.NOMINAL")
self.__nominal[i] = True
self.__integer[i] = False
elif Attributes.getInputAttribute(
Attributes, i).getType() == Attribute.INTEGER:
print("Attribute.INTEGER")
self.__nominal[i] = False
self.__integer[i] = True
else:
print("Attribute others begin ")
self.__nominal[i] = False
self.__integer[i] = False
print("Attribute others is finished")
"""
compare with chi algorithm
for n in range( 0,nInputLength):
self.emax[n] = Attributes.getAttributeByPos(Attributes,n).getMaxAttribute()
self.emin[n] = Attributes.getAttributeByPos(Attributes,n).getMinAttribute()
print("self.emax[n]:"+ str(self.emax[n]))
print("self.emin[n]:" + str(self.emin[n]))
"""
# All values are casted into double/integer
self.__nClasses = 0
print("Before loop of set X[][] value")
for i in range(0, nDataLength):
inst = self.__instanceSet.getInstance(i)
for j in range(0, nInputLength):
input_Numeric_Value = self.__instanceSet.getInputNumericValue(
i, j)
#print("self.__X [i] = "+ str(i)+",[j] = "+ str(j)+",input_Numeric_Value:"+str(input_Numeric_Value))
self.__X[i][
j] = input_Numeric_Value #inst.getInputRealValues(j);
#print("after get self.__X[i][j]")
self.__missing[i][
j] = inst.getInputMissingValuesWithPos(j)
#print("after self.__missing[i][j]")
if self.__missing[i][j]:
self.__X[i][j] = self.emin[j] - 1
if noOutputs:
print("noOutputs==True")
self.__outputInteger[i] = 0
self.__output[i] = ""
else:
#print("noOutputs==False")
self.__outputInteger[
i] = self.__instanceSet.getOutputNumericValue(
i, 0)
#print("self.__outputInteger["+str(i)+"] = "+str(self.__outputInteger[i]))
self.__output[
i] = self.__instanceSet.getOutputNominalValue(
i, 0)
if self.__outputInteger[i] > self.__nClasses:
self.__nClasses = self.__outputInteger[i]
self.__nClasses = self.__nClasses + 1
print('Number of classes=' + str(self.__nClasses))
except Exception as error:
print(
"readClassificationSet: Exception in readSet, in readClassificationSet:"
+ str(error))
self.computeInstancesPerClass()
class MyDataSet:
# Number to represent type of variable real or double.
REAL = 0
# *Number to represent type of variable integer.*
INTEGER = 1
# *Number to represent type of variable nominal.*
NOMINAL = 2
x_array = [] # examples array
missing_array = [] # possible missing values
output_integer_array = [
] # output of the data - set as integer values private
output_real_array = [] # output of the data - set as double values
output_array = [] # output of the data - set as string values
emax_array = [] # max value of an attribute private
emin_array = [] # min value of an attribute
ndata = None # Number of examples
nvars = None # Numer of variables
ninputs = None # Number of inputs
nclasses = None # Number of outputs
instance_set = None # The whole instance set
stdev_array = []
average_array = [] # standard deviation and average of each attribute
instances_cl = []
# nominal attributes bool array
nominal_array = []
# integer attributes int array
integer_array = []
frequent_class_array = []
attributes = None
# *Init a new set of instances
def __init__(self):
self.instance_set = InstanceSet()
self.attributes = Attributes()
