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Merged
sepandhaghighi merged 5 commits into from
May 8, 2026
+37 −36
Merged

update ref short links #639

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21f8f83
update ref short links
alirezazolanvari Apr 21, 2026
7d4a846
relocate shortlinks
alirezazolanvari Apr 21, 2026
eaf2169
add multi-lable link's placeholder
alirezazolanvari Apr 28, 2026
b07642f
remove PR curve ref link
alirezazolanvari May 7, 2026
b845450
remove ref n24 not implemented comment
alirezazolanvari May 7, 2026
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
- `README.md` modified
- Document modified
- Test system modified
- Relocate shortlinks to `pycm.io` domain
## [4.6] - 2026-03-09
### Added
- `PCurve` class
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72 changes: 36 additions & 36 deletions Test/verified_test.py
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Expand Up @@ -8,7 +8,7 @@
>>> ABS_TOL = 1e-12
>>> REL_TOL = 0
>>> assert isclose(NIR_calc({'Class2': 804, 'Class1': 196}, 1000), 0.804, abs_tol=ABS_TOL, rel_tol=REL_TOL) # Verified Case - (Caret package)
>>> cm = ConfusionMatrix([2, 0, 2, 2, 0, 1], [0, 0, 2, 2, 0, 2]) # Verified Case - (https://bit.ly/38nfMha)
>>> cm = ConfusionMatrix([2, 0, 2, 2, 0, 1], [0, 0, 2, 2, 0, 2]) # Verified Case - (https://pycm.io/ref?n=1)
>>> cm.print_matrix()
Predict 0 1 2
Actual
Expand All @@ -19,7 +19,7 @@
2 1 0 2
<BLANKLINE>
<BLANKLINE>
>>> cm = ConfusionMatrix(matrix={0: {0: 3, 1: 1}, 1: {0: 4, 1: 2}}) # Verified Case - (https://bit.ly/2DHQvjn)
>>> cm = ConfusionMatrix(matrix={0: {0: 3, 1: 1}, 1: {0: 4, 1: 2}}) # Verified Case - (https://pycm.io/ref?n=2)
>>> assert isclose(cm.LS[1], 1.1111111111111112, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.LS[0], 1.0714285714285714, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={"Class1": {"Class1": 183, "Class2": 13}, "Class2": {"Class1": 141, "Class2": 663}}) # Verified Case - (Caret package)
Expand Down Expand Up @@ -53,11 +53,11 @@
>>> assert isclose(cm.DP[1], 0.770700985610517, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.Y[1], 0.627145631592811, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.BM[1], 0.627145631592811, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={1: {1: 60, 2: 9, 3: 1, 4: 0, 5: 0, 6: 0}, 2: {1: 23, 2: 48, 3: 0, 4: 2, 5: 2, 6: 1}, 3: {1: 11, 2: 5, 3: 1, 4: 0, 5: 0, 6: 0}, 4: {1: 0, 2: 2, 3: 0, 4: 7, 5: 1, 6: 3}, 5: {1: 2, 2: 1, 3: 0, 4: 0, 5: 4, 6: 2}, 6: {1: 1, 2: 2, 3: 0, 4: 2, 5: 1, 6: 23}}) # Verified Case - (https://bit.ly/2YdvM01)
