def discretize_ent(infilename,outfilename):
"""
Discretize features of data sets according to the MDL method proposed by
[#fayyad1993]_. Necessitate Orange Python module to perform the
discretization. Only discretize all continuous features of classification datasets.
:param infilename: name of the input file (expecting an arff file)
:type infilename: string
:param outfilename: name of the output file
:type outfilename: string
"""
data = OTable(infilename)
disc=Disc()
disc.method=EntropyMDL()
data_ent = disc(data)
# Manipulation of the discretized data
for attr in data_ent.domain.attributes :
#Reset renamed attributes name to original ones
if (attr.name[0:2] == "D_"):
attr.name = attr.name[2:]
attr.values = [val.replace(',',";") for val in attr.values]
# save the discretized data
data_ent.save(outfilename)
def discretize_ent(infilename, outfilename):
"""
Discretize features of data sets according to the MDL method proposed by
[#fayyad1993]_. Necessitate Orange Python module to perform the
discretization. Only discretize all continuous features of classification datasets.
:param infilename: name of the input file (expecting an arff file)
:type infilename: string
:param outfilename: name of the output file
:type outfilename: string
"""
data = OTable(infilename)
disc = Disc()
disc.method = EntropyMDL()
data_ent = disc(data)
# Manipulation of the discretized data
for attr in data_ent.domain.attributes:
#Reset renamed attributes name to original ones
if (attr.name[0:2] == "D_"):
attr.name = attr.name[2:]
attr.values = [val.replace(',', ";") for val in attr.values]
# save the discretized data
data_ent.save(outfilename)
def predict_wine_quality(table, n):
#Make the continous varibles discrete
disc = Discretize()
disc.method = discretize.EqualWidth(n=n)
table = disc(table)
#Define domain
feature_vars = list(table.domain[1:])
class_label_var = table.domain[0]
wine_domain = Domain(feature_vars, class_label_var)
table = Table.from_table(domain=wine_domain, source=table)
#Construct learner and print results
tree_learner = NNClassificationLearner(hidden_layer_sizes=(10, ),
max_iter=4000)
eval_results = CrossValidation(table, [tree_learner], k=10)
print("Accuracy of cross validation: {:.3f}".format(
scoring.CA(eval_results)[0]))
print("AUC: {:.3f}".format(scoring.AUC(eval_results)[0]))
def formatTable(tble):
'''
Bins the data, one hot encodes the data
:param tble:
:return: data: tble with binned data,
X: representation of data with one-hot-encoding,
mapping: representations of what our one-hot-encoding is
'''
# Discretization (binning)
# https://docs.orange.biolab.si/3/data-mining-library/reference/preprocess.html
print("Discretizing data")
disc = Discretize()
disc.method = discretize.EqualWidth(n=4)
data = disc(tble)
# print("Discretized table:\n{}\n\n".format(data))
print("One hot encoding data")
X, mapping = OneHot.encode(data, include_class=True)
sorted(mapping.items())
return data, X, mapping