def test_balanced_split_k_fold_shuffle_stratified_success():
"""
Same as balanced_stratified, shuffle doesn't
make a difference if stratified
"""
df = util.iris(['class'], size=30)
df.loc[10:20, 'class'] = 'Iris-versicolor'
df.loc[20:30, 'class'] = 'Iris-virginica'
test_df = df.copy()
arguments = {
'parameters': {
'n_splits': 3,
'shuffle': 1,
'attribute': 'groups',
'stratified': 1,
'random_state': 0,
'column': ['class']
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
instance = SplitKFoldOperation(**arguments)
result = util.execute(instance.generate_code(), {'df': df})
data = {
"groups": [
0, 2, 1, 2, 0, 1, 2, 0, 0, 1, 1, 1, 0, 0, 2, 2, 0, 1, 2, 1, 0, 1,
2, 1, 1, 0, 0, 2, 2, 2
]
}
test_out = pd.concat([test_df, pd.DataFrame(data)], axis=1)
count_percent = percent(result['out'], 3)
assert [
count_percent['group0']['Iris-setosa'],
count_percent['group1']['Iris-setosa'],
count_percent['group2']['Iris-setosa']
] == [40, 30, 30]
assert [
count_percent['group0']['Iris-versicolor'],
count_percent['group1']['Iris-versicolor'],
count_percent['group2']['Iris-versicolor']
] == [30, 40, 30]
assert [
count_percent['group0']['Iris-virginica'],
count_percent['group1']['Iris-virginica'],
count_percent['group2']['Iris-virginica']
] == [30, 30, 40]
assert result['out'].equals(test_out)
def test_uneven_split_k_fold_success():
"""
With a uneven split, you get floating point class presence percentage
in some groups
"""
df = util.iris(['class'], size=30)
df.loc[10:20, 'class'] = 'Iris-versicolor'
df.loc[20:29, 'class'] = 'Iris-virginica'
test_df = df.copy()
arguments = {
'parameters': {
'n_splits': 4,
'shuffle': 0,
'attribute': 'groups',
'stratified': 0,
'random_state': 0
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
instance = SplitKFoldOperation(**arguments)
result = util.execute(instance.generate_code(), {'df': df})
data = {
"groups": [
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3
]
}
test_out = pd.concat([test_df, pd.DataFrame(data)], axis=1)
count_percent = percent(result['out'], 4)
assert count_percent['group0']['Iris-setosa'] == 100
assert [
count_percent['group1']['Iris-setosa'],
count_percent['group1']['Iris-versicolor']
] == [25, 75]
# Here
assert [
count_percent['group2']['Iris-versicolor'],
count_percent['group2']['Iris-virginica']
] == pytest.approx([57.14, 42.85], 0.1)
assert count_percent['group3']['Iris-virginica'] == 100
assert result['out'].equals(test_out)
def test_unbalanced_split_k_fold_shuffle_success():
"""
Unbalanced example/test, versicolor is occupying almost every groups space
"""
df = util.iris(['class'], size=30)
df.loc[8:27, 'class'] = 'Iris-versicolor'
df.loc[27:29, 'class'] = 'Iris-virginica'
test_df = df.copy()
arguments = {
'parameters': {
'n_splits': 3,
'shuffle': 1,
'attribute': 'groups',
'stratified': 0,
'random_state': 0
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
instance = SplitKFoldOperation(**arguments)
result = util.execute(instance.generate_code(), {'df': df})
data = {
"groups": [
2, 1, 0, 2, 1, 1, 1, 2, 1, 2, 0, 0, 2, 0, 1, 2, 1, 0, 2, 2, 1, 2,
0, 1, 0, 2, 0, 0, 0, 1
]
}
test_out = pd.concat([test_df, pd.DataFrame(data)], axis=1)
count_percent = percent(result['out'], 3)
assert [
count_percent['group0']['Iris-setosa'],
count_percent['group0']['Iris-versicolor'],
count_percent['group0']['Iris-virginica']