>>> cm = ConfusionMatrix(matrix={1: {1: 60, 2: 9, 3: 1, 4: 0, 5: 0, 6: 0}, 2: {1: 23, 2: 48, 3: 0, 4: 2, 5: 2, 6: 1}, 3: {1: 11, 2: 5, 3: 1, 4: 0, 5: 0, 6: 0}, 4: {1: 0, 2: 2, 3: 0, 4: 7, 5: 1, 6: 3}, 5: {1: 2, 2: 1, 3: 0, 4: 0, 5: 4, 6: 2}, 6: {1: 1, 2: 2, 3: 0, 4: 2, 5: 1, 6: 23}}) # Verified Case - (https://pycm.io/ref?n=3)
>>> cm.AM[1]
27
>>> assert isclose(cm.BCD[1], 0.0630841121495327, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={1: {1: 9, 2: 3, 3: 0}, 2: {1: 3, 2: 5, 3: 1}, 3: {1: 1, 2: 1, 3: 4}}) # Verified Case -- (https://bit.ly/2r80R9t)
>>> cm = ConfusionMatrix(matrix={1: {1: 9, 2: 3, 3: 0}, 2: {1: 3, 2: 5, 3: 1}, 3: {1: 1, 2: 1, 3: 4}}) # Verified Case -- (https://pycm.io/ref?n=4)
>>> assert isclose(cm.CI95[0], 0.48885185570907297, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.CI95[1], 0.8444814776242603, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.SE, 0.09072184232530289, abs_tol=ABS_TOL, rel_tol=REL_TOL)
Expand Down Expand Up @@ -88,12 +88,12 @@
>>> assert isclose(cm.IBA_alpha(0.5)[1], 0.41800000000000004, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.IBA_alpha(0.1)[1], 0.5016, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.GM[1], 0.722841614740048, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={1: {1: 22, 0: 18}, 0: {1: 2, 0: 14}}) # Verified Case - (https://bit.ly/2LiCZXB)
>>> cm = ConfusionMatrix(matrix={1: {1: 22, 0: 18}, 0: {1: 2, 0: 14}}) # Verified Case - (https://pycm.io/ref?n=5)
>>> assert isclose(cm.C, 0.36170212765957444, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.Chi_Squared, 8.429166666666667, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={0: {0: 42, 1: 7}, 1: {1: 114, 0: 203}}) # Verified Case - (https://bit.ly/2LiCZXB)
>>> cm = ConfusionMatrix(matrix={0: {0: 42, 1: 7}, 1: {1: 114, 0: 203}}) # Verified Case - (https://pycm.io/ref?n=5)
>>> assert isclose(cm.Q[0], 0.5422773393461104, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={0: {0: 27, 1: 10}, 1: {0: 16, 1: 15}}) # Verified Case - (https://bit.ly/2skyjKG)
>>> cm = ConfusionMatrix(matrix={0: {0: 27, 1: 10}, 1: {0: 16, 1: 15}}) # Verified Case - (https://pycm.io/ref?n=6)
>>> assert isclose(cm.Q[0], 0.4336283185840708, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm.QI[0]
'Weak'
Expand Down Expand Up @@ -123,7 +123,7 @@
>>> assert isclose(cm.TI(alpha=2, beta=8)[1], 0.7777777777777778, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.TI(alpha=2, beta=8)[0], 0.8064516129032258, abs_tol=ABS_TOL, rel_tol=REL_TOL)

>>> cm = ConfusionMatrix(matrix={1: {1: 22, 0: 54}, 0: {1: 1, 0: 57}}, transpose=True) # Verified Case -- (https://bit.ly/34KcVfB)
>>> cm = ConfusionMatrix(matrix={1: {1: 22, 0: 54}, 0: {1: 1, 0: 57}}, transpose=True) # Verified Case -- (https://pycm.io/ref?n=7)
Comment thread
sepandhaghighi marked this conversation as resolved.
