def test_ls_good():
y_pred, y_trues = zip(*((s['prediction'], l) for s, l in GOOD))
sps = ScaledPredictionStatistics(y_pred, y_trues, population_rate=0.05)
assert ((sps.tp, sps.fp, sps.tn, sps.fn) ==
(9.5, 9.5, 180.5, 0.5))
print(sps.format_str({}))
assert sps['match_rate'] == 19 / 200
assert sps['filter_rate'] == 181 / 200
assert sps['precision'] == 9.5 / 19
assert sps['!precision'] == 180.5 / 181
assert sps['recall'] == 95 / 100
assert sps['!recall'] == 95 / 100
assert sps['accuracy'] == 190 / 200
assert sps['fpr'] == 9.5 / 190
assert sps.n == 200
assert sps.tp == 9.5
assert (sps['f1'] ==
2 * (((9.5 / 19) * (95 / 100)) /
((9.5 / 19) + (95 / 100))))
assert (sps['!f1'] ==
2 * (((180.5 / 181) * (95 / 100)) /
((180.5 / 181) + (95 / 100))))
assert (sps.format_json({'precision': {}, 'recall': {}}) ==
{'precision': 9.5 / 19, 'recall': 95 / 100})
def test_ls_good():
y_pred, y_trues = zip(*((s['prediction'], l) for s, l in GOOD))
sps = ScaledPredictionStatistics(y_pred, y_trues, population_rate=0.05)
assert ((sps.tp, sps.fp, sps.tn, sps.fn) ==
(9.5, 9.5, 180.5, 0.5))
print(sps.format_str({}))
assert sps['match_rate'] == 19 / 200
assert sps['filter_rate'] == 181 / 200
assert sps['precision'] == 9.5 / 19
assert sps['!precision'] == 180.5 / 181
assert sps['recall'] == 95 / 100
assert sps['!recall'] == 95 / 100
assert sps['accuracy'] == 190 / 200
assert sps['fpr'] == 9.5 / 190
assert sps.n == 200
assert sps.tp == 9.5
assert (sps['f1'] ==
2 * (((9.5 / 19) * (95 / 100)) /
((9.5 / 19) + (95 / 100))))
assert (sps['!f1'] ==
2 * (((180.5 / 181) * (95 / 100)) /
((180.5 / 181) + (95 / 100))))
assert (sps.format_json({'precision': {}, 'recall': {}}) ==
{'precision': 9.5 / 19, 'recall': 95 / 100})
def test_ls_perfect():
y_pred, y_trues = zip(*((s['prediction'], l) for s, l in PERFECT))
sps = ScaledPredictionStatistics(y_pred, y_trues, population_rate=0.05)
print(sps.format_str({}))
assert ((sps.tp, sps.fp, sps.tn, sps.fn) ==
(10, 0, 190, 0))
assert sps['match_rate'] == 10 / 200
assert sps['filter_rate'] == 190 / 200
assert sps['precision'] == 1
assert sps['!precision'] == 1
assert sps['recall'] == 1
assert sps['!recall'] == 1
assert sps['accuracy'] == 1
assert sps['fpr'] == 0
assert sps['f1'] == 1
assert sps['!f1'] == 1
assert (sps.format_json({'precision': {}, 'recall': {}}) ==
{'precision': 1, 'recall': 1})
def test_ls_the_worst():
y_pred, y_trues = zip(*((s['prediction'], l) for s, l in WORST))
sps = ScaledPredictionStatistics(y_pred, y_trues, population_rate=0.05)
print(sps.format_str({}))
assert ((sps.tp, sps.fp, sps.tn, sps.fn) ==
(0, 190, 0, 10))
assert sps['match_rate'] == 190 / 200
assert sps['filter_rate'] == 1 - (190 / 200)
assert sps['precision'] == 0
assert sps['!precision'] == 0
assert sps['recall'] == 0
assert sps['!recall'] == 0
assert sps['accuracy'] == 0
assert sps['fpr'] == 1
assert sps['f1'] is None
assert sps['!f1'] is None
assert (sps.format_json({'precision': {}, 'recall': {}}) ==
{'precision': 0, 'recall': 0})
def test_ls_perfect():
y_pred, y_trues = zip(*((s['prediction'], l) for s, l in PERFECT))
sps = ScaledPredictionStatistics(y_pred, y_trues, population_rate=0.05)
print(sps.format_str({}))
assert ((sps.tp, sps.fp, sps.tn, sps.fn) ==
(10, 0, 190, 0))
assert sps['match_rate'] == 10 / 200
assert sps['filter_rate'] == 190 / 200
assert sps['precision'] == 1
assert sps['!precision'] == 1
assert sps['recall'] == 1
assert sps['!recall'] == 1
assert sps['accuracy'] == 1
assert sps['fpr'] == 0
assert sps['f1'] == 1
assert sps['!f1'] == 1
assert (sps.format_json({'precision': {}, 'recall': {}}) ==
{'precision': 1, 'recall': 1})
def test_ls_the_worst():
y_pred, y_trues = zip(*((s['prediction'], l) for s, l in WORST))
