def test_load(self):
examples = train_example.load()
print("X length: ", len(examples[0].x))
print("Y length: ", len(examples[0].y))
self.assertEqual(len(examples), 372)
def calibration():
"""Calibrates the model.
It uses default values for epochs and n-folds.
"""
examples = train_example.load()
# Split examples in train and validation using 5-fold
print("examples size:", len(examples))
kf = KFold(len(examples), n_folds=5)
wLength = len(examples[0].x[0].coref.feat)
for trainIndex, testIndex in kf:
train = [examples[e] for e in trainIndex]
print("\n\ntrain size:", len(train))
validation = [examples[e] for e in testIndex]
print("validation size:", len(validation))
w = np.array([0] * wLength)
w = perceptron.structured(w,
train,
test=validation,
epochs=65,
argmax=quotation.argmax,
phi=quotation.phi)
def test_load(self):
examples = train_example.load()
print("X length: ", len(examples[0].x))
print("Y length: ", len(examples[0].y))
self.assertEqual(len(examples), 372)
def main():
"""Main function, that train and validade the model for the Quotation Extractor Task.
"""
w = train.train()
print(w)
dataset = train_example.load(fileName=INPUT_TEST_FILE)
precision, recall = metrics.validate(w, dataset=dataset, argmax=quotation.argmax)
print("On the test set:\n")
print("precision:", precision)
print("recall:", recall)
def main():
"""Main function, that train and validade the model for the Quotation Extractor Task.
"""
w = train.train()
print(w)
dataset = train_example.load(fileName=INPUT_TEST_FILE)
precision, recall = metrics.validate(w,
dataset=dataset,
argmax=quotation.argmax)
print("On the test set:\n")
print("precision:", precision)
print("recall:", recall)
def train():
"""Train the model, based of the default GloboQuotes dataset.
Returns:
The w model trained.
"""
examples = train_example.load()
# Split examples in train and validation using 5-fold
print("examples size:", len(examples))
wLength = len(examples[0].x[0].coref.feat)
w = np.array([0]*wLength)
w = perceptron.structured(w, examples, test=None, epochs=38, argmax=quotation.argmax, phi=quotation.phi)
return w
def calibration():
"""Calibrates the model.
It uses default values for epochs and n-folds.
"""
examples = train_example.load()
# Split examples in train and validation using 5-fold
print("examples size:", len(examples))
kf = KFold(len(examples), n_folds=5)
wLength = len(examples[0].x[0].coref.feat)
for trainIndex, testIndex in kf:
train = [ examples[e] for e in trainIndex ]
print("\n\ntrain size:", len(train))
validation = [ examples[e] for e in testIndex ]
print("validation size:", len(validation))
w = np.array([0]*wLength)
w = perceptron.structured(w, train, test=validation, epochs=65, argmax=quotation.argmax, phi=quotation.phi)
def train():
"""Train the model, based of the default GloboQuotes dataset.
Returns:
The w model trained.
"""
examples = train_example.load()
# Split examples in train and validation using 5-fold
print("examples size:", len(examples))
wLength = len(examples[0].x[0].coref.feat)
w = np.array([0] * wLength)
w = perceptron.structured(w,
examples,
test=None,
epochs=38,
argmax=quotation.argmax,
phi=quotation.phi)
return w