>>> assert isclose(cm.TPR[1], 0.9565217391304348, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.CI("TPR", 0.05)[1][1][0], 0.8731774862637585, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.CI("TPR", 0.05)[1][1][1], 1.0398659919971112, abs_tol=ABS_TOL, rel_tol=REL_TOL)
Expand Down Expand Up @@ -176,7 +176,7 @@
>>> assert isclose(cm.CI("Overall ACC", binom_method="agresti-coull")[1][0], 0.5048603506825172, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.CI("Overall ACC", binom_method="wilson")[1][1], 0.6692157009292735, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.CI("Overall ACC", binom_method="wilson")[1][0], 0.5048971938717156, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> SE = AUC_SE_calc(0.88915, 279, 527) # Verified Case -- (https://bit.ly/2qblMrE)
>>> SE = AUC_SE_calc(0.88915, 279, 527) # Verified Case -- (https://pycm.io/ref?n=8)
>>> assert isclose(SE, 0.011116012490627622, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(CI_calc(0.88915, SE)[0], 0.8673626155183699, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(CI_calc(0.88915, SE)[1], 0.9109373844816301, abs_tol=ABS_TOL, rel_tol=REL_TOL)
Expand All @@ -188,22 +188,22 @@
>>> cm.POP[1]
3264
>>> assert isclose(cm.NB(w=0.059)[1], 0.022073223039215686, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={1: {1: 3, 2: 0, 3: 1}, 2: {1: 1, 2: 2, 3: 1}, 3: {1: 0, 2: 2, 3: 2}}) # Verified Case -- (https://bit.ly/2ur7Rj4)
>>> cm = ConfusionMatrix(matrix={1: {1: 3, 2: 0, 3: 1}, 2: {1: 1, 2: 2, 3: 1}, 3: {1: 0, 2: 2, 3: 2}}) # Verified Case -- (https://pycm.io/ref?n=9)
>>> assert isclose(cm.ARI, 0.08333333333333333, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix([0, 0, 1, 1], [0, 0, 1, 1]) # Verified Case -- (https://bit.ly/30PNzvL)
>>> cm = ConfusionMatrix([0, 0, 1, 1], [0, 0, 1, 1]) # Verified Case -- (https://pycm.io/ref?n=10)
>>> assert isclose(cm.ARI, 1.0, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix([0, 0, 1, 2], [0, 0, 1, 1]) # Verified Case -- (https://bit.ly/30PNzvL)
>>> cm = ConfusionMatrix([0, 0, 1, 2], [0, 0, 1, 1]) # Verified Case -- (https://pycm.io/ref?n=10)
>>> assert isclose(cm.ARI, 0.5714285714285715, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix([0, 1, 2, 0, 1, 2], [0, 2, 1, 0, 0, 1]) # Verified Case -- (https://bit.ly/3egZBEG)
>>> cm = ConfusionMatrix([0, 1, 2, 0, 1, 2], [0, 2, 1, 0, 0, 1]) # Verified Case -- (https://pycm.io/ref?n=11)
>>> assert isclose(cm.weighted_average("F1"), 0.26666666666666666, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix([0, 1, 2, 2, 2], [0, 0, 2, 2, 1]) # Verified Case -- (https://bit.ly/2yidCBo)
>>> cm = ConfusionMatrix([0, 1, 2, 2, 2], [0, 0, 2, 2, 1]) # Verified Case -- (https://pycm.io/ref?n=12)
>>> assert isclose(cm.average("PPV"), 0.5, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.average("TPR"), 0.5555555555555555, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.average("F1"), 0.4888888888888889, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.weighted_average("PPV"), 0.7, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.weighted_average("TPR"), 0.6, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm.weighted_average("F1"), 0.6133333333333334, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={"often": {"often": 16, "seldom": 6, "never": 2}, "seldom": {"often": 4, "seldom": 10, "never": 1}, "never": {"often": 3, "seldom": 0, "never": 8}}) # Verified Case -- (https://bit.ly/3btZm7z)
>>> cm = ConfusionMatrix(matrix={"often": {"often": 16, "seldom": 6, "never": 2}, "seldom": {"often": 4, "seldom": 10, "never": 1}, "never": {"often": 3, "seldom": 0, "never": 8}}) # Verified Case -- (https://pycm.io/ref?n=13)
