MetricsReloaded/test/test_processes/test_mixed_measures_processes.py at a805dd9698d956815c8caee55cd1983c25b47764 · Project-MONAI/MetricsReloaded · GitHub

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#import pytest
from MetricsReloaded.metrics.pairwise_measures import BinaryPairwiseMeasures as PM
from MetricsReloaded.metrics.pairwise_measures import MultiClassPairwiseMeasures as MPM
from MetricsReloaded.processes.mixed_measures_processes import (
    MultiLabelLocSegPairwiseMeasure as MLIS, MultiLabelPairwiseMeasures as MLPM,
)
import numpy as np
from numpy.testing import assert_allclose
from sklearn.metrics import cohen_kappa_score as cks
from sklearn.metrics import matthews_corrcoef as mcc

# Data for panoptic quality Figure 3.51 p96
pq_pred1 = np.zeros([18, 18])
pq_pred1[ 3:7,1:3] = 1
pq_pred1[3:6,3:7]=1
pq_pred2 = np.zeros([18, 18])
pq_pred2[13:16,4:6] = 1
pq_pred3 = np.zeros([18, 18])
pq_pred3[7:12,13:17] = 1
pq_pred4 = np.zeros([18, 18])
pq_pred4[13:15,13:17] = 1
pq_pred4[15,15] = 1

pq_ref1 = np.zeros([18, 18])
pq_ref1[2:7, 1:3] = 1
pq_ref1[2:5,3:6] = 1
pq_ref2 = np.zeros([18, 18])
pq_ref2[6:12,12:17] = 1
pq_ref3 = np.zeros([18, 18])
pq_ref3[14:15:,7:10] = 1
pq_ref3[13:16,8:9] = 1


def test_mismatch_category():
    ref = [pq_ref1, pq_ref2, pq_ref3]
    pred = [pq_pred1, pq_pred2, pq_pred3, pq_pred4]
    mlis = MLIS(
        [[0, 0, 0, 0]],
        ref_class=[[0, 1, 1]],
        pred_loc=[pred],
        ref_loc=[ref],
        pred_prob=[np.asarray([[1,0], [1,0],[1,0],[1,0]])],
        list_values=[0, 1],
        localization="mask_iou",
        measures_detseg=["PQ"],
        measures_pcc=["fbeta"],
    )
    value_tmp1, value_tmp2, value_tmp3 = mlis.per_label_dict()
    value_test = np.asarray(value_tmp2[value_tmp2["label"] == 1]["PQ"])[0]

    assert value_test == 0


def test_panoptic_quality():
    ref = [pq_ref1, pq_ref2, pq_ref3]
    pred = [pq_pred1, pq_pred2, pq_pred3, pq_pred4]
    mlis = MLIS(
        [[1, 1, 1, 1]],
        ref_class=[[1, 1, 1]],
        pred_loc=[pred],
        ref_loc=[ref],
        pred_prob=[np.asarray([[0,1], [0,1], [0,1], [0,1]])],
        list_values=[1],
        localization="mask_iou",
        measures_detseg=["PQ"],
    )
    _, value_tmp, _ = mlis.per_label_dict()
    print(value_tmp, ' is mlis per label in PQ')
    value_test = np.asarray(value_tmp[value_tmp["label"] == 1]["PQ"])[0]
    print("PQ ", value_test)
    expected_pq = 0.350
    assert_allclose(value_test, expected_pq, atol=0.001)

def test_image_level_classification():
    pred = [[1,1]]
    ref = [[1,0]]
    pred_proba= [[[0.2,0.8],[0.4,0.6]]]
    mlpm = MLPM(pred, ref, pred_proba,[1],measures_pcc=['fbeta'], measures_calibration=['ls'])
    df_pcc, df_mt = mlpm.per_label_dict()
    df_mcc, df_cal = mlpm.multi_label_res()
    print(float(np.asarray(df_cal['ls'])[0]))
    value_test = float(np.asarray(df_cal['ls'])[0])
    assert_allclose(value_test, -0.57, atol=0.01)