def test_dump(resource_tmp_file):
# synonym of save
p = Pipe() +\
Segment(lambda x: x+1, "step1") + \
Segment(lambda x: x+1, "step2")
p._dump(resource_tmp_file)
def test_sklearn_pipe_fit_predict1():
p = Pipe() +\
Segment(anova_filter, 'anova') +\
Segment(clf, 'svc')
p.fit(X, y)
p.predict(X)
def test_load_save_utility_funcs(resource_tmp_file):
from mlpipe.utils import save, load
p = Pipe() +\
Segment(lambda x: x+1, "step1") + \
Segment(lambda x: x+1, "step2")
save(p, resource_tmp_file)
load(resource_tmp_file)
def test_save_fitted_load_predict(resource_tmp_file):
p = Pipe() +\
Segment(anova_filter, 'anova') +\
Segment(clf, 'svc')
p.fit(X, y)
p._save(resource_tmp_file)
p = Pipe._load(resource_tmp_file)
p.predict(X)
def test_sklearn_pipe_serialize():
def dummy(*args):
return args
p = Pipe() +\
Segment(anova_filter, 'anova') +\
Segment(dummy) +\
Segment(clf, 'svc')
p.fit(X, y)
p.predict(X, y=None)
def test_no_input():
p = Pipe() +\
Segment(lambda *_: 2)
p2 = Pipe() + \
Segment(lambda *_: 2) + \
Segment(lambda x: x)
assert p() == 2
assert p(5) == 2
assert p2() == 2
assert p2(5) == 2
def test_kwargs2():
p = Pipe() +\
Segment(my_func, description="test", b=1) +\
Segment(my_func, description="test", b=2)
assert p(1) == 4
assert p.bla(1) == 4
# def test_args():
# p = Pipe() +\
# Segment(my_func, description="test", 1)
#
# assert p(1) == 2
def test_save(resource_tmp_file):
p = Pipe() +\
Segment(lambda x: x+1, "step1") + \
Segment(lambda x: x+1, "step2")
p._save(resource_tmp_file)
def test_dumps():
p = Pipe() +\
Segment(lambda x: x+1, "step1") + \
Segment(lambda x: x+1, "step2")
p._dumps()
def test_lambda1_wo_attr():
p = Pipe() +\
Segment(lambda x: x+2)
assert p(5) == 7
def test_lambda2_w_attr():
p = Pipe() +\
Segment(lambda x: x+2) + \
Segment(lambda x: x + 2)
assert p.testattr(5) == 9
def test_kwargs():
p = Pipe() + Segment(my_func, b=1)
assert p(1) == 2
assert p.bla(1) == 2