>>> weighted_kappa = cm.weighted_kappa(weight={"often": {"often": 0, "seldom": 1, "never": 2}, "seldom": {"often": 1, "seldom": 0, "never": 1}, "never": {"often": 2, "seldom": 1, "never": 0}})
>>> assert isclose(weighted_kappa, 0.5009505703422054, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> weighted_kappa = cm.weighted_kappa(weight={"often": {"often": 0, "seldom": 1, "never": 1}, "seldom": {"often": 1, "seldom": 0, "never": 1}, "never": {"often": 1, "seldom": 1, "never": 0}})
Expand All @@ -212,7 +212,7 @@
>>> assert isclose(cm.B, 0.6896551724137931, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={1: {1: 10, 2: 10, 3: 0}, 2: {1: 10, 2: 10, 3: 0}, 3: {1: 0, 2: 0, 3: 60}}) # Verified Case -- (Warrens, Raadt, 2019)
>>> assert isclose(cm.B, 0.8636363636363636, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={1: {1: 13, 2: 0, 3: 0}, 2: {1: 0, 2: 20, 3: 7}, 3: {1: 0, 2: 4, 3: 56}}) # Verified Case -- (https://bit.ly/3fWUuKF)
>>> cm = ConfusionMatrix(matrix={1: {1: 13, 2: 0, 3: 0}, 2: {1: 0, 2: 20, 3: 7}, 3: {1: 0, 2: 4, 3: 56}}) # Verified Case -- (https://pycm.io/ref?n=14)
>>> assert isclose(cm.Alpha, 0.7972584977308513, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> weighted_alpha = cm.weighted_alpha(weight={1: {1: 0, 2: 1, 3: 1}, 2: {1: 1, 2: 0, 3: 1}, 3: {1: 1, 2: 1, 3: 0}})
>>> assert isclose(weighted_alpha, 0.7972584977308516, abs_tol=ABS_TOL, rel_tol=REL_TOL)
Expand All @@ -221,62 +221,62 @@
>>> assert isclose(cm.AC1, 0.8493305482313461, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={1: {1: 55, 2: 10, 3: 2}, 2: {1: 6, 2: 4, 3: 10}, 3: {1: 2, 2: 5, 3: 6}}) # Verified Case -- (Gwet, Kilem L. Handbook of inter-rater reliability, 2014)
>>> assert isclose(cm.aickin_alpha(), 0.40455288947232665, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm = ConfusionMatrix(matrix={1: {1: 60, 0: 40}, 0: {0: 80, 1: 20}}) # Verified Case -- (https://bit.ly/3ooCi0t)
>>> cm = ConfusionMatrix(matrix={1: {1: 60, 0: 40}, 0: {0: 80, 1: 20}}) # Verified Case -- (https://pycm.io/ref?n=15)
>>> assert isclose(cm.sensitivity_index()[1], 1.094968336708714, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> y_true = np.array([0, 1, 1, 0])
>>> y_true_categorical = np.array(["spam", "ham", "ham", "spam"])
>>> y_prob = np.array([0.1, 0.9, 0.8, 0.3])
>>> cm1 = ConfusionMatrix(y_true, y_prob, threshold=lambda x: 1) # Verified Case -- (https://bit.ly/3n8Uo7R)
>>> cm1 = ConfusionMatrix(y_true, y_prob, threshold=lambda x: 1) # Verified Case -- (https://pycm.io/ref?n=16)
>>> assert isclose(cm1.brier_score(), 0.03749999999999999, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm1.brier_score(pos_class=1), 0.03749999999999999, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm2 = ConfusionMatrix(y_true, 1-y_prob, threshold=lambda x: 1) # Verified Case -- (https://bit.ly/3n8Uo7R)
>>> cm2 = ConfusionMatrix(y_true, 1-y_prob, threshold=lambda x: 1) # Verified Case -- (https://pycm.io/ref?n=16)
>>> assert isclose(cm2.brier_score(pos_class=0), 0.0375, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm3 = ConfusionMatrix(y_true_categorical, y_prob, threshold=lambda x: "ham") # Verified Case -- (https://bit.ly/3n8Uo7R)
>>> cm3 = ConfusionMatrix(y_true_categorical, y_prob, threshold=lambda x: "ham") # Verified Case -- (https://pycm.io/ref?n=16)
>>> assert isclose(cm3.brier_score(pos_class="ham"), 0.03749999999999999, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm4 = ConfusionMatrix(y_true, y_prob, sample_weight=[2, 2, 3, 3], threshold=lambda x: 1)
>>> assert isclose(cm4.brier_score(), 0.043, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm5 = ConfusionMatrix(y_true, np.array(y_prob) > 0.5, threshold=lambda x: 1) # Verified Case -- (https://bit.ly/3n8Uo7R)
