def test_best_current_value_lesser_is_better():
df = pandas.DataFrame.from_dict({
'model_group_id': ['1', '2', '3', '1', '2'],
'model_id': ['1', '2', '3', '4', '5'],
'train_end_time':
['2011-01-01', '2011-01-01', '2011-01-01', '2012-01-01', '2012-01-01'],
'metric': [
'false positives@', 'false positives@', 'false positives@',
'false positives@', 'false positives@'
],
'parameter': ['100_abs', '100_abs', '100_abs', '100_abs', '100_abs'],
'raw_value': [40, 50, 55, 60, 70],
'dist_from_best_case': [0, 10, 5, 0, 10],
})
assert best_current_value(df,
'2011-01-01',
'false positives@',
'100_abs',
n=2) == ['1', '2']
assert best_current_value(df,
'2012-01-01',
'false positives@',
'100_abs',
n=1) == ['1']
def test_best_current_value_lesser_is_better():
df = pd.DataFrame.from_dict(
{
"model_group_id": ["1", "2", "3", "1", "2"],
"model_id": ["1", "2", "3", "4", "5"],
"train_end_time": [
"2011-01-01",
"2011-01-01",
"2011-01-01",
"2012-01-01",
"2012-01-01",
],
"metric": [
"false positives@",
"false positives@",
"false positives@",
"false positives@",
"false positives@",
],
"parameter": ["100_abs", "100_abs", "100_abs", "100_abs", "100_abs"],
"raw_value": [40, 50, 55, 60, 70],
"dist_from_best_case": [0, 10, 5, 0, 10],
}
)
assert best_current_value(df, "2011-01-01", "false positives@", "100_abs", n=2) == [
"1",
"2",
]
assert best_current_value(df, "2012-01-01", "false positives@", "100_abs", n=1) == [
"1"
]
def test_best_current_value_greater_is_better():
df = pandas.DataFrame.from_dict({
'model_group_id': ['1', '2', '4', '1', '2', '3'],
'model_id': ['1', '2', '3', '4', '5', '6'],
'train_end_time': [
'2011-01-01', '2012-01-01', '2012-01-01', '2012-01-01',
'2012-01-01', '2012-01-01'
],
'metric': [
'precision@', 'precision@', 'precision@', 'precision@',
'precision@', 'precision@'
],
'parameter':
['100_abs', '100_abs', '100_abs', '100_abs', '100_abs', '100_abs'],
'raw_value': [0.5, 0.4, 0.4, 0.6, 0.8, 0.7],
'dist_from_best_case': [0.0, 0.1, 0.1, 0.1, 0.0, 0.0],
})
assert best_current_value(df, '2012-01-01', 'precision@', '100_abs',
n=2) == ['2', '3']
assert best_current_value(df, '2011-01-01', 'precision@', '100_abs',
n=2) == ['1']
assert best_current_value(df, '2011-01-01', 'precision@', '100_abs',
n=1) == ['1']
assert best_current_value(df, '2012-01-01', 'precision@',
'100_abs') == ['2']
def test_best_current_value_greater_is_better():
df = pandas.DataFrame.from_dict({
"model_group_id": ["1", "2", "4", "1", "2", "3"],
"model_id": ["1", "2", "3", "4", "5", "6"],
"train_end_time": [
"2011-01-01",
"2012-01-01",
"2012-01-01",
"2012-01-01",
"2012-01-01",
"2012-01-01",
],
"metric": [
"precision@",
"precision@",
"precision@",
"precision@",
"precision@",
"precision@",
],
"parameter": [
"100_abs",
"100_abs",
"100_abs",
"100_abs",
"100_abs",
"100_abs",
],
"raw_value": [0.5, 0.4, 0.4, 0.6, 0.8, 0.7],
"dist_from_best_case": [0.0, 0.1, 0.1, 0.1, 0.0, 0.0],
})
assert best_current_value(df, "2012-01-01", "precision@", "100_abs",
n=2) == [
"2",
"3",
]
assert best_current_value(df, "2011-01-01", "precision@", "100_abs",
n=2) == ["1"]
assert best_current_value(df, "2011-01-01", "precision@", "100_abs",
n=1) == ["1"]
assert best_current_value(df, "2012-01-01", "precision@",
"100_abs") == ["2"]