# '''
# * Outputs an array of examples with their corresponding attribute values.
# * @return double[][] an array of examples with their corresponding attribute values
# '''
def get_x(self):
return self.x_array
def set_x(self, x_parameter):
self.x_array = x_parameter
# '''
# * Output a specific example
# * @param pos int position (id) of the example in the data-set
# * @return double[] the attributes of the given example
# '''
def get_example(self, pos):
# # print(" In getExample, len(self.x_array) = " + str(len(self.x_array)) + ", pos = " + str(
# pos) + " ," + "self.x_array[pos] ==" + str(self.x_array[pos]))
return self.x_array[pos]
# * Returns the output of the data-set as integer values
# * @return int[] an array of integer values corresponding to the output values of the dataset
def get_output_as_integer(self):
size = len(self.output_integer_array)
output = [0 for x in range(size)]
for i in range(0, size):
output[i] = self.output_integer_array[i]
return output
# * Returns the output of the data-set as real values
# * @return double[] an array of real values corresponding to the output values of the dataset
def get_output_as_real(self):
output_length = len(self.output_real_array)
output = [0.0 for x in range(output_length)]
for i in range(0, len(self.output_real_array)):
output[i] = self.output_integer_array[i]
return output
# * Returns the output of the data-set as nominal values
# * @return String[] an array of nomianl values corresponding to the output values of the dataset
#
def get_output_as_string(self):
output_length = len(self.output_array)
output = ["" for x in range(output_length)]
for i in range(0, output_length):
output[i] = self.output_array[i]
return output
# * It returns the output value of the example "pos"
# * @param pos int the position (id) of the example
# * @return String a string containing the output value
def get_output_as_string_with_pos(self, pos):
# # print("pos is in getOutputAsStringWithPos "+str(pos))
# maybe the exception is here.
return self.output_array[pos]
# * It returns the output value of the example "pos"
# * @param pos int the position (id) of the example
# * @return int an integer containing the output value
def get_output_as_integer_with_pos(self, pos):
return self.output_integer_array[pos]
def set_output_integer_array(self, integer_array):
self.output_integer_array = integer_array
def set_output_array(self, output_array):
self.output_array = output_array
# * It returns the output value of the example "pos"
# * @param pos int the position (id) of the example
# * @return double a real containing the output value
def get_output_as_real_with_pos(self, pos):
return self.output_real_array[pos]
# *It returns an array with the maximum values of the attributes
# * @ return double[] an array with the maximum values of the attributes
#
def get_emax(self):
return self.emax_array
# *It returns an array with the minimum values of the attributes
# * @ return double[] an array with the minimum values of the attributes
def get_emin(self):
return self.emin_array
# *It returns the maximum value of the given attribute
# *
# * @ param variable the index of the attribute
# * @ return the maximum value of the given attribute
def get_max(self, variable):
return self.emax_array[variable]
# *It returns the minimum value of the given attribute
#
# * @ param variable the index of the attribute
# * @ return the minimum value of the given attribute
def get_min(self, variable):
return self.emin_array[variable]
# *It gets the size of the data - set
# * @ return int the number of examples in the data - set
def get_ndata(self):
return self.ndata
def set_ndata(self, ndata):
self.ndata = ndata
# *It gets the number of variables of the data - set(including the output)
# * @ return int the number of variables of the data - set(including the output)
# modified at 2020-08-14
def get_nvars(self):
return self.nvars
# * It gets the number of input attributes of the data-set
# * @return int the number of input attributes of the data-set
def get_ninputs(self):
return self.ninputs
def set_ninputs(self, ninputs_value):
self.ninputs = ninputs_value
# * It gets the number of output attributes of the data-set (for example number of classes in classification)
# * @return int the number of different output values of the data-set
def get_nclasses(self):
return self.nclasses
def set_nclasses(self, nclasses_value):
self.nclasses = nclasses_value
# added by rui for granularity rule generation
def calculate_nclasses_for_small_granularity_zone(self,
output_integer_array):
class_number = 0
class_array = []
has_class = False
for i in range(0, len(output_integer_array)):
# # print(" output_integer_array[i] " + str(output_integer_array[i]))
if len(class_array) == 0:
class_array.append(output_integer_array[i])
else:
has_class = False
for j in range(0, len(class_array)):
if class_array[j] == output_integer_array[i]:
# # print(" class_array[j] " + str(class_array[j]))
has_class = True
if not has_class:
class_array.append(output_integer_array[i])
class_number = len(class_array)
return class_number
# * This function checks if the attribute value is missing
# * @param i int Example id
# * @param j int Variable id
# * @return boolean True is the value is missing, else it returns false
def is_missing(self, i, j):
return self.missing_array[i][j]
