def test_compute_precision(self):
file_path = os.path.join(ROOT_DIR, 'datasets/tests/example_keys')
eval_tool = EvaluationTool(legit=0)
load_tool = LoadingTool()
stats = defaultdict(lambda: defaultdict(int))
trues = pd.Series()
preds = pd.Series()
for chunk in load_tool.load_classifications(file_path, ';', True):
chunk_stats = eval_tool.compute_stats(chunk)
trues = trues.append(chunk[0])
preds = preds.append(chunk[1])
for label in chunk_stats:
stats[label]['FP'] += chunk_stats[label]['FP']
stats[label]['FN'] += chunk_stats[label]['FN']
stats[label]['TP'] += chunk_stats[label]['TP']
labels = [1, 2]
prec = [eval_tool.compute_precision(x, stats) for x in labels]
prec_sklearn = list(
precision_score(y_true=trues,
y_pred=preds,
labels=labels,
average=None))
assert prec == prec_sklearn
def test_compute_precision_unbalanced(self):
file_path = os.path.join(ROOT_DIR, 'datasets/tests/example_unbalanced')
eval_tool = EvaluationTool()
load_tool = LoadingTool()
stats = defaultdict(lambda: defaultdict(int))
trues = pd.Series()
preds = pd.Series()
for chunk in load_tool.load_classifications(file_path, ';'):
chunk_stats = eval_tool.compute_stats(chunk)
trues = trues.append(chunk[0])
preds = preds.append(chunk[1])
for label in chunk_stats:
stats[label]['FP'] += chunk_stats[label]['FP']
stats[label]['FN'] += chunk_stats[label]['FN']
stats[label]['TP'] += chunk_stats[label]['TP']
prec = [
eval_tool.compute_precision(x, stats) for x in eval_tool.labels
]
assert np.isnan(prec[4])
def test_get_labels_with_prec_above(self):
file_path = os.path.join(ROOT_DIR, 'datasets/tests/example_keys')
e_tool = EvaluationTool()
l_tool = LoadingTool()
stats = defaultdict(lambda: defaultdict(int))
trues = pd.Series()
preds = pd.Series()
for chunk in l_tool.load_classifications(file_path, ';', True):
chunk_stats = e_tool.compute_stats(chunk)
for label in chunk_stats:
stats[label]['FP'] += chunk_stats[label]['FP']
stats[label]['FN'] += chunk_stats[label]['FN']
stats[label]['TP'] += chunk_stats[label]['TP']
prec = [e_tool.compute_precision(x, stats) for x in e_tool.labels]
threshold = 0.3
precs_above_threshold = e_tool.get_labels_with_prec_above_thres(
threshold, e_tool.labels, stats)
expected = [0, 1]
assert expected == precs_above_threshold