] == [10, 70, 20]
assert [
count_percent['group1']['Iris-setosa'],
count_percent['group1']['Iris-versicolor'],
count_percent['group1']['Iris-virginica']
] == [40, 50, 10]
assert [
count_percent['group2']['Iris-setosa'],
count_percent['group2']['Iris-versicolor']
] == [30, 70]
assert result['out'].equals(test_out)
def test_split_k_fold_missing_input_implies_no_code_success():
arguments = {
'parameters': {
'n_splits': 3,
'shuffle': 0,
'attribute': 'groups',
'stratified': 0,
'random_state': 0
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {}
}
instance = SplitKFoldOperation(**arguments)
assert instance.generate_code() is None
def test_split_k_fold_success():
slice_size = 10
df = [
'df',
util.iris(['sepallength', 'sepalwidth', 'petalwidth', 'petallength'],
slice_size)
]
arguments = {
'parameters': {},
'named_inputs': {
'input data': df[0],
},
'named_outputs': {
'output data': 'out'
}
}
instance = SplitKFoldOperation(**arguments)
result = util.execute(instance.generate_code(), dict([df]))
assert result['out'].equals(util.iris(size=slice_size))
def test_split_k_fold_invalid_n_split_param_fail():
arguments = {
'parameters': {
'n_splits': 1,
'shuffle': 0,
'attribute': 'groups',
'stratified': 0,
'random_state': 0
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
with pytest.raises(ValueError) as val_err:
SplitKFoldOperation(**arguments)
assert "Parameter 'n_splits' must be x>=2 for task" in str(val_err.value)
def test_split_k_fold_invalid_stratified_param_fail():
arguments = {
'parameters': {
'n_splits': 3,
'shuffle': 0,
'attribute': 'groups',
'stratified': 'invalid',
'random_state': 0
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
with pytest.raises(ValueError) as val_err:
SplitKFoldOperation(**arguments)
assert "invalid literal for int() with base 10: 'invalid'" in str(
val_err.value)
def test_uneven_split_k_fold_shuffle_stratified_success():
"""
Same as uneven_stratified
"""
df = util.iris(['class'], size=30)
df.loc[10:20, 'class'] = 'Iris-versicolor'
df.loc[20:29, 'class'] = 'Iris-virginica'
test_df = df.copy()
arguments = {
'parameters': {
'n_splits': 4,
'shuffle': 1,
'attribute': 'groups',
'stratified': 1,
'column': ['class'],
'random_state': 0
},
'named_inputs': {
'input data': 'df',
},
'named_outputs': {
'output data': 'out'
}
}
instance = SplitKFoldOperation(**arguments)
result = util.execute(instance.generate_code(), {'df': df})
data = {
"groups": [
0, 3, 1, 3, 0, 2, 2, 1, 0, 1, 1, 2, 0, 1, 3, 3, 0, 2, 3, 2, 0, 1,
3, 1, 1, 0, 0, 2, 3, 2
]
}
test_out = pd.concat([test_df, pd.DataFrame(data)], axis=1)
count_percent = percent(result['out'], 4)
assert [
count_percent['group0']['Iris-setosa'],
count_percent['group0']['Iris-versicolor'],
count_percent['group0']['Iris-virginica']
] == [37.5, 25, 37.5]
assert [
count_percent['group1']['Iris-setosa'],
count_percent['group1']['Iris-versicolor'],
count_percent['group1']['Iris-virginica']
] == [37.5, 25, 37.5]
assert [
count_percent['group2']['Iris-setosa'],
count_percent['group2']['Iris-versicolor'],
count_percent['group2']['Iris-virginica']
] == pytest.approx([28.57, 42.85, 28.57], 0.1)
assert [
count_percent['group3']['Iris-setosa'],
count_percent['group3']['Iris-versicolor'],
count_percent['group3']['Iris-virginica']
] == pytest.approx([28.57, 42.85, 28.57], 0.1)
assert result['out'].equals(test_out)