def test_lift(self):
y_true = np.array([1, 1, 0, 0, 0, 1, 1, 0, 0, 1])
y_predict = np.array([0.57, 0.70, 0.25, 0.30, 0.46, 0.62, 0.76, 0.46, 0.35, 0.56])
dict_score = {"0": {0: 0, 1: 1}, "0.4": {0: 2, 1: 1.43}, "0.6": {0: 1.43, 1: 2}}
eva = Evaluation("binary")
split_thresholds = [0, 0.4, 0.6]
lifts = eva.lift(y_true, y_predict, thresholds=split_thresholds)
fix_lifts = []
for lift in lifts:
fix_lift = [round(pos, 2) for pos in lift]
fix_lifts.append(fix_lift)
for i in range(len(split_thresholds)):
score_0 = dict_score[str(split_thresholds[i])][0]
score_1 = dict_score[str(split_thresholds[i])][1]
pos_lift = fix_lifts[i]
self.assertEqual(len(pos_lift), 2)
self.assertFloatEqual(score_0, pos_lift[0])
self.assertFloatEqual(score_1, pos_lift[1])
def test_lift(self):
y_true = np.array([1, 1, 0, 0, 0, 1, 1, 0, 0, 1])
y_predict = np.array(
[0.57, 0.70, 0.25, 0.30, 0.46, 0.62, 0.76, 0.46, 0.35, 0.56])
lift_y_true = [[1.0, 2.0], [1.0, 2.0], [1.0, 2.0], [1.0, 2.0],
[1.0, 2.0], [1.0, 2.0], [1.0, 2.0], [1.0, 2.0],
[1.0, 2.0], [1.0, 2.0], [1.0, 2.0], [1.0, 2.0],
[1.1111111111111112, 2.0], [1.1111111111111112, 2.0],
[1.1111111111111112, 2.0], [1.1111111111111112, 2.0],
[1.1111111111111112, 2.0], [1.1111111111111112, 2.0],
[1.1111111111111112, 2.0], [1.1111111111111112, 2.0],
[1.1111111111111112, 2.0], [1.1111111111111112, 2.0],
[1.1111111111111112, 2.0], [1.25, 2.0], [1.25, 2.0],
[1.25, 2.0], [1.25, 2.0], [1.25, 2.0], [1.25, 2.0],
[1.25, 2.0], [1.25, 2.0], [1.25, 2.0], [1.25, 2.0],
[1.25, 2.0], [1.4285714285714286, 2.0],
[1.4285714285714286, 2.0], [1.4285714285714286, 2.0],
[1.4285714285714286, 2.0], [1.4285714285714286, 2.0],
[1.4285714285714286, 2.0], [1.4285714285714286, 2.0],
[1.4285714285714286, 2.0], [1.4285714285714286, 2.0],
[1.4285714285714286, 2.0], [1.4285714285714286, 2.0],
[1.6666666666666667, 2.0], [1.6666666666666667, 2.0],
[1.6666666666666667, 2.0], [1.6666666666666667, 2.0],
[1.6666666666666667, 2.0], [1.6666666666666667, 2.0],
[1.6666666666666667, 2.0], [1.6666666666666667, 2.0],
[1.6666666666666667, 2.0], [1.6666666666666667, 2.0],
[1.6666666666666667, 2.0], [2.0, 2.0], [2.0, 2.0],
[2.0, 2.0], [2.0, 2.0], [2.0, 2.0], [2.0, 2.0],
[2.0, 2.0], [2.0, 2.0], [2.0, 2.0], [2.0, 2.0],
[2.0, 2.0], [2.0, 1.4285714285714286],
[2.0, 1.4285714285714286], [2.0, 1.4285714285714286],
[2.0, 1.4285714285714286], [2.0, 1.4285714285714286],
[2.0, 1.4285714285714286], [2.0, 1.4285714285714286],
[2.0, 1.4285714285714286], [2.0, 1.4285714285714286],
[2.0, 1.4285714285714286], [2.0, 1.4285714285714286],
[2.0, 1.25], [2.0, 1.25], [2.0, 1.25], [2.0, 1.25],
[2.0, 1.25], [2.0, 1.25], [2.0, 1.25], [2.0, 1.25],
[2.0, 1.25], [2.0, 1.25], [2.0, 1.25],
[2.0, 1.1111111111111112], [2.0, 1.1111111111111112],
[2.0, 1.1111111111111112], [2.0, 1.1111111111111112],
[2.0, 1.1111111111111112], [2.0, 1.1111111111111112],
[2.0, 1.1111111111111112], [2.0, 1.1111111111111112],
[2.0, 1.1111111111111112], [2.0, 1.1111111111111112],
[2.0, 1.1111111111111112]]
lift_x_true = [[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0],
[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0],
[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0],
[0.9, 0.1], [0.9, 0.1], [0.9, 0.1], [0.9, 0.1],
[0.9, 0.1], [0.9, 0.1], [0.9, 0.1], [0.9, 0.1],
[0.9, 0.1], [0.9, 0.1], [0.9, 0.1], [0.8, 0.2],
[0.8, 0.2], [0.8, 0.2], [0.8, 0.2], [0.8, 0.2],
[0.8, 0.2], [0.8, 0.2], [0.8, 0.2], [0.8, 0.2],
[0.8, 0.2], [0.8, 0.2], [0.7, 0.3], [0.7, 0.3],
[0.7, 0.3], [0.7, 0.3], [0.7, 0.3], [0.7, 0.3],
[0.7, 0.3], [0.7, 0.3], [0.7, 0.3], [0.7, 0.3],
[0.7, 0.3], [0.6, 0.4], [0.6, 0.4], [0.6, 0.4],
[0.6, 0.4], [0.6, 0.4], [0.6, 0.4], [0.6, 0.4],
[0.6, 0.4], [0.6, 0.4], [0.6, 0.4], [0.6, 0.4],
[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5],
[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5],
[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.3, 0.7],
[0.3, 0.7], [0.3, 0.7], [0.3, 0.7], [0.3, 0.7],
[0.3, 0.7], [0.3, 0.7], [0.3, 0.7], [0.3, 0.7],
[0.3, 0.7], [0.3, 0.7], [0.2, 0.8], [0.2, 0.8],
[0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8],
[0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8],
[0.2, 0.8], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9],
[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9],
[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9]]
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.BINARY, pos_label=1))
lift_y, lift_x, thresholds = eva.lift(y_true, y_predict)
self.assertListEqual(lift_y, lift_y_true)
self.assertListEqual(lift_x, lift_x_true)