def test_partition_iterator(self):
fines_issued = np.array([(2001, 12.31), (1999, 14.32), (1999, 120.76),
(2002, 32.12), (2004, 98.64), (2005, 32.21),
(2002, 100.23), (2006, 123.40),
(2000, 72.21)],
dtype=[('year', int), ('fine', float)])
y = fines_issued['year']
training_windows = by_window_ranges(1999, 2000, 2004, 2)
testing_windows = by_window_ranges(2001, 2002, 2006, 2)
ctrls = {
ByWindowMode.SLIDING: [(set([8, 1, 2]), set([0, 3, 6])),
(set([0, 3, 6]), set([4])),
(set([4]), set([5, 7]))],
ByWindowMode.EXPANDING: [(set([8, 1, 2]), set([0, 3, 6])),
(set([0, 1, 2, 3, 6, 8]), set([4])),
(set([0, 1, 2, 3, 4, 6, 8]), set([5, 7]))]
}
for mode in ctrls:
bw = ByWindow(y, training_windows, testing_windows, mode)
result = [(set(train_inds), set(test_inds))
for train_inds, test_inds in bw]
ctrl = ctrls[mode]
self.assertEqual(result, ctrl)
self.assertEqual(
ByWindow.est_n_folds(y, training_windows, testing_windows,
mode), len(result))
def test_by_window_ranges(self):
self.assertEqual(by_window_ranges(1999, 2000, 2006, 2), [(1999, 2000),
(2001, 2002),
(2003, 2004),
(2005, 2006)])
self.assertEqual(by_window_ranges(1, 3, 7, 1), [(1, 3), (2, 4), (3, 5),
(4, 6), (5, 7)])
def test_partition_iterator(self):
fines_issued = np.array([(2001, 12.31), (1999, 14.32), (1999, 120.76),
(2002, 32.12), (2004, 98.64), (2005, 32.21),
(2002, 100.23), (2006, 123.40), (2000, 72.21)],
dtype=[('year', int), ('fine', float)])
y = fines_issued['year']
training_windows = by_window_ranges(1999, 2000, 2004, 2)
testing_windows = by_window_ranges(2001, 2002, 2006, 2)
ctrls = {ByWindowMode.SLIDING: [(set([8, 1, 2]), set([0, 3, 6])),
(set([0, 3, 6]), set([4])),
(set([4]), set([5, 7]))],
ByWindowMode.EXPANDING: [(set([8, 1, 2]), set([0, 3, 6])),
(set([0, 1, 2, 3, 6, 8]),
set([4])),
(set([0, 1, 2, 3, 4, 6, 8]),
set([5, 7]))]}
for mode in ctrls:
bw = ByWindow(y,
training_windows,
testing_windows,
mode)
result = [(set(train_inds), set(test_inds)) for
train_inds, test_inds in bw]
ctrl = ctrls[mode]
self.assertEqual(result, ctrl)
self.assertEqual(ByWindow.est_n_folds(
y,
training_windows,
testing_windows,
mode), len(result))
def test_by_window_ranges(self):
self.assertEqual(
by_window_ranges(1999, 2000, 2006, 2),
[(1999, 2000), (2001, 2002), (2003, 2004), (2005, 2006)])
self.assertEqual(by_window_ranges(1, 3, 7, 1),
[(1, 3), (2, 4), (3, 5), (4, 6), (5, 7)])
def test_wrap_cross_validation(self):
X = np.array(
[
(0, 2001, 12.31),
(1, 1999, 14.32),
(2, 1999, 120.76),
(3, 2002, 32.12),
(4, 2004, 98.64),
(5, 2005, 32.21),
(6, 2002, 100.23),
(7, 2006, 123.40),
(8, 2000, 72.21),
],
dtype=[("id", int), ("year", int), ("fine", float)],
)
y = np.array([(0,), (1,), (0,), (1,), (0,), (1,), (0,), (1,), (0,)], dtype=[("category", int)])
ctrl_inds = [([1, 2, 8], [0, 3, 6]), ([0, 3, 6], [4]), ([4], [5, 7])]
p = Pipeline()
node_X_in = p.add(NumpyRead(X))
node_y_in = p.add(NumpyRead(y))
node_just_time = p.add(SplitColumns(["year"]))
node_just_time(node_X_in)
training_windows = by_window_ranges(1999, 2000, 2004, 2)
testing_windows = by_window_ranges(2001, 2002, 2006, 2)
mode = ByWindowMode.SLIDING
node_cv = p.add(
