def load(self, filename):
global classifier
classifier = Classifier.load(filename)
print "Classifier is Loaded"
def test_IBk(self):
# Train a classifier.
print('Training IBk classifier...')
c = Classifier(name='weka.classifiers.lazy.IBk', ckargs={'-K': 1})
training_fn = os.path.join(BP, 'fixtures/abalone-train.arff')
c.train(training_fn, verbose=1)
self.assertTrue(c._model_data)
# Make a valid query.
print('Using IBk classifier...')
query_fn = os.path.join(BP, 'fixtures/abalone-query.arff')
predictions = list(c.predict(query_fn, verbose=1, cleanup=0))
pred0 = predictions[0]
print('pred0:', pred0)
pred1 = PredictionResult(actual=None, predicted=7, probability=None)
print('pred1:', pred1)
self.assertEqual(pred0, pred1)
# Make a valid query.
with self.assertRaises(PredictionError):
query_fn = os.path.join(BP, 'fixtures/abalone-query-bad.arff')
predictions = list(c.predict(query_fn, verbose=1, cleanup=0))
# Make a valid query manually.
query = arff.ArffFile(relation='test',
schema=[
('Sex', ('M', 'F', 'I')),
('Length', 'numeric'),
('Diameter', 'numeric'),
('Height', 'numeric'),
('Whole weight', 'numeric'),
('Shucked weight', 'numeric'),
('Viscera weight', 'numeric'),
('Shell weight', 'numeric'),
('Class_Rings', 'integer'),
])
query.append(
['M', 0.35, 0.265, 0.09, 0.2255, 0.0995, 0.0485, 0.07, '?'])
data_str0 = """%
@relation test
@attribute 'Sex' {F,I,M}
@attribute 'Length' numeric
@attribute 'Diameter' numeric
@attribute 'Height' numeric
@attribute 'Whole weight' numeric
@attribute 'Shucked weight' numeric
@attribute 'Viscera weight' numeric
@attribute 'Shell weight' numeric
@attribute 'Class_Rings' integer
@data
M,0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,?
"""
data_str1 = query.write(fmt=DENSE)
# print(data_str0
# print(data_str1
self.assertEqual(data_str0, data_str1)
predictions = list(c.predict(query, verbose=1, cleanup=0))
self.assertEqual(
predictions[0],
PredictionResult(actual=None, predicted=7, probability=None))
# Test pickling.
fn = os.path.join(BP, 'fixtures/IBk.pkl')
c.save(fn)
c = Classifier.load(fn)
predictions = list(c.predict(query, verbose=1, cleanup=0))
self.assertEqual(
predictions[0],
PredictionResult(actual=None, predicted=7, probability=None))
#print('Pickle verified.')
# Make a valid dict query manually.
query = arff.ArffFile(relation='test',
schema=[
('Sex', ('M', 'F', 'I')),
('Length', 'numeric'),
('Diameter', 'numeric'),
('Height', 'numeric'),
('Whole weight', 'numeric'),
('Shucked weight', 'numeric'),
('Viscera weight', 'numeric'),
('Shell weight', 'numeric'),
('Class_Rings', 'integer'),
])
query.append({
'Sex': 'M',
'Length': 0.35,
'Diameter': 0.265,
'Height': 0.09,
'Whole weight': 0.2255,
'Shucked weight': 0.0995,
'Viscera weight': 0.0485,
'Shell weight': 0.07,
'Class_Rings': arff.MISSING,
})
predictions = list(c.predict(query, verbose=1, cleanup=0))
self.assertEqual(
predictions[0],
PredictionResult(actual=None, predicted=7, probability=None))
def load(self, filename):
global classifier
classifier = Classifier.load(filename)
print "Classifier is Loaded"
def test_IBk(self):
# Train a classifier.
print('Training IBk classifier...')
c = Classifier(name='weka.classifiers.lazy.IBk', ckargs={'-K':1})
training_fn = os.path.join(BP, 'fixtures/abalone-train.arff')
c.train(training_fn, verbose=1)
self.assertTrue(c._model_data)
# Make a valid query.
print('Using IBk classifier...')
query_fn = os.path.join(BP, 'fixtures/abalone-query.arff')
predictions = list(c.predict(query_fn, verbose=1, cleanup=0))
pred0 = predictions[0]
print('pred0:', pred0)
pred1 = PredictionResult(actual=None, predicted=7, probability=None)
print('pred1:', pred1)
self.assertEqual(pred0, pred1)
# Make a valid query.
with self.assertRaises(PredictionError):
query_fn = os.path.join(BP, 'fixtures/abalone-query-bad.arff')
predictions = list(c.predict(query_fn, verbose=1, cleanup=0))
# Make a valid query manually.
query = arff.ArffFile(relation='test', schema=[
('Sex', ('M', 'F', 'I')),
('Length', 'numeric'),
('Diameter', 'numeric'),
('Height', 'numeric'),
('Whole weight', 'numeric'),
('Shucked weight', 'numeric'),
('Viscera weight', 'numeric'),
('Shell weight', 'numeric'),
('Class_Rings', 'integer'),
])
query.append(['M', 0.35, 0.265, 0.09, 0.2255, 0.0995, 0.0485, 0.07, '?'])
data_str0 = """%
@relation test
@attribute 'Sex' {F,I,M}
@attribute 'Length' numeric
@attribute 'Diameter' numeric
@attribute 'Height' numeric
@attribute 'Whole weight' numeric
@attribute 'Shucked weight' numeric
@attribute 'Viscera weight' numeric
@attribute 'Shell weight' numeric
@attribute 'Class_Rings' integer
@data
M,0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,?
"""
data_str1 = query.write(fmt=DENSE)
# print(data_str0
# print(data_str1
self.assertEqual(data_str0, data_str1)
predictions = list(c.predict(query, verbose=1, cleanup=0))
self.assertEqual(predictions[0],
PredictionResult(actual=None, predicted=7, probability=None))
# Test pickling.
fn = os.path.join(BP, 'fixtures/IBk.pkl')
c.save(fn)
c = Classifier.load(fn)
predictions = list(c.predict(query, verbose=1, cleanup=0))
self.assertEqual(predictions[0],
PredictionResult(actual=None, predicted=7, probability=None))
#print('Pickle verified.')
# Make a valid dict query manually.
query = arff.ArffFile(relation='test', schema=[
('Sex', ('M', 'F', 'I')),
('Length', 'numeric'),
('Diameter', 'numeric'),
('Height', 'numeric'),
('Whole weight', 'numeric'),
('Shucked weight', 'numeric'),
('Viscera weight', 'numeric'),
('Shell weight', 'numeric'),
('Class_Rings', 'integer'),
])
query.append({
'Sex': 'M',
'Length': 0.35,
'Diameter': 0.265,
'Height': 0.09,
'Whole weight': 0.2255,
'Shucked weight': 0.0995,
'Viscera weight': 0.0485,
'Shell weight': 0.07,
'Class_Rings': arff.MISSING,
})
predictions = list(c.predict(query, verbose=1, cleanup=0))
self.assertEqual(predictions[0],
PredictionResult(actual=None, predicted=7, probability=None))