def __init__(self):
super().__init__()
self.model_param = EvaluateParam()
self.eval_results = defaultdict(list)
self.save_single_value_metric_list = [
consts.AUC, consts.EXPLAINED_VARIANCE, consts.MEAN_ABSOLUTE_ERROR,
consts.MEAN_SQUARED_ERROR, consts.MEAN_SQUARED_LOG_ERROR,
consts.MEDIAN_ABSOLUTE_ERROR, consts.R2_SCORE,
consts.ROOT_MEAN_SQUARED_ERROR
]
self.save_curve_metric_list = [
consts.KS, consts.ROC, consts.LIFT, consts.GAIN, consts.PRECISION,
consts.RECALL, consts.ACCURACY
]
self.special_metric_list = [consts.PSI]
self.metrics = None
self.round_num = 6
self.validate_metric = {}
self.train_metric = {}
# where to call metric computations
self.metric_interface: MetricInterface = None
self.psi_train_scores, self.psi_validate_scores = None, None
self.psi_train_labels, self.psi_validate_labels = None, None
def test_precision(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])
true_score = [[0.5, 1.0], [0.5, 1.0], [0.5, 1.0], [0.5,
1.0], [0.5, 1.0],
[0.5, 1.0], [0.5, 1.0], [0.5, 1.0], [0.5, 1.0],
[0.5, 1.0], [0.5, 1.0], [0.5, 1.0],
[0.5555555555555556, 1.0], [0.5555555555555556, 1.0],
[0.5555555555555556, 1.0], [0.5555555555555556, 1.0],
[0.5555555555555556, 1.0], [0.5555555555555556, 1.0],
[0.5555555555555556, 1.0], [0.5555555555555556, 1.0],
[0.5555555555555556, 1.0], [0.5555555555555556, 1.0],
[0.5555555555555556, 1.0], [0.625, 1.0], [0.625, 1.0],
[0.625, 1.0], [0.625, 1.0], [0.625, 1.0], [0.625, 1.0],
[0.625, 1.0], [0.625, 1.0], [0.625, 1.0], [0.625, 1.0],
[0.625, 1.0], [0.7142857142857143, 1.0],
[0.7142857142857143, 1.0], [0.7142857142857143, 1.0],
[0.7142857142857143, 1.0], [0.7142857142857143, 1.0],
[0.7142857142857143, 1.0], [0.7142857142857143, 1.0],
[0.7142857142857143, 1.0], [0.7142857142857143, 1.0],
[0.7142857142857143, 1.0], [0.7142857142857143, 1.0],
[0.8333333333333334, 1.0], [0.8333333333333334, 1.0],
[0.8333333333333334, 1.0], [0.8333333333333334, 1.0],
[0.8333333333333334, 1.0], [0.8333333333333334, 1.0],
[0.8333333333333334, 1.0], [0.8333333333333334, 1.0],
[0.8333333333333334, 1.0], [0.8333333333333334, 1.0],
[0.8333333333333334, 1.0], [1.0, 1.0], [1.0, 1.0],
[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0,
1.0], [1.0, 1.0],
[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0],
[1.0, 0.7142857142857143], [1.0, 0.7142857142857143],
[1.0, 0.7142857142857143], [1.0, 0.7142857142857143],
[1.0, 0.7142857142857143], [1.0, 0.7142857142857143],
[1.0, 0.7142857142857143], [1.0, 0.7142857142857143],
[1.0, 0.7142857142857143], [1.0, 0.7142857142857143],
[1.0, 0.7142857142857143], [1.0, 0.625], [1.0, 0.625],
[1.0, 0.625], [1.0, 0.625], [1.0, 0.625], [1.0, 0.625],
[1.0, 0.625], [1.0, 0.625], [1.0, 0.625], [1.0, 0.625],
[1.0, 0.625], [1.0, 0.5555555555555556],
[1.0, 0.5555555555555556], [1.0, 0.5555555555555556],
[1.0, 0.5555555555555556], [1.0, 0.5555555555555556],
[1.0, 0.5555555555555556], [1.0, 0.5555555555555556],
[1.0, 0.5555555555555556], [1.0, 0.5555555555555556],
[1.0, 0.5555555555555556], [1.0, 0.5555555555555556],
[0.0, 0.5]]
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.BINARY, pos_label=1))
rs = eva.precision(y_true, y_predict)
self.assertListEqual(true_score, rs[0])
def test_median_absolute_error(self):
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.REGRESSION,
pos_label=1))
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
self.assertFloatEqual(eva.median_absolute_error(y_true, y_pred), 0.5)
y_true = [3, -0.6, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
self.assertFloatEqual(eva.median_absolute_error(y_true, y_pred), 0.55)
def test_auc(self):
y_true = np.array([0, 0, 1, 1])
y_predict = np.array([0.1, 0.4, 0.35, 0.8])
ground_true_auc = 0.75
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.BINARY, pos_label=1))
auc = eva.auc(y_true, y_predict)
