def test_json_subtri():
assert (
cl.read_json(
cl.Chainladder().fit_predict(cl.load_sample("raa")).to_json()
).full_triangle_
== cl.Chainladder().fit_predict(cl.load_sample("raa")).full_triangle_
)
def test_pipeline_json_io():
pipe = cl.Pipeline(
steps=[('dev', cl.Development()), ('model', cl.BornhuetterFerguson())])
pipe2 = cl.read_json(pipe.to_json())
assert {item[0]: item[1].get_params()
for item in pipe.get_params()['steps']} == \
{item[0]: item[1].get_params()
for item in pipe2.get_params()['steps']}
def test_pipeline_json_io():
pipe = cl.Pipeline(
steps=[("dev", cl.Development()), ("model", cl.BornhuetterFerguson())]
)
pipe2 = cl.read_json(pipe.to_json())
assert {item[0]: item[1].get_params() for item in pipe.get_params()["steps"]} == {
item[0]: item[1].get_params() for item in pipe2.get_params()["steps"]
}
def test_triangle_json_io():
clrd = cl.load_dataset('clrd')
clrd2 = cl.read_json(clrd.to_json())
np.testing.assert_equal(clrd.values, clrd2.values)
np.testing.assert_equal(clrd.kdims, clrd2.kdims)
np.testing.assert_equal(clrd.vdims, clrd2.vdims)
np.testing.assert_equal(clrd.odims, clrd2.odims)
np.testing.assert_equal(clrd.ddims, clrd2.ddims)
assert np.all(clrd.valuation == clrd2.valuation)
def test_triangle_json_io(clrd):
xp = clrd.get_array_module()
clrd2 = cl.read_json(clrd.to_json(), array_backend=clrd.array_backend)
xp.testing.assert_array_equal(clrd.values, clrd2.values)
xp.testing.assert_array_equal(clrd.kdims, clrd2.kdims)
xp.testing.assert_array_equal(clrd.vdims, clrd2.vdims)
xp.testing.assert_array_equal(clrd.odims, clrd2.odims)
xp.testing.assert_array_equal(clrd.ddims, clrd2.ddims)
assert np.all(clrd.valuation == clrd2.valuation)
def test_triangle_json_io():
clrd = cl.load_sample('clrd')
xp = clrd.get_array_module()
clrd2 = cl.read_json(clrd.to_json())
xp.testing.assert_array_equal(clrd.values, clrd2.values)
xp.testing.assert_array_equal(clrd.kdims, clrd2.kdims)
xp.testing.assert_array_equal(clrd.vdims, clrd2.vdims)
xp.testing.assert_array_equal(clrd.odims, clrd2.odims)
xp.testing.assert_array_equal(clrd.ddims, clrd2.ddims)
assert np.all(clrd.valuation == clrd2.valuation)
def test_estimator_json_io():
assert cl.read_json(cl.Development().to_json()).get_params() == \
cl.Development().get_params()
def test_json_df():
x = cl.MunichAdjustment(paid_to_incurred=('paid',
'incurred')).fit_transform(
cl.load_sample('mcl'))
assert abs(cl.read_json(x.to_json()).lambda_ - x.lambda_).sum() < 1e-5
def test_json_for_val():
x = cl.load_sample('raa').dev_to_val().to_json()
assert cl.read_json(x) == cl.load_sample('raa').dev_to_val()
def test_json_subtri():
a = cl.read_json(cl.Chainladder().fit_predict(
cl.load_sample("raa")).to_json()).full_triangle_
b = cl.Chainladder().fit_predict(cl.load_sample("raa")).full_triangle_
abs(a - b).max().max() < 1e-4
def test_json_subtri(raa):
a = cl.read_json(
cl.Chainladder().fit_predict(raa).to_json()
).full_triangle_
b = cl.Chainladder().fit_predict(raa).full_triangle_
abs(a - b).max().max() < 1e-4
def test_json_for_val(raa):
x = raa.dev_to_val().to_json()
assert cl.read_json(x) == raa.dev_to_val()
