class ColumnModelTest(test_util.PWLFitTest, parameterized.TestCase):
def test_simple_properties(self):
model = model_util.ColumnModel('x1')
self.assertEqual('x1', model.name)
self.assertEqual('x1', model.feature_name)
@parameterized.named_parameters((
'simple ',
model_util.ColumnModel('x1'),
model_util.ColumnModel('x1'),
True,
), (
'not a ColumnModel',
model_util.ColumnModel('x1'),
's',
False,
), (
'different columns',
model_util.ColumnModel('x1'),
model_util.ColumnModel('x2'),
False,
))
def test_eq(self, m1, m2, expected):
self.assertEqual(expected, m1 == m2)
def test_eval(self):
model = model_util.ColumnModel('x1')
self.assert_allclose(
np.array([1.0, 2.0, 3.0]),
model.eval(pd.DataFrame({'x1': [1.0, 2.0, 3.0]})))
def test_expr(self):
model = model_util.ColumnModel('x1')
self.assertEqual('Column("x1")', model.expr())
@parameterized.named_parameters((
'simple',
'Column("x1")',
), (
'extra spaces',
'Column( "x1" )',
))
def test_from_expr(self, s):
model = model_util.ColumnModel('x1')
self.assertEqual(model, model_util.ColumnModel.from_expr(s))
def test_from_expr(self, expr):
model = model_util.AdditiveModel([
model_util.PWLCurveModel('x1', [(1, 1), (2, 2)]),
model_util.EnumCurveModel('x2', {
1: 1,
2: 2
}),
model_util.ColumnModel('x3')
], 'my_model')
self.assertEqual(model,
model_util.AdditiveModel.from_expr(expr, 'my_model'))
def test_eval(self):
model = model_util.AdditiveModel([
model_util.PWLCurveModel('x1', [(1, 1), (2, 2)]),
model_util.EnumCurveModel('x2', {
1: 10,
2: 20
}),
model_util.ColumnModel('x3')
], 'my_model')
df = pd.DataFrame({'x1': [1, 1, 2], 'x2': [1, 1, 2], 'x3': [3, 3, 3]})
self.assert_allclose(np.array([14, 14, 25]), model.eval(df))
def test_simple_properties(self):
model = model_util.AdditiveModel([
model_util.PWLCurveModel('x1', [(1, 1), (2, 2)]),
model_util.EnumCurveModel('x2', {
1: 1,
2: 2
}),
model_util.ColumnModel('x3')
], 'my_model')
self.assertEqual('my_model', model.name)
self.assertEqual(['x1', 'x2', 'x3'], model.feature_names)
def test_expr(self):
model = model_util.AdditiveModel([
model_util.PWLCurveModel('x1', [(1, 1), (2, 2)]),
model_util.EnumCurveModel('x2', {
1: 1,
2: 2
}),
model_util.ColumnModel('x3')
], 'my_model')
expr = """score = sum([
PWLCurve("x1", [(1, 1), (2, 2)]),
EnumCurve("x2", {1: 1, 2: 2}),
Column("x3"),
])"""
self.assertEqual(expr, model.expr())
def test_no_duplicate_features_allowed(self):
with self.assertRaisesRegex(ValueError, 'Duplicate submodels'):
model_util.AdditiveModel([
model_util.ColumnModel('x1'),
model_util.PWLCurveModel('x1', [(1, 1), (2, 2)])
], 'my_model')
class AdditiveModelTest(test_util.PWLFitTest, parameterized.TestCase):
def test_simple_properties(self):
model = model_util.AdditiveModel([
model_util.PWLCurveModel('x1', [(1, 1), (2, 2)]),
model_util.EnumCurveModel('x2', {
1: 1,
2: 2
}),
model_util.ColumnModel('x3')
], 'my_model')
self.assertEqual('my_model', model.name)