auc = round(auc, 2)
self.assertFloatEqual(auc, ground_true_auc)
def test_recall(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, 0.31, 0.46, 0.62, 0.76, 0.46, 0.35, 0.56])
true_score = [[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], [1.0, 0.2], [1.0,
0.2], [1.0, 0.2],
[1.0, 0.2], [1.0, 0.2], [1.0, 0.2], [1.0,
0.2], [1.0, 0.2],
[1.0, 0.2], [1.0, 0.2], [1.0, 0.2], [1.0,
0.4], [1.0, 0.4],
[1.0, 0.4], [1.0, 0.4], [1.0, 0.4], [1.0,
0.4], [1.0, 0.4],
[1.0, 0.4], [1.0, 0.4], [1.0, 0.4], [1.0,
0.4], [1.0, 0.6],
[1.0, 0.6], [1.0, 0.6], [1.0, 0.6], [1.0,
0.6], [1.0, 0.6],
[1.0, 0.6], [1.0, 0.6], [1.0, 0.6], [1.0,
0.6], [1.0, 0.6],
[1.0, 0.8], [1.0, 0.8], [1.0, 0.8], [1.0,
0.8], [1.0, 0.8],
[1.0, 0.8], [1.0, 0.8], [1.0, 0.8], [1.0,
0.8], [1.0, 0.8],
[1.0, 0.8], [1.0, 1.0], [1.0, 1.0], [1.0,
1.0], [1.0, 1.0],
[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0,
1.0], [1.0, 1.0],
[1.0, 1.0], [1.0, 1.0], [0.6, 1.0], [0.6,
1.0], [0.6, 1.0],
[0.6, 1.0], [0.6, 1.0], [0.6, 1.0], [0.6,
1.0], [0.6, 1.0],
[0.6, 1.0], [0.6, 1.0], [0.6, 1.0], [0.4,
1.0], [0.4, 1.0],
[0.4, 1.0], [0.4, 1.0], [0.4, 1.0], [0.4,
1.0], [0.4, 1.0],
[0.4, 1.0], [0.4, 1.0], [0.4, 1.0], [0.4,
1.0], [0.2, 1.0],
[0.2, 1.0], [0.2, 1.0], [0.2, 1.0], [0.2,
1.0], [0.2, 1.0],
[0.2, 1.0], [0.2, 1.0], [0.2, 1.0], [0.2, 1.0],
[0.2, 1.0], [0.0, 1.0]]
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.BINARY, pos_label=1))
rs = eva.recall(y_true, y_predict)
self.assertListEqual(rs[0], true_score)
def test_mean_squared_error(self):
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.REGRESSION,
pos_label=1))
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
self.assertFloatEqual(eva.mean_squared_error(y_true, y_pred), 0.375)
y_true = [[0.5, 1], [-1, 1], [7, -6]]
y_pred = [[0, 2], [-1, 2], [8, -5]]
self.assertFloatEqual(
np.around(eva.mean_squared_error(y_true, y_pred), 4), 0.7083)
def test_mean_squared_log_error(self):
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.REGRESSION,
pos_label=1))
y_true = [3, 5, 2.5, 7]
y_pred = [2.5, 5, 4, 8]
self.assertFloatEqual(
np.around(eva.mean_squared_log_error(y_true, y_pred), 4), 0.0397)
y_true = [[0.5, 1], [1, 2], [7, 6]]
y_pred = [[0.5, 2], [1, 2.5], [8, 8]]
self.assertFloatEqual(
np.around(eva.mean_squared_log_error(y_true, y_pred), 4), 0.0442)
def test_explained_variance(self):
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.REGRESSION,
pos_label=1))
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
self.assertFloatEqual(
np.around(eva.explained_variance(y_true, y_pred), 4), 0.9572)
y_true = [[0.5, 1], [-1, 1], [7, -6]]
y_pred = [[0, 2], [-1, 2], [8, -5]]
self.assertFloatEqual(
np.around(eva.explained_variance(y_true, y_pred), 4), 0.9839)
def test_multi_accuracy(self):
y_true = np.array(
[1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4])
y_predict = [
1, 1, 2, 2, 3, 2, 1, 1, 1, 1, 3, 3, 3, 3, 2, 4, 4, 4, 4, 4
]
gt_score = 0.6
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.MULTY, pos_label=1))
acc = eva.accuracy(y_true, y_predict)
self.assertFloatEqual(acc, gt_score)
def test_ks(self):
y_true = np.array(
[1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0])
y_predict = np.array([
0.42, 0.73, 0.55, 0.37, 0.57, 0.70, 0.25, 0.23, 0.46, 0.62, 0.76,
0.46, 0.55, 0.56, 0.56, 0.38, 0.37, 0.73, 0.77, 0.21, 0.39
])
ground_true_ks = 0.75
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.BINARY, pos_label=1))
ks, fpr, tpr, score_threshold, cuts = eva.ks(y_true, y_predict)
ks = round(ks, 2)
self.assertFloatEqual(ks, ground_true_ks)
def test_multi_precision(self):
y_true = np.array([
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5,
5, 5, 5
])
y_predict = np.array([
1, 1, 2, 2, 3, 2, 1, 1, 1, 1, 3, 3, 3, 3, 2, 4, 4, 4, 4, 4, 6, 6,
6, 6, 6
])
gt_score = [0.33333333, 0.25, 0.8, 1., 0., 0.]