# * It reads the whole input data-set and it stores each example and its associated output value in
# * local arrays to ease their use.
# * @param datasetFile String name of the file containing the dataset
# * @param train boolean It must have the value "true" if we are reading the training data-set
# * @throws IOException If there ocurs any problem with the reading of the data-set
def read_classification_set(self, dataset_file, train, file_path):
try:
# Load in memory a dataset that contains a classification problem
print("Inside read_classification_set, datasetFile :" +
str(dataset_file))
# print("train is :" + str(train))
# print("object instanceSet is :" + str(self.instance_set))
if self.instance_set is None:
print("self.instance_set is Null")
else:
no_outputs = None
print("self.instance_set is not None, train = " + str(train))
self.instance_set.read_set(dataset_file, train, file_path)
print(
"begin getNumInstances ...... in read_classification_set ")
self.ndata = self.instance_set.getNumInstances()
print(
"In readCread_classification_setlassificationSet , self.ndata is : "
+ str(self.ndata))
self.ninputs = self.attributes.getInputNumAttributes()
print("In read_classification_set , self.ninputs is : " +
str(self.ninputs))
self.nvars = self.ninputs + self.attributes.getOutputNumAttributes(
)
print("In read_classification_set , self.nvars is : " +
str(self.nvars))
# outputInteger check that there is only one output variable
if self.attributes.getOutputNumAttributes() > 1:
outAttrs = self.attributes.getOutputAttributes()
# print("Output Attributes number is bigger than 1")
i = 1
for outAtt in outAttrs:
# print("Att" + str(i) + str(outAtt.getName()))
i = i + 1
# print("" + Attributes.getOutputAttributesHeader(Attributes))
print(
"This algorithm can not process MIMO datasets !!! exit 1"
)
# print("All outputs but the first one will be removed")
exit(1)
no_outputs = False
if self.attributes.getOutputNumAttributes() < 1:
print(
"This algorithm can not process datasets without outputs !!!!!!"
)
# print("Zero-valued output generated")
no_outputs = True
exit(1)
# print("define all the array in MyDataSet class......")
# Initialice and fill our own tables
# print("The two dimension array X, dimension 1 is :" + str(self.ndata) + " ,Dimension 2 is :" + str(self.ninputs))
ndata_length = self.ndata
ninput_length = self.ninputs
print("nDataLength = " + str(ndata_length))
# print("nInputLength = " + str(nInputLength))
# [[0 for j in range(m)] for i in range(n)] first column, then row
self.x_array = [[0.0 for y in range(ninput_length)]
for x in range(ndata_length)]
self.missing_array = [[True for y in range(ninput_length)]
for x in range(ndata_length)]
self.nominal_array = [True for x in range(ninput_length)]
self.integer_array = [True for x in range(ninput_length)]
self.output_integer_array = [0 for x in range(ndata_length)]
self.output_real_array = [0.0 for x in range(ndata_length)]
self.output_array = ["" for x in range(ndata_length)]
# Maximum and minimum of inputs
self.emax = [0.0 for x in range(ninput_length)]
self.emin = [0.0 for x in range(ninput_length)]
for i in range(0, ninput_length):
attribute_instance = self.attributes.getInputAttribute(i)
if attribute_instance.getNumNominalValues() > 0:
self.emin[i] = 0
self.emax[i] = self.attributes.getInputAttribute(
i).getNumNominalValues() - 1
else:
self.emax[i] = self.attributes.getAttributeByPos(
i).getMaxAttribute()
self.emin[i] = self.attributes.getAttributeByPos(
i).getMinAttribute()
if attribute_instance.getType() == Attribute.NOMINAL:
self.nominal_array[i] = True
self.integer_array[i] = False
elif attribute_instance.getType() == Attribute.INTEGER:
self.nominal_array[i] = False
self.integer_array[i] = True
else:
self.nominal_array[i] = False
self.integer_array[i] = False
# print("self.emax[n]:" + str(self.emax[n]))
# print("self.emin[n]:" + str(self.emin[n]))
# All values are casted into double/integer
self.nclasses = 0