self.assertEqual(['x1', 'x2', 'x3'], model.feature_names)
@parameterized.named_parameters((
'not too sensitive ',
model_util.AdditiveModel([
model_util.ColumnModel('x1'),
model_util.ColumnModel('x2'),
], 'my_model'),
model_util.AdditiveModel([
model_util.ColumnModel('x2'),
model_util.ColumnModel('x1'),
], 'my_model'),
True,
), (
'not an AdditiveModel',
model_util.AdditiveModel([
model_util.ColumnModel('x1'),
model_util.ColumnModel('x2'),
], 'my_model'),
's',
False,
), (
'different submodels',
model_util.AdditiveModel([
model_util.ColumnModel('x1'),
], 'my_model'),
model_util.AdditiveModel([
model_util.ColumnModel('x2'),
], 'my_model'),
False,
), (
'different names',
model_util.AdditiveModel([
model_util.ColumnModel('x1'),
], 'my_model1'),
model_util.AdditiveModel([
model_util.ColumnModel('x1'),
], 'my_model2'),
False,
))
def test_eq(self, m1, m2, expected):
self.assertEqual(expected, m1 == m2)
def test_eval(self):
model = model_util.AdditiveModel([
model_util.PWLCurveModel('x1', [(1, 1), (2, 2)]),
model_util.EnumCurveModel('x2', {
1: 10,
2: 20
}),
model_util.ColumnModel('x3')
], 'my_model')
df = pd.DataFrame({'x1': [1, 1, 2], 'x2': [1, 1, 2], 'x3': [3, 3, 3]})
self.assert_allclose(np.array([14, 14, 25]), model.eval(df))
def test_expr(self):
model = model_util.AdditiveModel([
model_util.PWLCurveModel('x1', [(1, 1), (2, 2)]),
model_util.EnumCurveModel('x2', {
1: 1,
2: 2
}),
model_util.ColumnModel('x3')
], 'my_model')
expr = """score = sum([
PWLCurve("x1", [(1, 1), (2, 2)]),
EnumCurve("x2", {1: 1, 2: 2}),
Column("x3"),
])"""
self.assertEqual(expr, model.expr())
@parameterized.named_parameters((
'simple',
"""score = sum([
PWLCurve("x1", [(1, 1), (2, 2)]),
EnumCurve("x2", {1: 1, 2: 2}),
Column("x3"),
])
""",
), (
'flexible',
"""score = sum([PWLCurve("x1", [(1, 1), (2, 2)]),
EnumCurve("x2", {1: 1, 2: 2}),
Column( "x3" ),
])
""",
))
def test_from_expr(self, expr):
model = model_util.AdditiveModel([
model_util.PWLCurveModel('x1', [(1, 1), (2, 2)]),
model_util.EnumCurveModel('x2', {
1: 1,
2: 2
}),
model_util.ColumnModel('x3')
], 'my_model')
self.assertEqual(model,
model_util.AdditiveModel.from_expr(expr, 'my_model'))
def test_no_duplicate_features_allowed(self):
with self.assertRaisesRegex(ValueError, 'Duplicate submodels'):
model_util.AdditiveModel([
model_util.ColumnModel('x1'),
model_util.PWLCurveModel('x1', [(1, 1), (2, 2)])
], 'my_model')
def test_from_expr(self, s):
model = model_util.ColumnModel('x1')
self.assertEqual(model, model_util.ColumnModel.from_expr(s))
def test_expr(self):
model = model_util.ColumnModel('x1')
self.assertEqual('Column("x1")', model.expr())
def test_eval(self):
model = model_util.ColumnModel('x1')
self.assert_allclose(np.array([1.0, 2.0, 3.0]),
model.eval(pd.DataFrame({'x1': [1.0, 2.0, 3.0]})))
def test_simple_properties(self):
model = model_util.ColumnModel('x1')
self.assertEqual('x1', model.name)
self.assertEqual('x1', model.feature_name)