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.MULTY, pos_label=1))
precision_scores, _ = eva.precision(y_true, y_predict)
for a, b in zip(precision_scores, gt_score):
assert round(a, 2) == round(b, 2)
def test_multi_recall(self):
y_true = np.array([
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5,
5, 5, 5
])
y_predict = np.array([
1, 1, 2, 2, 3, 2, 1, 1, 1, 1, 3, 3, 3, 3, 2, 4, 4, 4, 4, 4, 6, 6,
6, 6, 6
])
gt_score = [0.4, 0.2, 0.8, 1, 0, 0]
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.MULTY, pos_label=1))
recall_scores, _ = eva.recall(y_true, y_predict)
for a, b in zip(recall_scores, gt_score):
assert round(a, 2) == round(b, 2)
def __init__(self):
super().__init__()
self.model_param = EvaluateParam()
self.eval_results = defaultdict(list)
self.save_single_value_metric_list = [
consts.AUC, consts.EXPLAINED_VARIANCE, consts.MEAN_ABSOLUTE_ERROR,
consts.MEAN_SQUARED_ERROR, consts.MEAN_SQUARED_LOG_ERROR,
consts.MEDIAN_ABSOLUTE_ERROR, consts.R2_SCORE,
consts.ROOT_MEAN_SQUARED_ERROR
]
self.save_curve_metric_list = [
consts.KS, consts.ROC, consts.LIFT, consts.GAIN, consts.PRECISION,
consts.RECALL, consts.ACCURACY
]
self.metrics = None
self.round_num = 6
self.validate_metric = {}
self.train_metric = {}
def __init__(self):
super().__init__()
self.model_param = EvaluateParam()
self.eval_results = defaultdict(list)
self.save_single_value_metric_list = [
consts.AUC, consts.EXPLAINED_VARIANCE, consts.MEAN_ABSOLUTE_ERROR,
consts.MEAN_SQUARED_ERROR, consts.MEAN_SQUARED_LOG_ERROR,
consts.MEDIAN_ABSOLUTE_ERROR, consts.R2_SCORE,
consts.ROOT_MEAN_SQUARED_ERROR
]
self.save_curve_metric_list = [
consts.KS, consts.ROC, consts.LIFT, consts.GAIN, consts.PRECISION,
consts.RECALL, consts.ACCURACY
]
self.regression_support_func = [
consts.EXPLAINED_VARIANCE, consts.MEAN_ABSOLUTE_ERROR,
consts.MEAN_SQUARED_ERROR, consts.MEAN_SQUARED_LOG_ERROR,
consts.MEDIAN_ABSOLUTE_ERROR, consts.R2_SCORE,
consts.ROOT_MEAN_SQUARED_ERROR
]
self.binary_classification_support_func = [
consts.AUC, consts.KS, consts.LIFT, consts.GAIN, consts.ACCURACY,
consts.PRECISION, consts.RECALL, consts.ROC
]
self.multi_classification_support_func = [
consts.ACCURACY, consts.PRECISION, consts.RECALL
]
self.metrics = {
consts.BINARY: self.binary_classification_support_func,
consts.MULTY: self.multi_classification_support_func,
consts.REGRESSION: self.regression_support_func
}
self.round_num = 6
def test_bin_accuracy(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, 0.31, 0.46, 0.62, 0.76, 0.46, 0.35, 0.56])
gt_score = [
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.6,
0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.7, 0.7, 0.7,
0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9,
0.9, 0.9, 0.9, 0.9, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.6, 0.6,
0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6
]
eva = Evaluation()
eva._init_model(EvaluateParam(eval_type=consts.BINARY, pos_label=1))
acc, cuts, thresholds = eva.accuracy(y_true, y_predict)
self.assertListEqual(acc, gt_score)
def __init__(self):
super().__init__()
self.model_param = EvaluateParam()
self.eval_results = defaultdict(list)
self.save_single_value_metric_list = [
consts.AUC, consts.EXPLAINED_VARIANCE, consts.MEAN_ABSOLUTE_ERROR,
consts.MEAN_SQUARED_ERROR, consts.MEAN_SQUARED_LOG_ERROR,
consts.MEDIAN_ABSOLUTE_ERROR, consts.R2_SCORE,
consts.ROOT_MEAN_SQUARED_ERROR, consts.JACCARD_SIMILARITY_SCORE,
consts.ADJUSTED_RAND_SCORE, consts.FOWLKES_MALLOWS_SCORE,
consts.DAVIES_BOULDIN_INDEX
]
self.special_metric_list = [consts.PSI]
self.clustering_intra_metric_list = [
consts.DAVIES_BOULDIN_INDEX, consts.DISTANCE_MEASURE
]
self.metrics = None
self.round_num = 6
self.eval_type = None
# where to call metric computations
self.metric_interface: MetricInterface = None
self.psi_train_scores, self.psi_validate_scores = None, None
self.psi_train_labels, self.psi_validate_labels = None, None
# multi unfold setting
self.need_unfold_multi_result = False
# summaries
self.metric_summaries = {}
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)