for i in range(0, ndata_length):
inst = self.instance_set.getInstance(i)
for j in range(0, ninput_length):
input_Numeric_Value = self.instance_set.getInputNumericValue(
i, j)
# # print("self.x_array [i] = " + str(i) + ",[j] = " + str(j) + ",input_Numeric_Value:" + str(
# input_Numeric_Value))
self.x_array[i][
j] = input_Numeric_Value # inst.getInputRealValues(j);
# # print("after get self.x_array[i][j]")
self.missing_array[i][
j] = inst.getInputMissingValuesWithPos(j)
# # print("after self.missing_array[i][j]")
if self.missing_array[i][j]:
self.x_array[i][j] = self.emin[j] - 1
if no_outputs:
# print("no_outputs==True")
self.output_integer_array[i] = 0
# elf.output_real_array[i] = 0.0
self.output_array[i] = ""
else:
# print("no_outputs==False")
self.output_integer_array[
i] = self.instance_set.getOutputNumericValue(i, 0)
# print(" 202001-1 self.output_integer_array[ "+str(i)+"]"+ str( self.output_integer_array[i]))
# self.output_real_array[i] = self.instance_set.getOutputNumericValue(i, 0)
# print("self.output_integer_array[" + str(i) + "] = " + str(self.output_integer_array[i]))
self.output_array[
i] = self.instance_set.getOutputNominalValue(i, 0)
# print(" 202001-1 self.output_integer_array[ " + str(i) + "]" + str(self.output_integer_array[i]))
if self.output_integer_array[i] > self.nclasses:
self.nclasses = self.output_integer_array[i]
self.nclasses = self.nclasses + 1
print('Number of classes=' + str(self.nclasses))
except Exception as error:
print(
"read_classification_set: Exception in readSet, in read_classification_set:"
+ str(error))
# self.computeStatistics()
self.compute_instances_per_class()
# * It reads the whole input data-set and it stores each example and its associated output value in
# * local arrays to ease their use.
# * @param datasetFile String name of the file containing the dataset
# * @param train boolean It must have the value "true" if we are reading the training data-set
# * @throws IOException If there ocurs any problem with the reading of the data-set
# added by rui for granularity rule generation
def read_classification_set_from_data_row_array(self, data_row_array):
self.compute_statistics_data_row_array(data_row_array)
self.compute_instances_perclass_data_row_array(data_row_array)
def readRegressionSet(self, datasetFile, train, file_path):
try:
# Load in memory a dataset that contains a regression problem
self.instance_set.readSet(datasetFile, train, file_path)
self.ndata = self.instance_set.getNumInstances()
self.ninputs = self.attributes.getInputNumAttributes()
self.nvars = self.ninputs + self.attributes.getOutputNumAttributes(
)
# print("In readRegressionSet , self.ndata is : " + str(self.ndata))
# print("In readRegressionSet , self.ninputs is : " + str(self.ninputs))
# print("In readRegressionSet , self.nvars is : " + str(self.nvars))
# outputIntegerheck that there is only one output variable
if self.attributes.getOutputNumAttributes() > 1:
# print("Out put attribute: ")
outPutAttHeader = self.attributes.getOutputAttributesHeader()
# print(outPutAttHeader)
# print("This algorithm can not process MIMO datasets")
# print("All outputs but the first one will be removed")
exit(1)
noOutputs = False
if self.attributes.getOutputNumAttributes() < 1:
# print("This algorithm can not process datasets without outputs")
# print("Zero-valued output generated")
noOutputs = True
print("noOutputs = True, exit 1 !!!!!")
exit(1)
# Initialice and fill our own tables
self.x_array = [[0.0 for y in range(self.ninputs)]
for x in range(self.ndata)]
self.missing_array = [[False for y in range(self.ninputs)]
for x in range(self.ndata)]
self.output_integer_array = [0 for x in range(self.ndata)]
# Maximum and minimum of inputs
self.emax_array = [None for x in range(self.ninputs)]
self.emin_array = [None for x in range(self.ninputs)]
for i in range(0, self.ninputs):