wrap_and_make_instance(
"upsg.transform.partition_iterators.ByWindow",
n_arrays=2,
training_windows=training_windows,
testing_windows=testing_windows,
mode=ByWindowMode.SLIDING,
)
)
node_cv(input0=node_X_in, input1=node_y_in, y=node_just_time)
self.assertEqual(len(node_cv.output_keys), 2 * 2 * len(ctrl_inds))
out_nodes = []
for i in xrange(len(ctrl_inds)):
train_node_X = p.add(NumpyWrite())
train_node_X(node_cv["train0_{}".format(i)])
train_node_y = p.add(NumpyWrite())
train_node_y(node_cv["train1_{}".format(i)])
test_node_X = p.add(NumpyWrite())
test_node_X(node_cv["test0_{}".format(i)])
test_node_y = p.add(NumpyWrite())
test_node_y(node_cv["test1_{}".format(i)])
out_nodes.append((train_node_X, train_node_y, test_node_X, test_node_y))
p.run()
for i, (train_node_X, train_node_y, test_node_X, test_node_y) in enumerate(out_nodes):
self.assertTrue(np.array_equal(train_node_X.get_stage().result, X[ctrl_inds[i][0]]))
self.assertTrue(np.array_equal(train_node_y.get_stage().result, y[ctrl_inds[i][0]]))
self.assertTrue(np.array_equal(test_node_X.get_stage().result, X[ctrl_inds[i][1]]))
self.assertTrue(np.array_equal(test_node_y.get_stage().result, y[ctrl_inds[i][1]]))
def test_wrap_cross_validation(self):
X = np.array([(0, 2001, 12.31), (1, 1999, 14.32), (2, 1999, 120.76),
(3, 2002, 32.12), (4, 2004, 98.64), (5, 2005, 32.21),
(6, 2002, 100.23), (7, 2006, 123.40), (8, 2000, 72.21)],
dtype=[('id', int), ('year', int), ('fine', float)])
y = np.array([(0, ), (1, ), (0, ), (1, ), (0, ), (1, ), (0, ), (1, ),
(0, )],
dtype=[('category', int)])
ctrl_inds = [([1, 2, 8], [0, 3, 6]), ([0, 3, 6], [4]), ([4], [5, 7])]
p = Pipeline()
node_X_in = p.add(NumpyRead(X))
node_y_in = p.add(NumpyRead(y))
node_just_time = p.add(SplitColumns(['year']))
node_just_time(node_X_in)
training_windows = by_window_ranges(1999, 2000, 2004, 2)
testing_windows = by_window_ranges(2001, 2002, 2006, 2)
mode = ByWindowMode.SLIDING
node_cv = p.add(
wrap_and_make_instance(
'upsg.transform.partition_iterators.ByWindow',
n_arrays=2,
training_windows=training_windows,
testing_windows=testing_windows,
mode=ByWindowMode.SLIDING))
node_cv(input0=node_X_in, input1=node_y_in, y=node_just_time)
self.assertEqual(len(node_cv.output_keys), 2 * 2 * len(ctrl_inds))
out_nodes = []
for i in xrange(len(ctrl_inds)):
train_node_X = p.add(NumpyWrite())
train_node_X(node_cv['train0_{}'.format(i)])
train_node_y = p.add(NumpyWrite())
train_node_y(node_cv['train1_{}'.format(i)])
test_node_X = p.add(NumpyWrite())
test_node_X(node_cv['test0_{}'.format(i)])
test_node_y = p.add(NumpyWrite())
test_node_y(node_cv['test1_{}'.format(i)])
out_nodes.append(
(train_node_X, train_node_y, test_node_X, test_node_y))
p.run()
for i, (train_node_X, train_node_y, test_node_X, test_node_y) in \
enumerate(out_nodes):
self.assertTrue(
np.array_equal(train_node_X.get_stage().result,
X[ctrl_inds[i][0]]))
self.assertTrue(
np.array_equal(train_node_y.get_stage().result,
y[ctrl_inds[i][0]]))
self.assertTrue(
np.array_equal(test_node_X.get_stage().result,
X[ctrl_inds[i][1]]))
self.assertTrue(
np.array_equal(test_node_y.get_stage().result,
y[ctrl_inds[i][1]]))
