def test_gradient_descent(self):
scale = Scale.scale_from_data(self.test_dataset)
# self.logreg = LogisticRegressor(self.train_dataset, self.train_labels, scale=scale)
self.logreg = LogisticRegressor(self.train_dataset, self.train_labels)
gr = GradientDescentRunner(self.logreg.get_gradient(), len(self.samples[0]) + 1,
self.logreg.get_error_function(), alpha=1e-8, max_iter=300)
weights = gr.run()
self.logreg.weights = weights
mismatches = 0
predictions = [self.logreg.predict(test_data) for test_data in self.test_dataset]
print "All predictions: ", predictions
for test_data, label in zip(self.test_dataset, self.test_labels):
prediction = self.logreg.predict(test_data)
if prediction != label:
mismatches +=1
# print "Mismatch! Predicted ", prediction, ", True ", label
total = len(self.test_dataset)
print "total data: ", total
print "total mismatch: ", mismatches
print "percentage success: ", (100 - float(mismatches) / float(total) * 100), "%"
def test_gradient_descent(self):
scale = Scale.scale_from_data(self.test_dataset)
# self.logreg = LogisticRegressor(self.train_dataset, self.train_labels, scale=scale)
self.logreg = LogisticRegressor(self.train_dataset, self.train_labels)
gr = GradientDescentRunner(self.logreg.get_gradient(),
len(self.samples[0]) + 1,
self.logreg.get_error_function(),
alpha=1e-8,
max_iter=300)
weights = gr.run()
self.logreg.weights = weights
mismatches = 0
predictions = [
self.logreg.predict(test_data) for test_data in self.test_dataset
]
print "All predictions: ", predictions
for test_data, label in zip(self.test_dataset, self.test_labels):
prediction = self.logreg.predict(test_data)
if prediction != label:
mismatches += 1
# print "Mismatch! Predicted ", prediction, ", True ", label
total = len(self.test_dataset)
print "total data: ", total
print "total mismatch: ", mismatches
print "percentage success: ", (
100 - float(mismatches) / float(total) * 100), "%"
def test_gradient_descent(samples, test_data, scikit_pred):
reg = LinearRegressor(samples, labels)
gr = GradientDescentRunner(reg.get_gradient(), len(samples[0]) + 1)
_, weights = gr.run_once()
reg.weights = weights
p = reg.predict(test_data)
print "Prediction from gradient descent: ", p
def test_lets_just_look_at_the_outputs(self):
gr = GradientDescentRunner(self.logreg.get_gradient(), len(self.samples[0]) + 1,
self.logreg.get_error_function(), alpha=1e-8, max_iter=300)
_, weights = gr.run_once()
self.logreg.weights = weights
predictions = [self.logreg.get_probability(d) for d in self.train_dataset]
import pprint
pprint.pprint(zip(predictions, self.train_labels))
def test_lets_just_look_at_the_outputs(self):
gr = GradientDescentRunner(self.logreg.get_gradient(),
len(self.samples[0]) + 1,
self.logreg.get_error_function(),
alpha=1e-8,
max_iter=300)
_, weights = gr.run_once()
self.logreg.weights = weights
predictions = [
self.logreg.get_probability(d) for d in self.train_dataset
]
import pprint
pprint.pprint(zip(predictions, self.train_labels))