self.emax_array[i] = self.attributes.getAttributeByPos(
i).getMaxAttribute()
self.emin_array[i] = self.attributes.getAttributeByPos(
i).getMinAttribute()
# All values are casted into double / integer
self.nclasses = 0
for i in range(0, self.ndata):
inst = self.instance_set.getInstance(i)
for j in range(0, self.ninputs):
self.x_array[i][
j] = self.instance_set.getInputNumericValue(
i, j) # inst.getInputRealValues(j);
self.missing_array[i][j] = inst.getInputMissingValues(j)
if self.missing_array[i][j]:
self.x_array[i][j] = self.emin_array[j] - 1
if noOutputs:
print("noOutputs self.output_real_array[i]" + str(i) +
"is 0 ")
self.output_real_array[i] = 0
self.output_integer_array[i] = 0
else:
print("noOutputs else part:")
self.output_real_array[
i] = self.instance_set.getOutputNumericValue(i, 0)
print("self.output_real_array[i]" + str(i) +
str(self.output_real_array[i]))
self.output_integer_array[i] = int(
self.output_real_array[i])
except OSError as error:
print("OS error: {0}".format(error))
except Exception as otherException:
# print("DBG: Exception in readSet:", sys.exc_info()[0])
print(" In readRegressionSet other Exception is :" +
str(otherException))
self.computeStatistics()
# *It copies the header of the dataset
# * @ return String A string containing all the data - set information
def copy_header(self):
p = ""
# # print("copyHeader begin...., P is :" + p)
p = "@relation " + self.attributes.getRelationName() + "\n"
# # print(" after relation P is :" + p)
p += self.attributes.getInputAttributesHeader()
# # print(" after getInputAttributesHeader P is :" + p)
p += self.attributes.getOutputAttributesHeader()
# # print(" after getOutputAttributesHeader P is :" + p)
p += self.attributes.getInputHeader() + "\n"
# # print(" after getInputHeader P is :" + p)
p += self.attributes.getOutputHeader() + "\n"
# # print(" after getOutputHeader P is :" + p)
p += "@data\n"
# print("P is :" + p)
return p
# * It transform the input space into the [0,1] range
def normalize(self):
atts = self.getn_inputs()
maxs = [0.0 for x in range(atts)]
for j in range(0, atts):
maxs[j] = 1.0 / (self.emax_array[j] - self.emin_array[j])
for i in range(0, self.get_ndata()):
for j in range(0, atts):
if not self.isMissing(
i, j): # this process ignores missing values
self.x_array[i][j] = (self.x_array[i][j] -
self.__emin[j]) * maxs[j]
# * It checks if the data-set has any real value
# * @return boolean True if it has some real values, else false.
def has_real_attributes(self):
return Attributes.hasRealAttributes(self)
# * It checks if the data-set has any real value
# * @return boolean True if it has some real values, else false.
def has_numerical_attributes(self):
return Attributes.hasIntegerAttributes(
self) or Attributes.hasRealAttributes(self)
# * It checks if the data-set has any missing value
# * @return boolean True if it has some missing values, else false.
def has_missing_attributes(self):
return self.size_without_missing() < self.get_ndata()
# * It return the size of the data-set without having account the missing values
# * @return int the size of the data-set without having account the missing values
def size_without_missing(self):
tam = 0
# # print("self.ndata is :" + str(self.ndata) + ", self.ninputs :" + str(self.ninputs))
for i in range(0, self.ndata):
for j in range(1, self.ninputs):
# changed the isMissing condition inside if
if self.is_missing(i, j):
# print("It is missing value is i = " + str(i) + ",j==" + str(j))
break
j = j + 1
# # print("sizeWithoutMissing, i = " + str(i) + ",j==" + str(j))
if j == self.ninputs:
tam = tam + 1
# print("tam=" + str(tam))
return tam
# * It returns the number of examples
# *
# * @return the number of examples
def size(self):
return self.ndata
# * It computes the average and standard deviation of the input attributes
def compute_statistics(self):
try:
print("Begin computeStatistics......")
