def test_polynomial(self):
n = 4
poly = 2
data, expected = range(n), range(n)
squared = [e**poly for e in expected]
expected.extend(squared)
output = lib.polynomial(data, poly)
leftover = (output - expected).any()
assert leftover == False, "{0} != {1}".format(output, expected)
def test_polynomial(self):
n = 4
poly = 2
data, expected = range(n), range(n)
squared = [e**poly for e in expected]
expected.extend(squared)
output = lib.polynomial(data, poly)
leftover = (output-expected).any()
assert leftover == False, "{0} != {1}".format(output, expected)
def optimize(self, data, targets):
model = LinearRegressionModel(Objective.MINIMIZE, log_level=self.logger.level)
model.optimize(data, targets)
best = model.best_score
self.polynomial = 1
for p in range(1, FeatureConstants.MAX_POLYNOMIAL + 1):
new_data = lib.polynomial(data, p)
model = LinearRegressionModel(Objective.MINIMIZE, log_level=self.logger.level)
model.optimize(new_data, targets)
self.logger.info("score={0} with polynomial {1}".format(model.best_score, p))
if self.objective == Objective.MINIMIZE:
if model.best_score < best:
self.polynomial = p
best = model.best_score
elif self.objective == Objective.MAXIMIZE:
if model.best_score > best:
self.polynomial = p
best = model.best_score
def optimize(self, data, targets):
model = LinearRegressionModel(Objective.MINIMIZE,
log_level=self.logger.level)
model.optimize(data, targets)
best = model.best_score
self.polynomial = 1
for p in range(1, FeatureConstants.MAX_POLYNOMIAL + 1):
new_data = lib.polynomial(data, p)
model = LinearRegressionModel(Objective.MINIMIZE,
log_level=self.logger.level)
model.optimize(new_data, targets)
self.logger.info("score={0} with polynomial {1}".format(
model.best_score, p))
if self.objective == Objective.MINIMIZE:
if model.best_score < best:
self.polynomial = p
best = model.best_score
elif self.objective == Objective.MAXIMIZE:
if model.best_score > best:
self.polynomial = p
best = model.best_score
def transform(self, dataset, exponent):
return lib.polynomial(dataset, exponent)