>>> cm5 = ConfusionMatrix(y_true, np.array(y_prob) > 0.5, threshold=lambda x: 1) # Verified Case -- (https://pycm.io/ref?n=16)
>>> assert isclose(cm5.brier_score(), 0.0, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> y_true = np.array([0, 1, 1, 0])
>>> y_true_categorical = np.array(["spam", "ham", "ham", "spam"])
>>> y_prob = np.array([0.1, 0.9, 0.8, 0.35])
>>> cm1 = ConfusionMatrix(y_true, y_prob, threshold=lambda x: 1) # Verified Case -- (https://bit.ly/420uyVW)
>>> cm1 = ConfusionMatrix(y_true, y_prob, threshold=lambda x: 1) # Verified Case -- (https://pycm.io/ref?n=17)
>>> assert isclose(cm1.log_loss(), 0.21616187468057912, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm1.log_loss(pos_class=1), 0.21616187468057912, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm2 = ConfusionMatrix(y_true, 1-y_prob, threshold=lambda x: 1) # Verified Case -- (https://bit.ly/420uyVW)
>>> cm2 = ConfusionMatrix(y_true, 1-y_prob, threshold=lambda x: 1) # Verified Case -- (https://pycm.io/ref?n=17)
>>> assert isclose(cm2.log_loss(pos_class=0), 0.21616187468057912, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm3 = ConfusionMatrix(y_true_categorical, y_prob, threshold=lambda x: "ham") # Verified Case -- (https://bit.ly/420uyVW)
>>> cm3 = ConfusionMatrix(y_true_categorical, y_prob, threshold=lambda x: "ham") # Verified Case -- (https://pycm.io/ref?n=17)
>>> assert isclose(cm3.log_loss(pos_class="ham"), 0.21616187468057912, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm3.log_loss(pos_class="ham", normalize=False), 0.8646474987223165, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm4 = ConfusionMatrix(y_true, y_prob, sample_weight=[2, 2, 3, 3], threshold=lambda x: 1) # Verified Case -- (https://bit.ly/420uyVW)
>>> cm4 = ConfusionMatrix(y_true, y_prob, sample_weight=[2, 2, 3, 3], threshold=lambda x: 1) # Verified Case -- (https://pycm.io/ref?n=17)
>>> assert isclose(cm4.log_loss(), 0.2383221464851297, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm4.log_loss(normalize=False), 2.383221464851297, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> y1 = [1, 1, 0, 0, 0, 1]
>>> y2 = [1, 0, 1, 1, 0, 1]
>>> cm1 = ConfusionMatrix(y1, y2) # Verified Case -- (https://bit.ly/3OWrZ00)
>>> cm1 = ConfusionMatrix(y1, y2) # Verified Case -- (https://pycm.io/ref?n=18)
>>> cm1.HD[1]
3
>>> cm1.HD[0]
3
>>> y1 = [1, 1, 0, 1, 0, 0, 1, 1, 1, 1]
>>> y2 = [1, 0, 1, 1, 0, 1, 0, 1, 0, 1]
>>> cm2 = ConfusionMatrix(y1, y2) # Verified Case -- (https://bit.ly/3zVWUoV)
>>> cm2 = ConfusionMatrix(y1, y2) # Verified Case -- (https://pycm.io/ref?n=18)
>>> cm2.HD[1]
5
>>> cm2.HD[0]
5
>>> cm1 = ConfusionMatrix(matrix = {1: {1: 2, 0: 2}, 0: {0: 778, 1: 2}}) # Verified Case -- (https://bit.ly/3BVdNBp)
>>> cm1 = ConfusionMatrix(matrix = {1: {1: 2, 0: 2}, 0: {0: 778, 1: 2}}) # Verified Case -- (https://pycm.io/ref?n=19)
>>> assert isclose(cm1.BB[1], 0.5, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm2 = ConfusionMatrix(matrix = {1: {1: 2, 0: 3}, 0: {0: 775, 1: 4}}) # Verified Case -- (https://bit.ly/3BVdNBp)
>>> cm2 = ConfusionMatrix(matrix = {1: {1: 2, 0: 3}, 0: {0: 775, 1: 4}}) # Verified Case -- (https://pycm.io/ref?n=19)
Comment thread
sepandhaghighi marked this conversation as resolved.
>>> assert isclose(cm2.BB[1], 0.3333333333333333, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> crv = Curve(actual_vector = np.array([1, 1, 2, 2]), probs = np.array([[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]]), classes=[2, 1]) # Verified Case -- (https://bit.ly/3MIMk9z)
>>> crv = Curve(actual_vector = np.array([1, 1, 2, 2]), probs = np.array([[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]]), classes=[2, 1]) # Verified Case -- (https://pycm.io/ref?n=20)
Comment thread
sepandhaghighi marked this conversation as resolved.