var_num = self.get_nvars()
print("varNum = " + str(var_num))
self.stdev_array = [
0.0 for x in range(var_num)
] # original was double ,changed into float in python
self.average_array = [0.0 for x in range(var_num)]
input_num = self.getn_inputs()
data_num = self.get_ndata()
print("inputNum = " + str(input_num) + ",dataNum = " +
str(data_num))
for i in range(0, input_num):
self.average_array[i] = 0
for j in range(0, data_num):
if not self.isMissing(j, i):
self.average_array[
i] = self.average_array[i] + self.x_array[j][i]
if data_num != 0:
self.average_array[i] = self.average_array[i] / data_num
average_length = len(self.average_array)
print(" average_length is " + str(average_length))
self.average_array[average_length - 1] = 0
if len(self.output_real_array) == 0:
print("len(self.output_real_array) is 0")
else:
# print("len(self.output_real_array) is " + str(len(self.output_real_array)))
for j in range(0, len(self.output_real_array)):
# print("self.output_real_array[j] is : "+str(self.output_real_array[j]) + " ,j is :"+str(j))
self.average_array[average_length - 1] = self.average_array[average_length - 1] + \
self.output_real_array[j]
if len(self.output_real_array) != 0:
self.average_array[average_length - 1] = self.average_array[
average_length - 1] / len(self.output_real_array)
print("before the loop for inputNum")
for i in range(0, input_num):
sum_value = 0.0
for j in range(0, data_num):
if not self.isMissing(j, i):
# print("self.isMissing(j, i)==False")
sum_value = sum_value + (
self.x_array[j][i] - self.average_array[i]) * (
self.x_array[j][i] - self.average_array[i])
if data_num != 0:
# print("dataNum != 0" + " , dataNum=" + str(data_num))
sum_value = sum_value / data_num
self.stdev_array[i] = math.sqrt(sum_value)
sum_value = 0.0
for j in range(0, len(self.output_real_array)):
sum_value += (self.output_real_array[j] -
self.average_array[average_length - 1]) * (
self.output_real_array[j] -
self.average_array[average_length - 1])
if len(self.output_real_array) != 0:
sum_value /= len(self.output_real_array)
self.stdev_array[len(self.stdev_array) -
1] = math.sqrt(sum_value)
print("sum is :" + str(sum_value) + " self.stdev_array :" +
str(self.stdev_array))
except Exception as error:
print("Exception in computeStatistics : " + str(error))
# * It return the standard deviation of an specific attribute
# * @param position int attribute id (position of the attribute)
# * @return double the standard deviation of the attribute
def std_dev(self, position):
return self.stdev_array[position]
# * It return the average of an specific attribute
# * @param position int attribute id (position of the attribute)
# * @return double the average of the attribute
def average(self, position):
return self.average_array[position]
# *It computes the number of examples per class
def compute_instances_per_class(self):
# print("compute_instances_per_class begin..., self.nclasses = " + str(self.nclasses))
self.instances_cl = [0 for x in range(self.nclasses)]
self.frequent_class_array = [
Decimal(0.0) for x in range(self.nclasses)
]
data_num = self.get_ndata()
# print("dataNum = " + str(dataNum))
for i in range(0, data_num):
integer_in_loop = self.output_integer_array[i]
# # print("outputInteger[" + str(i) + "]" + str(integerInLoop))
self.instances_cl[
integer_in_loop] = self.instances_cl[integer_in_loop] + 1
for i in range(0, self.nclasses):
if data_num is 0:
self.frequent_class_array[i] = 0
else:
self.frequent_class_array[i] = (1.0 * self.instances_cl[i] /
data_num)
# *It returns the number of examples for a given class
# * @ param clas int the class label id
# * @ return int the number of examples
# for the class
def number_instances(self, clas):
return self.instances_cl[clas]
# /**
# * It returns the number of labels for a nominal attribute
# * @param attribute int the attribute position in the data-set
# * @return int the number of labels for the attribute
# */
#
def number_values(self, attribute):
return Attributes.getInputAttribute(attribute).getNumNominalValues(
Attributes)
# * It returns the class label (string) given a class id (int)
# * @param intValue int the class id
# * @return String the corrresponding class label
#
# * It returns the class label (string) given a class id (int)
# * @param intValue int the class id
# * @return String the corrresponding class label
def get_output_value(self, int_value):
