def test_ndarray_to_instances(self):
"""
Tests the ndarray_to_instances method.
"""
x = numpy.array([[1, 2, 3], [4, 5, 6]], numpy.float64)
inst = converters.ndarray_to_instances(x, "test")
self.assertIsNotNone(inst, msg="Should not be None!")
self.assertEqual("Att-1", inst.attribute(0).name, msg="Attribute name differs at #0")
self.assertEqual(3, inst.num_attributes, msg="# of columns differ")
self.assertEqual(2, inst.num_instances, msg="# of rows differ")
self.assertEqual(1, inst.get_instance(0).get_value(0), msg="value differs at 0,0")
x = numpy.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], numpy.float64)
inst = converters.ndarray_to_instances(x, "test", att_template="@-!")
self.assertIsNotNone(inst, msg="Should not be None!")
self.assertEqual("test-0", inst.attribute(0).name, msg="Attribute name differs at #0")
self.assertEqual(3, inst.num_attributes, msg="# of columns differ")
self.assertEqual(2, inst.num_instances, msg="# of rows differ")
self.assertEqual(1.1, inst.get_instance(0).get_value(0), msg="value differs at 0,0")
x = numpy.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], numpy.float64)
inst = converters.ndarray_to_instances(x, "test", att_list=["a", "b", "c"])
self.assertIsNotNone(inst, msg="Should not be None!")
self.assertEqual("a", inst.attribute(0).name, msg="Attribute name differs at #0")
self.assertEqual(3, inst.num_attributes, msg="# of columns differ")
self.assertEqual(2, inst.num_instances, msg="# of rows differ")
self.assertEqual(1.1, inst.get_instance(0).get_value(0), msg="value differs at 0,0")
def exposed_evaluate(self, X, d, task, i_model, i_evl):
data = np.reshape(eval(X), [d, -1], order='C')
if task == 'regression':
if i_model == 'LR':
data = converters.ndarray_to_instances(data, relation='tmp')
data.class_is_last()
model = Classifier(
classname='weka.classifiers.functions.LinearRegression')
evl = Evaluation(data)
evl.crossvalidate_model(model, data, 5, Random(0))
elif i_model == 'RF':
data = converters.ndarray_to_instances(data, relation='tmp')
data.class_is_last()
model = Classifier(
classname='weka.classifiers.trees.RandomForest')
evl = Evaluation(data)
evl.crossvalidate_model(model, data, 5, Random(0))
if i_evl == 'mae':
r_mae = evl.mean_absolute_error
return r_mae
elif i_evl == 'mse':
r_mae = evl.mean_square_error
return r_mse
elif i_evl == '1-rae':
r_one_minus_rae = 1 - evl.relative_absolute_error / 100
del evl, model, data
return r_one_minus_rae
elif task == 'classification':
le = LabelEncoder()
data[:, -1] = le.fit_transform(data[:, -1])
if i_model == 'RF':
dataRaw = converters.ndarray_to_instances(data, relation='tmp')
weka_filter = Filter(
classname=
"weka.filters.unsupervised.attribute.NumericToNominal",
options=["-R", "last"])
weka_filter.inputformat(dataRaw)
data = weka_filter.filter(dataRaw)
data.class_is_last()
model = Classifier(
classname='weka.classifiers.trees.RandomForest')
evl = Evaluation(data)
evl.crossvalidate_model(model, data, 5, Random(0))
elif i_model == 'LR':
model = LogisticRegression(multi_class='ovr')
elif i_model == 'SVM':
model = svm.SVC()
if i_evl == 'f_score':
fscore = evl.weighted_f_measure
del evl, model, data, dataRaw
if not (fscore >= 0.01 and fscore < 1.01):
fscore = 0.01
return fscore
def _sklearn2weka(self, features, labels=None):
encoder = CategoricalEncoder(encoding='ordinal')
labels_nominal = encoder.fit_transform(np.array(labels).reshape(-1, 1))
if not hasattr(self, 'dict') and labels is not None:
dict = {}
for label, nominal in zip(labels, labels_nominal):
if nominal.item(0) not in dict:
dict[nominal.item(0)] = label
self._dict = dict
labels_column = np.reshape(labels_nominal,[labels_nominal.shape[0], 1])
weka_dataset = ndarray_to_instances(np.ascontiguousarray(features, dtype=np.float_), 'weka_dataset')
weka_dataset.insert_attribute(Attribute.create_nominal('tag', [str(float(i)) for i in range(len(self._dict))]), features.shape[1])
if labels is not None:
for index, inst in enumerate(weka_dataset):
inst.set_value(features.shape[1], labels_column[index])
weka_dataset.set_instance(index,inst)
return weka_dataset
def test_ndarray_to_instances(self):
"""
Tests the ndarray_to_instances method.