>>> crv.thresholds
[0.1, 0.2, 0.35, 0.4, 0.6, 0.65, 0.8, 0.9]
>>> crv.data[2]["TPR"]
[1.0, 1.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.0]
>>> crv.data[2]["FPR"]
[1.0, 0.5, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0]
>>> crv = ROCCurve(actual_vector = np.array([1, 1, 2, 2]), probs = np.array([[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]]), classes=[2, 1]) # Verified Case -- (https://bit.ly/2Hqg0Ix)
>>> crv = ROCCurve(actual_vector = np.array([1, 1, 2, 2]), probs = np.array([[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]]), classes=[2, 1]) # Verified Case -- (https://pycm.io/ref?n=21)
>>> crv.thresholds
[0.1, 0.2, 0.35, 0.4, 0.6, 0.65, 0.8, 0.9]
>>> crv.data[2]["TPR"]
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True
>>> abs(crv.area(method="midpoint")[2]-0.75) < 0.001
True
>>> crv = PRCurve(actual_vector = np.array([1, 1, 2, 2]), probs = np.array([[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]]), classes=[2, 1]) # Verified Case -- (https://bit.ly/2PqUeKx)
>>> crv = PRCurve(actual_vector = np.array([1, 1, 2, 2]), probs = np.array([[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]]), classes=[2, 1]) # Verified Case -- (https://pycm.io/ref?n=22)
>>> crv.data[2]["TPR"]
[1.0, 1.0, 1.0, 0.5, 0.5, 0.5, 0.5]
>>> crv.data[2]["PPV"]
[0.5, 0.6666666666666666, 0.6666666666666666, 0.5, 1.0, 1.0, 1.0]
>>> abs(crv.area()[2] - 0.2916) < 0.001 # Verified Case -- (https://bit.ly/2Hqg0Ix)
>>> abs(crv.area()[2] - 0.2916) < 0.001 # Verified Case -- (https://pycm.io/ref?n=21)
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True
>>> abs(crv.area(method="midpoint")[2] - 0.2916) < 0.001
True
>>> cm1 = ConfusionMatrix(matrix = {1: {1: 2, 0: 2}, 0: {0: 778, 1: 2}}) # Verified Case -- (https://bit.ly/3vVMWRT)
>>> cm2 = ConfusionMatrix(matrix = {1: {1: 2, 0: 3}, 0: {0: 775, 1: 4}}) # Verified Case -- (https://bit.ly/3vVMWRT)
>>> cm1 = ConfusionMatrix(matrix = {1: {1: 2, 0: 2}, 0: {0: 778, 1: 2}}) # Verified Case -- (https://pycm.io/ref?n=23)
>>> cm2 = ConfusionMatrix(matrix = {1: {1: 2, 0: 3}, 0: {0: 775, 1: 4}}) # Verified Case -- (https://pycm.io/ref?n=23)
>>> assert isclose(cm1.distance(metric=DistanceType.AMPLE)[1], 0.49743589743589745, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm2.distance(metric=DistanceType.AMPLE)[1], 0.32947729220222793, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm1.distance(metric=DistanceType.Anderberg)[1], 0.0, abs_tol=ABS_TOL, rel_tol=REL_TOL)
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>>> assert isclose(cm2.distance(metric=DistanceType.KuhnsVI)[1], 0.394865211810013, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm1.distance(metric=DistanceType.KuhnsVII)[1], 0.49489795918367346, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm2.distance(metric=DistanceType.KuhnsVII)[1], 0.3581621145590755, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> mlcm = MultiLabelCM(actual_vector=[{"cat", "bird"}, {"dog"}], predict_vector=[{"cat"}, {"dog", "bird"}], classes=["cat", "dog", "bird"]) # Verified Case -- (http://bitly.ws/GNq2)
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>>> mlcm = MultiLabelCM(actual_vector=[{"cat", "bird"}, {"dog"}], predict_vector=[{"cat"}, {"dog", "bird"}], classes=["cat", "dog", "bird"]) # Verified Case -- (?)
>>> mlcm.actual_vector_multihot
[[1, 0, 1], [0, 1, 0]]
>>> mlcm.predict_vector_multihot
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