def apply_mapped_classifier_get_instances(weka_classifier, original_data,
data):
'''An advanced version of the Apply Classifier method.
Addresses incompatible training and test data, and returns a dataset with predictions.
:param weka_classifier: WekaClassifier object
:param original_data: original training instances, bunch
:param data: test instances, bunch
:return: Dataset (Bunch) object with predictions and a textual report from the InputMappedClassifier class
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
try:
classifier = common.deserialize_weka_object(
weka_classifier.sclassifier)
except:
raise Exception(
"Only WEKA classifiers/models supported. Please provide a valid WEKA learner."
)
original_training_instances = ut.convert_bunch_to_weka_instances(
original_data)
instances = ut.convert_bunch_to_weka_instances(data)
# serialize classifier with original instances to a file once again for the Mapped classifier
tfile = common.TemporaryFile(flags='wb+')
s = jp.JClass('weka.core.SerializationHelper')
s.writeAll(tfile.name, [classifier, original_training_instances])
# construct a MappedClassifier
mapped_classifier = jp.JClass(
'weka.classifiers.misc.InputMappedClassifier')()
mapped_classifier.setIgnoreCaseForNames(True)
mapped_classifier.setTrim(True)
# mapped_classifier.setSuppressMappingReport(True)
# mc.setModelHeader(original_training_instances)
mapped_classifier.setModelPath(tfile.name)
predictions = []
try:
for instance in instances:
label = int(mapped_classifier.classifyInstance(instance))
predictions.append(label)
data["targetPredicted"] = predictions
except:
raise Exception(
"Classifier not built. Please use the Build Classifier widget first."
)
report = mapped_classifier.toString()
if MAPPING_REPORT_START in report:
report = report[report.index(MAPPING_REPORT_START):]
return data, report
def apply_mapped_classifier_get_instances(weka_classifier, original_data, data):
'''An advanced version of the Apply Classifier method.
Addresses incompatible training and test data, and returns a dataset with predictions.
:param weka_classifier: WekaClassifier object
:param original_data: original training instances, bunch
:param data: test instances, bunch
:return: Dataset (Bunch) object with predictions and a textual report from the InputMappedClassifier class
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
try:
classifier = common.deserialize_weka_object(weka_classifier.sclassifier)
except:
raise Exception("Only WEKA classifiers/models supported. Please provide a valid WEKA learner.")
original_training_instances = ut.convert_bunch_to_weka_instances(original_data)
instances = ut.convert_bunch_to_weka_instances(data)
# serialize classifier with original instances to a file once again for the Mapped classifier
tfile = common.TemporaryFile(flags='wb+')
s = jp.JClass('weka.core.SerializationHelper')
s.writeAll(tfile.name, [classifier, original_training_instances])
# construct a MappedClassifier
mapped_classifier = jp.JClass('weka.classifiers.misc.InputMappedClassifier')()
mapped_classifier.setIgnoreCaseForNames(True)
mapped_classifier.setTrim(True)
# mapped_classifier.setSuppressMappingReport(True)
# mc.setModelHeader(original_training_instances)
mapped_classifier.setModelPath(tfile.name)
predictions = []
try:
for instance in instances:
label = int(mapped_classifier.classifyInstance(instance))
predictions.append(label)
data["targetPredicted"] = predictions
except:
raise Exception("Classifier not built. Please use the Build Classifier widget first.")
report = mapped_classifier.toString()
if MAPPING_REPORT_START in report:
report = report[report.index(MAPPING_REPORT_START):]
return data, report
def build_classifier(self, data):
"""Builds a classifier
:param data: bunch
"""
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
instances = ut.convert_bunch_to_weka_instances(data)
classifier = common.deserialize_weka_object(self.sclassifier)
if instances.classIndex() == -1:
instances.setClassIndex(instances.numAttributes() - 1)
# raise ValueError('Class not set!')
classifier.buildClassifier(instances)
self.sclassifier = common.serialize_weka_object(classifier)
def build_classifier(self, data):
"""Builds a classifier
:param data: bunch
"""
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
instances = ut.convert_bunch_to_weka_instances(data)
classifier = common.deserialize_weka_object(self.sclassifier)
if instances.classIndex() == -1:
instances.setClassIndex(instances.numAttributes() - 1)
# raise ValueError('Class not set!')
classifier.buildClassifier(instances)
self.sclassifier = common.serialize_weka_object(classifier)
def wekaLocalExportDatasetToARFF(request,input_dict,output_dict,widget):
import utilities as ut
from cf_core import helpers
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
bunch = input_dict['instances']
output_dict = {}
instances = ut.convert_bunch_to_weka_instances(bunch)
destination = helpers.get_media_root()+'/'+str(request.user.id)+'/'+str(widget.id)+'.arff'
f = open(destination, 'w')
s = instances.toString()
f.write(s)
f.close()
filename = str(request.user.id)+'/'+str(widget.id)+'.arff'
output_dict['filename'] = filename
return render(request, 'visualizations/string_to_file.html',{'widget':widget,'input_dict':input_dict,'output_dict':output_dict})
def apply_classifier(self, data):
"""Applies a classifier on a dataset, and gets predictions
:param data: bunch
:return: bunch with targetPredicted
"""
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
instances = ut.convert_bunch_to_weka_instances(data)
classifier = common.deserialize_weka_object(self.sclassifier)
class_index = instances.classIndex()
if class_index == -1:
raise ValueError('Class not set!')
predictions = []
for instance in instances:
label = int(classifier.classifyInstance(instance))
predictions.append(label)
data["targetPredicted"] = predictions
return data
def apply_classifier(self, data):
"""Applies a classifier on a dataset, and gets predictions
:param data: bunch
:return: bunch with targetPredicted
"""
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
instances = ut.convert_bunch_to_weka_instances(data)
classifier = common.deserialize_weka_object(self.sclassifier)
class_index = instances.classIndex()
if class_index == -1:
raise ValueError('Class not set!')
predictions = []
for instance in instances:
label = int(classifier.classifyInstance(instance))
predictions.append(label)
data["targetPredicted"] = predictions
return data