"""
x = numpy.array([[1, 2, 3], [4, 5, 6]], numpy.float64)
inst = converters.ndarray_to_instances(x, "test")
self.assertIsNotNone(inst, msg="Should not be None!")
self.assertEqual("Att-1",
inst.attribute(0).name,
msg="Attribute name differs at #0")
self.assertEqual(3, inst.num_attributes, msg="# of columns differ")
self.assertEqual(2, inst.num_instances, msg="# of rows differ")
self.assertEqual(1,
inst.get_instance(0).get_value(0),
msg="value differs at 0,0")
x = numpy.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], numpy.float64)
inst = converters.ndarray_to_instances(x, "test", att_template="@-!")
self.assertIsNotNone(inst, msg="Should not be None!")
self.assertEqual("test-0",
inst.attribute(0).name,
msg="Attribute name differs at #0")
self.assertEqual(3, inst.num_attributes, msg="# of columns differ")
self.assertEqual(2, inst.num_instances, msg="# of rows differ")
self.assertEqual(1.1,
inst.get_instance(0).get_value(0),
msg="value differs at 0,0")
x = numpy.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], numpy.float64)
inst = converters.ndarray_to_instances(x,
"test",
att_list=["a", "b", "c"])
self.assertIsNotNone(inst, msg="Should not be None!")
self.assertEqual("a",
inst.attribute(0).name,
msg="Attribute name differs at #0")
self.assertEqual(3, inst.num_attributes, msg="# of columns differ")
self.assertEqual(2, inst.num_instances, msg="# of rows differ")
self.assertEqual(1.1,
inst.get_instance(0).get_value(0),
msg="value differs at 0,0")
def classify(train, test, name="RF", tuning=False):
jvm.start()
if isinstance(train, list) and isinstance(test, list):
train = weka_instance(train)
trn_data = converters.load_any_file(train)
test = weka_instance(test)
tst_data = converters.load_any_file(test)
elif os.path.isfile(train) and os.path.isfile(test):
trn_data = converters.load_any_file(train)
tst_data = converters.load_any_file(test)
else:
trn = csv_as_ndarray(train)
tst = csv_as_ndarray(test)
trn_data = converters.ndarray_to_instances(trn, relation="Train")
tst_data = converters.ndarray_to_instances(tst, relation="Test")
trn_data.class_is_last()
tst_data.class_is_last()
# t = time()
if tuning:
opt = tune(train)
else:
opt = default_opt
# print("Time to tune: {} seconds".format(time() - t))
cls = Classifier(classname=classifiers[name.lower()], options=opt)
cls.build_classifier(trn_data)
distr = [cls.distribution_for_instance(inst)[1] for inst in tst_data]
preds = [cls.classify_instance(inst) for inst in tst_data]
jvm.stop()
return preds, distr
def SMOreg(obj):
jvm.start(packages=True)
# load model
cls = Classifier(jobject=serialization.read("new_models/SMOreg.model"))
input = np.array([[obj.screen_num_7, obj.show_num_7, obj.money_num_7, obj.audience_num_7, obj.director_effect,
obj.distributor_effect, obj.month, obj.nationality, obj.before_grade, obj.after_grade, obj.age,
obj.actor_effect, 0]])
instance = ndarray_to_instances(input, relation="input")
for index, inst in enumerate(instance):
audience_num = cls.classify_instance(inst)
jvm.stop()
return audience_num
def _sklearn2weka(self, features, labels=None):
# All weka datasets have to be a zero-based coding for the column of labels
# We can use non-aligned labels for training and testing because the labels
# in testing phase are only used to obtain performance, but not for preds.
# We compute performance off-line.
labels_encoder = OrdinalEncoder()
labels_nominal = labels_encoder.fit_transform(np.array(labels).reshape(-1, 1))
labels_column = np.reshape(labels_nominal, [labels_nominal.shape[0], 1])
# TODO: find another way to do the same
# The follow is used to assign the value of _dict only in training phase
if not hasattr(self, '_dict') and labels is not None:
dict = {}
for label, nominal in zip(labels, labels_nominal):
if nominal.item(0) not in dict:
dict[nominal.item(0)] = label
self._dict = dict
weka_dataset = ndarray_to_instances(np.ascontiguousarray(features, dtype=np.float_), 'weka_dataset')
weka_dataset.insert_attribute(Attribute.create_nominal('tag', [str(float(i)) for i in range(len(self._dict))]),
features.shape[1])
if labels is not None:
try:
for index, inst in enumerate(weka_dataset):
inst.set_value(features.shape[1], labels_column[index])
weka_dataset.set_instance(index, inst)
except TypeError as e:
print('Error: it seems InstanceIterator does not implement a valid iterator.')
print('Please, check the class definition in lib/python3.7/site-packages/weka/core/dataset.py.')
print('This error could be due to the next() method: it should be declared as __next__().')
exit()
return weka_dataset