# # print("Before att get ")
att = Attributes.getOutputAttribute(Attributes, 0)
# # print("After att get ")
return att.getNominalValue(int_value)
# * It returns the type of the variable
# * @param variable int the variable id
# * @return int a code for the type of the variable (INTEGER, REAL or NOMINAL)
def get_type(self, variable):
if self.attributes.getAttributeByPos(
variable).getType() == Attributes.getAttributeByPos(0).INTEGER:
return self.INTEGER
if self.attributes.getAttributeByPos(
variable).getType() == Attributes.getAttributeByPos(0).REAL:
return self.REAL
if self.attributes.getAttributeByPos(
variable).getType() == Attributes.getAttributeByPos(0).NOMINAL:
return self.NOMINAL
return 0
# * It returns the discourse universe for the input and output variables
# * @return double[][] The minimum [0] and maximum [1] range of each variable
def set_nvars(self, nvar_value):
self.nvars = nvar_value
# modified at 2020-08-14
def get_ranges(self):
# print("self.get_nvars()" + str(self.get_nvars()))
rangos = [[0.0 for y in range(2)] for x in range(self.get_nvars())]
# print("rangos has two dimensions, first is self.get_nvars()==" + str(self.getn_inputs()) + ",second is 2")
ninputs = self.get_ninputs()
for i in range(0, ninputs):
# print("self.getn_inputs() is :" + str(nInputs) + " i = " + str(i))
attHere = self.attributes.getInputAttribute(i)
# print("attHere.getNumNominalValues()== " + str(attHere.getNumNominalValues()))
if attHere.getNumNominalValues() > 0:
rangos[i][0] = 0.0
rangos[i][1] = attHere.getNumNominalValues() - 1
# print(" attHere.getNumNominalValues() > 0,rangos[" + str(i) + "][0]==" + str(rangos[i][0]) + ",rangos[i][1]== " + str(rangos[i][1]))
else:
rangos[i][0] = attHere.getMinAttribute()
rangos[i][1] = attHere.getMaxAttribute()
# print(" attHere.getNumNominalValues() <= 0, rangos[" + str(i) + "][0]==" + str(rangos[i][0]) + ",rangos[i][1]== " + str(rangos[i][1]))
# save the output rango in the last range array
rangos[self.get_nvars() -
1][0] = self.attributes.getOutputAttribute(0).getMinAttribute()
rangos[self.get_nvars() -
1][1] = self.attributes.getOutputAttribute(0).getMaxAttribute()
return rangos
def get_granularity_zone_ranges(self, data_set_x_array):
# print("self.get_nvars()" + str(self.get_nvars()))
rangos = [[0.0 for y in range(2)] for x in range(self.get_nvars())]
# print("rangos has two dimensions, first is self.get_nvars()==" + str(self.get_nvars()) + ",second is 2")
nInputs = self.getn_inputs()
for i in range(0, nInputs):
# print("self.getn_inputs() is :" + str(nInputs) + " i = " + str(i))
attHere = Attributes.getInputAttribute(Attributes, i)
# print("attHere.getNumNominalValues()== " + str(attHere.getNumNominalValues()))
if attHere.getNumNominalValues() > 0:
rangos[i][0] = 0.0
rangos[i][1] = attHere.getNumNominalValues() - 1
# print(" attHere.getNumNominalValues() > 0,rangos[" + str(i) + "][0]==" + str(rangos[i][0]) + ",rangos[i][1]== " + str(rangos[i][1]))
else:
rangos[i][0] = attHere.get_min_granularity_attribute(
data_set_x_array, i)
rangos[i][1] = attHere.get_max_granularity_attribute(
data_set_x_array, i)
# print(" attHere.getNumNominalValues() <= 0, rangos[" + str(i) + "][0]==" + str(rangos[i][0]) + ",rangos[i][1]== " + str(rangos[i][1]))
last_min_value = self.attributes.getOutputAttribute(
0).getMinAttribute()
last_max_value = self.attributes.getOutputAttribute(
0).getMaxAttribute()
# print("The last_min_value is " + str(last_min_value)+" The last_max_value is " + str(last_max_value))
rangos[self.get_nvars() - 1][0] = last_min_value
rangos[self.get_nvars() - 1][1] = last_max_value
return rangos
# * It returns the attribute labels for the input features
# * @return String[] the attribute labels for the input features
def get_names(self):
names = ["" for x in range(self.ninputs)]
for i in range(0, self.ninputs):
names[i] = self.attributes.getInputAttribute(i).getName()
print(" attributes' names[" + str(i) + "]:" + names[i])
return names
# * It returns the class labels
# * @return String[] the class labels
def get_classes(self):
clases = ["" for x in range(self.nclasses)]
# print(" getClasses,self.nclasses: " + str(self.nclasses))
for i in range(0, self.nclasses):
