def test_gen_binary_roc_curve():
y = [0, 1, 0, 1, 0, 1, 0, 1, 1, 0]
y_prob = [0.1, 0.9, 0.8, 0.2, 0.7, 0.8, 0.3, 0.6, 0.65, 0.4]
results = _gen_classifier_curve(is_binomial=True,
y=y,
y_probs=y_prob,
labels=[0, 1],
curve_type="roc")
assert np.allclose(
results.plot_fn_args["data_series"][0][1],
np.array([0.0, 0.0, 0.2, 0.4, 0.4, 0.8, 0.8, 1.0]),
rtol=1e-3,
)
assert np.allclose(
results.plot_fn_args["data_series"][0][2],
np.array([0.0, 0.2, 0.4, 0.4, 0.8, 0.8, 1.0, 1.0]),
rtol=1e-3,
)
assert results.plot_fn_args["xlabel"] == "False Positive Rate"
assert results.plot_fn_args["ylabel"] == "True Positive Rate"
assert results.plot_fn_args["line_kwargs"] == {
"drawstyle": "steps-post",
"linewidth": 1
}
assert np.isclose(results.auc, 0.66, rtol=1e-3)
def test_gen_multiclass_roc_curve():
y = [0, 1, 2, 1, 2]
y_probs = [
[0.7, 0.1, 0.2],
[0.2, 0.3, 0.5],
[0.25, 0.4, 0.35],
[0.3, 0.4, 0.3],
[0.8, 0.1, 0.1],
]
results = _gen_classifier_curve(
is_binomial=False, y=y, y_probs=y_probs, labels=[0, 1, 2], curve_type="roc"
)
print(results)
expected_x_data_list = [
[0.0, 0.25, 0.25, 1.0],
[0.0, 0.33333333, 0.33333333, 1.0],
[0.0, 0.33333333, 0.33333333, 1.0, 1.0],
]
expected_y_data_list = [[0.0, 0.0, 1.0, 1.0], [0.0, 0.5, 1.0, 1.0], [0.0, 0.0, 0.5, 0.5, 1.0]]
line_labels = ["label=0,AUC=0.750", "label=1,AUC=0.750", "label=2,AUC=0.333"]
for index, (name, x_data, y_data) in enumerate(results.plot_fn_args["data_series"]):
assert name == line_labels[index]
assert np.allclose(x_data, expected_x_data_list[index], rtol=1e-3)
assert np.allclose(y_data, expected_y_data_list[index], rtol=1e-3)
assert results.plot_fn_args["xlabel"] == "False Positive Rate"
assert results.plot_fn_args["ylabel"] == "True Positive Rate"
assert results.plot_fn_args["line_kwargs"] == {"drawstyle": "steps-post", "linewidth": 1}
expected_auc = [0.75, 0.75, 0.3333]
assert np.allclose(results.auc, expected_auc, rtol=1e-3)
def test_gen_binary_precision_recall_curve():
y = [0, 1, 0, 1, 0, 1, 0, 1, 1, 0]
y_prob = [0.1, 0.9, 0.8, 0.2, 0.7, 0.8, 0.3, 0.6, 0.65, 0.4]
results = _gen_classifier_curve(is_binomial=True,
y=y,
y_probs=y_prob,
labels=[0, 1],
curve_type="pr")
assert np.allclose(
results.plot_fn_args["data_series"][0][1],
np.array([1.0, 0.8, 0.8, 0.8, 0.6, 0.4, 0.4, 0.2, 0.0]),
rtol=1e-3,
)
assert np.allclose(
results.plot_fn_args["data_series"][0][2],
np.array([
0.55555556, 0.5, 0.57142857, 0.66666667, 0.6, 0.5, 0.66666667, 1.0,
1.0
]),
rtol=1e-3,
)
assert results.plot_fn_args["xlabel"] == "recall"
assert results.plot_fn_args["ylabel"] == "precision"
assert results.plot_fn_args["line_kwargs"] == {
"drawstyle": "steps-post",
"linewidth": 1
}
assert np.isclose(results.auc, 0.7088888888888889, rtol=1e-3)
def test_gen_multiclass_precision_recall_curve():
y = [0, 1, 2, 1, 2]
y_probs = [
[0.7, 0.1, 0.2],
[0.2, 0.3, 0.5],
[0.25, 0.4, 0.35],
[0.3, 0.4, 0.3],
[0.8, 0.1, 0.1],
]
results = _gen_classifier_curve(
is_binomial=False, y=y, y_probs=y_probs, labels=[0, 1, 2], curve_type="pr"
)
expected_x_data_list = [[1.0, 0.0, 0.0], [1.0, 0.5, 0.0], [1.0, 0.5, 0.5, 0.5, 0.0, 0.0]]
expected_y_data_list = [
[0.5, 0.0, 1.0],
[0.66666667, 0.5, 1.0],
[0.4, 0.25, 0.33333333, 0.5, 0.0, 1.0],
]
line_labels = ["label=0,AP=0.500", "label=1,AP=0.722", "label=2,AP=0.414"]
for index, (name, x_data, y_data) in enumerate(results.plot_fn_args["data_series"]):
assert name == line_labels[index]
assert np.allclose(x_data, expected_x_data_list[index], rtol=1e-3)
assert np.allclose(y_data, expected_y_data_list[index], rtol=1e-3)
assert results.plot_fn_args["xlabel"] == "recall"
assert results.plot_fn_args["ylabel"] == "precision"
assert results.plot_fn_args["line_kwargs"] == {"drawstyle": "steps-post", "linewidth": 1}
expected_auc = [0.25, 0.6666666666666666, 0.2875]
assert np.allclose(results.auc, expected_auc, rtol=1e-3)