def test_lwlr(self):
# python -m unittest tests_regression.Tests_Regression.test_lwlr
import locally_weighted_linear_regression as lwlr1
from discomll.regression import locally_weighted_linear_regression as lwlr2
x_train, y_train, x_test, y_test = datasets.regression_data()
train_data, test_data = datasets.regression_data_discomll()
lwlr1 = lwlr1.Locally_Weighted_Linear_Regression()
taus = [1, 10, 25]
sorted_indices = np.argsort([str(el) for el in x_test[:, 1].tolist()])
for tau in taus:
thetas1, estimation1 = lwlr1.fit(x_train, y_train, x_test, tau=tau)
thetas1, estimation1 = np.array(thetas1)[sorted_indices], np.array(
estimation1)[sorted_indices]
results = lwlr2.fit_predict(train_data, test_data, tau=tau)
thetas2, estimation2 = [], []
for x_id, (est, thetas) in result_iterator(results):
estimation2.append(est)
thetas2.append(thetas)
self.assertTrue(np.allclose(thetas1, thetas2, atol=1e-8))
self.assertTrue(np.allclose(estimation1, estimation2, atol=1e-3))
def test_lwlr(self):
# python -m unittest tests_regression.Tests_Regression.test_lwlr
import locally_weighted_linear_regression as lwlr1
from discomll.regression import locally_weighted_linear_regression as lwlr2
x_train, y_train, x_test, y_test = datasets.regression_data()
train_data, test_data = datasets.regression_data_discomll()
lwlr1 = lwlr1.Locally_Weighted_Linear_Regression()
taus = [1, 10, 25]
sorted_indices = np.argsort([str(el) for el in x_test[:, 1].tolist()])
for tau in taus:
thetas1, estimation1 = lwlr1.fit(x_train, y_train, x_test, tau=tau)
thetas1, estimation1 = np.array(thetas1)[sorted_indices], np.array(estimation1)[sorted_indices]
results = lwlr2.fit_predict(train_data, test_data, tau=tau)
thetas2, estimation2 = [], []
for x_id, (est, thetas) in result_iterator(results):
estimation2.append(est)
thetas2.append(thetas)
self.assertTrue(np.allclose(thetas1, thetas2, atol=1e-8))
self.assertTrue(np.allclose(estimation1, estimation2, atol=1e-3))
def lwlr_fit_predict(input_dict):
from discomll.regression import locally_weighted_linear_regression
predictions_url = locally_weighted_linear_regression.fit_predict(
fitting_data=input_dict["fitting_dataset"],
training_data=input_dict["training_dataset"],
tau=input_dict["tau"],
save_results=True)
return {"string": predictions_url}
def lwlr_fit_predict(input_dict):
from discomll.regression import locally_weighted_linear_regression
predictions_url = locally_weighted_linear_regression.fit_predict(
fitting_data=input_dict["fitting_dataset"],
training_data=input_dict["training_dataset"],
tau=input_dict["tau"],
save_results=True)
return {"string": predictions_url}
from disco.core import result_iterator
from discomll import dataset
from discomll.regression import locally_weighted_linear_regression
training_data = dataset.Data(
data_tag=["test:regression_data1", "test:regression_data2"],
data_type="chunk",
id_index=0,
X_indices=[0],
y_index=1)
fitting_data = dataset.Data(
data_tag=["test:regression_data_test1", "test:regression_data_test2"],
data_type="chunk",
id_index=0,
X_indices=[0],
y_index=1)
# fit fitting data to training data
results = locally_weighted_linear_regression.fit_predict(training_data,
fitting_data,
tau=10)
# output results
for k, v in result_iterator(results):
print k, v
from discomll import dataset
from discomll.regression import locally_weighted_linear_regression
train = dataset.Data(
data_tag=["http://ropot.ijs.si/data/fraction/train/xaaaaa.gz", "http://ropot.ijs.si/data/fraction/train/xaaabj.gz"],
data_type="gzip",
generate_urls=True,
X_indices=range(1, 14),
id_index=0,
y_index=14,
delimiter=",")
test = dataset.Data(data_tag=[["http://ropot.ijs.si/data/fraction/test/xaaaaa.gz"]],
data_type="gzip",
X_indices=range(1, 14),
id_index=0,
y_index=14,
delimiter=",")
predictions = locally_weighted_linear_regression.fit_predict(train, test, tau=1, samples_per_job=0, save_results=True)
print predictions
from disco.core import result_iterator
from discomll import dataset
from discomll.regression import locally_weighted_linear_regression
training_data = dataset.Data(data_tag=["test:regression_data1", "test:regression_data2"],
data_type="chunk",
id_index=0,
X_indices=[0],
y_index=1)
fitting_data = dataset.Data(data_tag=["test:regression_data_test1", "test:regression_data_test2"],
data_type="chunk",
id_index=0,
X_indices=[0],
y_index=1)
# fit fitting data to training data
results = locally_weighted_linear_regression.fit_predict(training_data, fitting_data, tau=10)
# output results
for k, v in result_iterator(results):
print k, v
from discomll import dataset
from discomll.regression import locally_weighted_linear_regression
train = dataset.Data(data_tag=[
"http://ropot.ijs.si/data/fraction/train/xaaaaa.gz",
"http://ropot.ijs.si/data/fraction/train/xaaabj.gz"
],
data_type="gzip",
generate_urls=True,
X_indices=range(1, 14),
id_index=0,
y_index=14,
delimiter=",")
test = dataset.Data(
data_tag=[["http://ropot.ijs.si/data/fraction/test/xaaaaa.gz"]],
data_type="gzip",
X_indices=range(1, 14),
id_index=0,
y_index=14,
delimiter=",")
predictions = locally_weighted_linear_regression.fit_predict(train,
test,
tau=1,
samples_per_job=0,
save_results=True)
print predictions