sps = ScaledPredictionStatistics(y_pred, y_trues, population_rate=0.05)
print(sps.format_str({}))
assert ((sps.tp, sps.fp, sps.tn, sps.fn) ==
(0, 190, 0, 10))
assert sps['match_rate'] == 190 / 200
assert sps['filter_rate'] == 1 - (190 / 200)
assert sps['precision'] == 0
assert sps['!precision'] == 0
assert sps['recall'] == 0
assert sps['!recall'] == 0
assert sps['accuracy'] == 0
assert sps['fpr'] == 1
assert sps['f1'] is None
assert sps['!f1'] is None
assert (sps.format_json({'precision': {}, 'recall': {}}) ==
{'precision': 0, 'recall': 0})
def test_ls_balanced():
y_pred, y_trues = zip(*((s['prediction'], l) for s, l in BALANCED))
sps = ScaledPredictionStatistics(y_pred, y_trues)
print(sps.format_str({}))
assert ((sps.tp, sps.fp, sps.tn, sps.fn) ==
(50, 50, 50, 50))
assert sps['match_rate'] == 100 / 200
assert sps['filter_rate'] == 100 / 200
assert sps['precision'] == 50 / 100
assert sps['!precision'] == 50 / 100
assert sps['recall'] == 50 / 100
assert sps['!recall'] == 50 / 100
assert sps['accuracy'] == 100 / 200
assert sps['fpr'] == 50 / 100
assert (sps['f1'] ==
2 * (((1 / 2) * (1 / 2)) /
((1 / 2) + (1 / 2))))
assert (sps['!f1'] ==
2 * (((1 / 2) * (1 / 2)) /
((1 / 2) + (1 / 2))))
assert (sps.format_json({'precision': {}, 'recall': {}}) ==
{'precision': 50 / 100, 'recall': 50 / 100})
def test_ls_balanced():
y_pred, y_trues = zip(*((s['prediction'], l) for s, l in BALANCED))
sps = ScaledPredictionStatistics(y_pred, y_trues)
print(sps.format_str({}))
assert ((sps.tp, sps.fp, sps.tn, sps.fn) ==
(50, 50, 50, 50))
assert sps['match_rate'] == 100 / 200
assert sps['filter_rate'] == 100 / 200
assert sps['precision'] == 50 / 100
assert sps['!precision'] == 50 / 100
assert sps['recall'] == 50 / 100
assert sps['!recall'] == 50 / 100
assert sps['accuracy'] == 100 / 200
assert sps['fpr'] == 50 / 100
assert (sps['f1'] ==
2 * (((1 / 2) * (1 / 2)) /
((1 / 2) + (1 / 2))))
assert (sps['!f1'] ==
2 * (((1 / 2) * (1 / 2)) /
((1 / 2) + (1 / 2))))
assert (sps.format_json({'precision': {}, 'recall': {}}) ==
{'precision': 50 / 100, 'recall': 50 / 100})
def test_ls_balanced_scaled():
# Balanced, scaled
y_pred, y_trues = zip(*((s['prediction'], l) for s, l in BALANCED))
sps = ScaledPredictionStatistics(y_pred, y_trues, population_rate=0.05)
print(sps.format_str({}))
assert ((sps.tp, sps.fp, sps.tn, sps.fn) ==
(5, 95, 95, 5))
assert sps['match_rate'] == 100 / 200
assert sps['filter_rate'] == 100 / 200
assert sps['precision'] == 5 / 100
assert sps['!precision'] == 95 / 100
assert sps['recall'] == 5 / 10
assert sps['!recall'] == 95 / 190
assert sps['accuracy'] == 100 / 200
assert sps['fpr'] == 5 / 10
assert (sps['f1'] ==
2 * (((5 / 10) * (5 / 100)) /
((5 / 10) + (5 / 100))))
assert (sps['!f1'] ==
2 * (((95 / 190) * (95 / 100)) /
((95 / 190) + (95 / 100))))
assert (sps.format_json({'precision': {}, 'recall': {}}) ==
{'precision': 5 / 100, 'recall': 5 / 10})
def test_ls_balanced_scaled():
# Balanced, scaled
y_pred, y_trues = zip(*((s['prediction'], l) for s, l in BALANCED))
sps = ScaledPredictionStatistics(y_pred, y_trues, population_rate=0.05)
print(sps.format_str({}))
assert ((sps.tp, sps.fp, sps.tn, sps.fn) ==
(5, 95, 95, 5))
assert sps['match_rate'] == 100 / 200
assert sps['filter_rate'] == 100 / 200
assert sps['precision'] == 5 / 100
assert sps['!precision'] == 95 / 100
assert sps['recall'] == 5 / 10
assert sps['!recall'] == 95 / 190
assert sps['accuracy'] == 100 / 200
assert sps['fpr'] == 5 / 10
assert (sps['f1'] ==
2 * (((5 / 10) * (5 / 100)) /
((5 / 10) + (5 / 100))))
assert (sps['!f1'] ==
2 * (((95 / 190) * (95 / 100)) /
((95 / 190) + (95 / 100))))
assert (sps.format_json({'precision': {}, 'recall': {}}) ==
{'precision': 5 / 100, 'recall': 5 / 10})