# print(" getClasses method i is "+str(i))
clases[i] = self.attributes.getOutputAttribute(0).getNominalValue(
i)
return clases
def is_nominal(self, index_i):
return self.nominal_array[index_i]
def is_integer(self, index_i):
return self.integer_array[index_i]
def get_frequent_class(self, class_value):
print("class_value" + str(class_value))
return self.frequent_class_array[class_value]
"""
* It gets the number of input attributes of the data-set
* @return int the number of input attributes of the data-set
"""
def get_ninputs(self):
return self.ninputs
"""
* It returns the ratio of instances of the given class in the dataset
*
* @param clas the index of the class
* @return the ratio of instances of the given class in the dataset
"""
def frecuent_class(self, class_value):
return self.frequent_class_array[class_value]
def get_X(self):
return np.array(self.x_array)
def get_y(self, type_name='integer'):
if type_name == "real":
return np.array(self.output_real_array)
elif type_name == "integer":
return np.array(self.output_integer_array)
else:
return np.array(self.output_array)
def copyHeader(self):
p = ""
# # print("copyHeader begin...., P is :" + p)
p = "@relation " + self.attributes.getRelationName() + "\n"
# # print(" after relation P is :" + p)
p += self.attributes.getInputAttributesHeader()
# # print(" after getInputAttributesHeader P is :" + p)
p += self.attributes.getOutputAttributesHeader()
# # print(" after getOutputAttributesHeader P is :" + p)
p += self.attributes.getInputHeader() + "\n"
# # print(" after getInputHeader P is :" + p)
p += self.attributes.getOutputHeader() + "\n"
# # print(" after getOutputHeader P is :" + p)
p += "@data\n"
# print("P is :" + p)
return p
def readRegressionSet(self, datasetFile, train):
try:
#Load in memory a dataset that contains a regression problem
self.__instanceSet.readSet(datasetFile, train)
self.__nData = self.__instanceSet.getNumInstances()
self.__nInputs = Attributes.getInputNumAttributes(Attributes)
self.__nVars = self.__nInputs + Attributes.getOutputNumAttributes(
Attributes)
print("In readRegressionSet , self.__nData is : " +
str(self.__nData))
print("In readRegressionSet , self.__nInputs is : " +
str(self.__nInputs))
print("In readRegressionSet , self.__nVars is : " +
str(self.__nVars))
#outputIntegerheck that there is only one output variable
if (Attributes.getOutputNumAttributes(Attributes) > 1):
print("Out put attribute: ")
outPutAttHeader = Attributes.getOutputAttributesHeader(
Attributes)
print(outPutAttHeader)
print("This algorithm can not process MIMO datasets")
print("All outputs but the first one will be removed")
exit(1)
noOutputs = False
if (Attributes.getOutputNumAttributes(Attributes) < 1):
print(
"This algorithm can not process datasets without outputs")
print("Zero-valued output generated")
noOutputs = True
exit(1)
# Initialice and fill our own tables
self.__X = [[0.0 for y in range(self.__nInputs)]
for x in range(self.__nData)]
self.__missing = [[False for y in range(self.__nInputs)]
for x in range(self.__nData)]
self.__outputInteger = [0 for x in range(self.__nData)]
# Maximum and minimum of inputs
self.__emax = [None for x in range(self.__nInputs)]
self.__emin = [None for x in range(self.__nInputs)]
for i in range(0, self.__nInputs):
self.__emax[i] = Attributes.getAttributeByPos(
Attributes, i).getMaxAttribute()
self.__emin[i] = Attributes.getAttributeByPos(
Attributes, i).getMinAttribute()
# All values are casted into double / integer
self.__nClasses = 0
for i in range(0, self.__nData):
inst = self.__instanceSet.getInstance(i)
for j in range(0, self.__nInputs):
self.__X[i][j] = self.__instanceSet.getInputNumericValue(
i, j) #inst.getInputRealValues(j);
self.__missing[i][j] = inst.getInputMissingValues(j)
if (self.__missing[i][j]):
self.__X[i][j] = self.__emin[j] - 1
if (noOutputs):
self.__outputReal[i] = 0
self.__outputInteger[i] = 0
else:
self.__outputReal[
i] = self.__instanceSet.getOutputNumericValue(i, 0)
self.__outputInteger[i] = int(self.__outputReal[i])
except OSError as error:
print("OS error: {0}".format(error))
except Exception as otherException:
print("DBG: Exception in readSet:", sys.exc_info()[0])
print(" In readRegressionSet other Exception is :" +
str(otherException))
self.computeStatistics()