import numpy as np
import Orange
import discomll
from discomll import dataset
path = "/".join(discomll.__file__.split("/")[:-2] + ["discomll", "datasets", ""])
def breastcancer_disc_orange(replication=2):
test_samples = 100
data = Orange.data.Table("breast-cancer-wisconsin")
train_data = data[:-test_samples]
test_data = data[-test_samples:]
for j in range(replication - 1):
for i in range(len(train_data)):
train_data.append(train_data[i])
for i in range(len(test_data)):
test_data.append(test_data[i])
return train_data, test_data
def breastcancer_cont_orange(replication=2):
test_samples = 100
data = Orange.data.Table("breast-cancer-wisconsin-cont")
train_data = data[:-test_samples]
test_data = data[-test_samples:]
for j in range(replication - 1):
for i in range(len(train_data)):
train_data.append(train_data[i])
for i in range(len(test_data)):
test_data.append(test_data[i])
return train_data, test_data
def breastcancer_disc_discomll(replication=2):
data_tag = ["test:breast_cancer_disc" for i in range(replication)]
train_data = dataset.Data(data_tag=data_tag,
data_type="chunk",
X_indices=xrange(1, 10),
X_meta=["d" for i in range(9)],
id_index=0,
y_index=10,
delimiter=",",
y_map=["2", "4"])
data_tag = ["test:breast_cancer_disc_test" for i in range(replication)]
test_data = dataset.Data(data_tag=data_tag,
data_type="chunk",
X_indices=xrange(1, 10),
X_meta=["d" for i in range(9)],
id_index=0,
y_index=10,
delimiter=",",
y_map=["2", "4"],
missing_vals=["?"])
return train_data, test_data
def breastcancer_cont_discomll(replication=2):
data_tag = ["test:breast_cancer_cont" for _ in range(replication)]
train_data = dataset.Data(data_tag=data_tag,
data_type="chunk",
X_indices=xrange(0, 9),
X_meta=["c" for i in range(9)],
y_index=9,
delimiter=",",
y_map=["benign", "malign"])
data_tag = ["test:breast_cancer_cont_test" for i in range(replication)]
test_data = dataset.Data(data_tag=data_tag,
data_type="chunk",
X_indices=xrange(0, 9),
X_meta=["c" for i in range(9)],
y_index=9,
delimiter=",",
y_map=["benign", "malign"])
return train_data, test_data
def breastcancer_cont(replication=2):
f = open(path + "breast_cancer_wisconsin_cont.txt", "r")
data = np.loadtxt(f, delimiter=",", dtype=np.string0)
x_train = np.array(data[:, range(0, 9)])
y_train = np.array(data[:, 9])
for j in range(replication - 1):
x_train = np.vstack([x_train, data[:, range(0, 9)]])
y_train = np.hstack([y_train, data[:, 9]])
x_train = np.array(x_train, dtype=np.float)
f = open(path + "breast_cancer_wisconsin_cont_test.txt")
data = np.loadtxt(f, delimiter=",", dtype=np.string0)
x_test = np.array(data[:, range(0, 9)])
y_test = np.array(data[:, 9])
for j in range(replication - 1):
x_test = np.vstack([x_test, data[:, range(0, 9)]])
y_test = np.hstack([y_test, data[:, 9]])
x_test = np.array(x_test, dtype=np.float)
return x_train, y_train, x_test, y_test
def breastcancer_disc(replication=2):
f = open(path + "breast_cancer_wisconsin_disc.txt")
data = np.loadtxt(f, delimiter=",")
x_train = data[:, range(1, 10)]
y_train = data[:, 10]
for j in range(replication - 1):
x_train = np.vstack([x_train, data[:, range(1, 10)]])
y_train = np.hstack([y_train, data[:, 10]])
f = open(path + "breast_cancer_wisconsin_disc_test.txt")
data = np.loadtxt(f, delimiter=",")
x_test = data[:, range(1, 10)]
y_test = data[:, 10]
for j in range(replication - 1):
x_test = np.vstack([x_test, data[:, range(1, 10)]])
y_test = np.hstack([y_test, data[:, 10]])
return x_train, y_train, x_test, y_test
def ex4_orange(replication=2):
f = open(path + "ex4.txt")
data = np.loadtxt(f, delimiter=",")
features = [Orange.feature.Continuous("atr1"), Orange.feature.Continuous("atr2")]
classattr = Orange.feature.Continuous("class")
domain = Orange.data.Domain(features + [classattr])
train_data = Orange.data.Table(domain)
for j in range(replication):
for row in data:
train_data.append(row.tolist())
return train_data
def ex4_discomll(replication=2):
data_tag = ["test:ex4" for i in range(replication)]
data = dataset.Data(data_tag=data_tag,
data_type="chunk",
X_indices=xrange(0, 2),
y_index=2,
y_map=["0.0000000e+00", "1.0000000e+00"])
return data
def iris(replication=2):
f = open(path + "iris.txt")
data = np.loadtxt(f, delimiter=",", dtype=np.string0)
x_train = np.array(data[:, range(0, 4)], dtype=np.float)
y_train = data[:, 4]
for j in range(replication - 1):
x_train = np.vstack([x_train, data[:, range(0, 4)]])
y_train = np.hstack([y_train, data[:, 4]])
x_train = np.array(x_train, dtype=np.float)
f = open(path + "iris_test.txt")
data = np.loadtxt(f, delimiter=",", dtype=np.string0)
x_test = np.array(data[:, range(0, 4)], dtype=np.float)
y_test = data[:, 4]
for j in range(replication - 1):
x_test = np.vstack([x_test, data[:, range(0, 4)]])
y_test = np.hstack([y_test, data[:, 4]])
x_test = np.array(x_test, dtype=np.float)
return x_train, y_train, x_test, y_test
def iris_discomll(replication=2):
data_tag = ["test:iris" for i in range(replication)]
train_data = dataset.Data(data_tag=data_tag,
data_type="chunk",
X_indices=xrange(0, 4),
X_meta=["c" for i in xrange(0, 4)],
y_index=4,
delimiter=",")
data_tag = ["test:iris_test" for i in range(replication)]
test_data = dataset.Data(data_tag=data_tag,
data_type="chunk",
X_indices=xrange(0, 4),
X_meta=["c" for i in xrange(0, 4)],
y_index=4,
delimiter=",")
return train_data, test_data
def regression_data():
f = open(path + "regression_data1.txt")
data = np.loadtxt(f, delimiter=",")
x1 = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1)
y1 = data[:, 1]
f = open(path + "regression_data2.txt")
data = np.loadtxt(f, delimiter=",")
x2 = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1)
y2 = data[:, 1]
x1 = np.vstack((x1, x2))
y1 = np.hstack((y1, y2))
f = open(path + "regression_data_test1.txt")
data = np.loadtxt(f, delimiter=",")
x1_test = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1)
y1_test = data[:, 1]
f = open(path + "regression_data_test2.txt")
data = np.loadtxt(f, delimiter=",")
x2_test = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1)
y2_test = data[:, 1]
x1_test = np.vstack((x1_test, x2_test))
y1_test = np.hstack((y1_test, y2_test))
return x1, y1, x1_test, y1_test
def regression_data_discomll():
train = dataset.Data(data_tag=["test:regression_data1", "test:regression_data2"],
data_type="chunk",
id_index=0,
X_indices=[0],
X_meta=["c"],
y_index=1)
test = dataset.Data(data_tag=["test:regression_data_test1", "test:regression_data_test2"],
data_type="chunk",
id_index=0,
X_indices=[0],
X_meta=["c"],
y_index=1)
return train, test
def ex3(replication=2):
f = open(path + "ex3.txt")
train_data = np.loadtxt(f, delimiter=",")
f = open(path + "ex3_test.txt")
test_data = np.loadtxt(f, delimiter=",")
x_train = np.insert(train_data[:, (0, 1)], 0, np.ones(len(train_data)), axis=1)
y_train = train_data[:, 2]
x_test = np.insert(test_data[:, (0, 1)], 0, np.ones(len(test_data)), axis=1)
y_test = test_data[:, 2]
for i in range(replication - 1):
x_train = np.vstack((x_train, np.insert(train_data[:, (0, 1)], 0, np.ones(len(train_data)), axis=1)))
y_train = np.hstack((y_train, train_data[:, 2]))
x_test = np.vstack((x_test, np.insert(test_data[:, (0, 1)], 0, np.ones(len(test_data)), axis=1)))
y_test = np.hstack((y_test, test_data[:, 2]))
return x_train, y_train, x_test, y_test
def ex3_discomll(replication=2):
data_tag = ["test:ex3" for i in range(replication)]
train_data = dataset.Data(data_tag=data_tag,
data_type="chunk",
X_indices=[0, 1],
y_index=2)
data_tag = ["test:ex3_test" for i in range(replication)]
test_data = dataset.Data(data_tag=data_tag,
data_type="chunk",
X_indices=[0, 1],
y_index=2)
return train_data, test_data
