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Upgrade to Python 3.13 (#11588)

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Christian Clauss
2024-09-30 23:01:15 +02:00
committed by GitHub
parent a7bfa22455
commit 0177ae1cd5
35 changed files with 135 additions and 131 deletions
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machine_learning/loss_functions.py
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@ -22,7 +22,7 @@ def binary_cross_entropy(
>>> true_labels = np.array([0, 1, 1, 0, 1])
>>> predicted_probs = np.array([0.2, 0.7, 0.9, 0.3, 0.8])
>>> binary_cross_entropy(true_labels, predicted_probs)
>>> float(binary_cross_entropy(true_labels, predicted_probs))
0.2529995012327421
>>> true_labels = np.array([0, 1, 1, 0, 1])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
@ -68,7 +68,7 @@ def binary_focal_cross_entropy(
>>> true_labels = np.array([0, 1, 1, 0, 1])
>>> predicted_probs = np.array([0.2, 0.7, 0.9, 0.3, 0.8])
>>> binary_focal_cross_entropy(true_labels, predicted_probs)
>>> float(binary_focal_cross_entropy(true_labels, predicted_probs))
0.008257977659239775
>>> true_labels = np.array([0, 1, 1, 0, 1])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
@ -108,7 +108,7 @@ def categorical_cross_entropy(
>>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
>>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
>>> categorical_cross_entropy(true_labels, pred_probs)
>>> float(categorical_cross_entropy(true_labels, pred_probs))
0.567395975254385
>>> true_labels = np.array([[1, 0], [0, 1]])
>>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
@ -179,13 +179,13 @@ def categorical_focal_cross_entropy(
>>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
>>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
>>> alpha = np.array([0.6, 0.2, 0.7])
>>> categorical_focal_cross_entropy(true_labels, pred_probs, alpha)
>>> float(categorical_focal_cross_entropy(true_labels, pred_probs, alpha))
0.0025966118981496423
>>> true_labels = np.array([[0, 1, 0], [0, 0, 1]])
>>> pred_probs = np.array([[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
>>> alpha = np.array([0.25, 0.25, 0.25])
>>> categorical_focal_cross_entropy(true_labels, pred_probs, alpha)
>>> float(categorical_focal_cross_entropy(true_labels, pred_probs, alpha))
0.23315276982014324
>>> true_labels = np.array([[1, 0], [0, 1]])
@ -265,7 +265,7 @@ def hinge_loss(y_true: np.ndarray, y_pred: np.ndarray) -> float:
>>> true_labels = np.array([-1, 1, 1, -1, 1])
>>> pred = np.array([-4, -0.3, 0.7, 5, 10])
>>> hinge_loss(true_labels, pred)
>>> float(hinge_loss(true_labels, pred))
1.52
>>> true_labels = np.array([-1, 1, 1, -1, 1, 1])
>>> pred = np.array([-4, -0.3, 0.7, 5, 10])
@ -309,11 +309,11 @@ def huber_loss(y_true: np.ndarray, y_pred: np.ndarray, delta: float) -> float:
>>> true_values = np.array([0.9, 10.0, 2.0, 1.0, 5.2])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> np.isclose(huber_loss(true_values, predicted_values, 1.0), 2.102)
>>> bool(np.isclose(huber_loss(true_values, predicted_values, 1.0), 2.102))
True
>>> true_labels = np.array([11.0, 21.0, 3.32, 4.0, 5.0])
>>> predicted_probs = np.array([8.3, 20.8, 2.9, 11.2, 5.0])
>>> np.isclose(huber_loss(true_labels, predicted_probs, 1.0), 1.80164)
>>> bool(np.isclose(huber_loss(true_labels, predicted_probs, 1.0), 1.80164))
True
>>> true_labels = np.array([11.0, 21.0, 3.32, 4.0])
>>> predicted_probs = np.array([8.3, 20.8, 2.9, 11.2, 5.0])
@ -347,7 +347,7 @@ def mean_squared_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
>>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> np.isclose(mean_squared_error(true_values, predicted_values), 0.028)
>>> bool(np.isclose(mean_squared_error(true_values, predicted_values), 0.028))
True
>>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
@ -381,11 +381,11 @@ def mean_absolute_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
>>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> np.isclose(mean_absolute_error(true_values, predicted_values), 0.16)
>>> bool(np.isclose(mean_absolute_error(true_values, predicted_values), 0.16))
True
>>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> np.isclose(mean_absolute_error(true_values, predicted_values), 2.16)
>>> bool(np.isclose(mean_absolute_error(true_values, predicted_values), 2.16))
False
>>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 5.2])
@ -420,7 +420,7 @@ def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> fl
>>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
>>> mean_squared_logarithmic_error(true_values, predicted_values)
>>> float(mean_squared_logarithmic_error(true_values, predicted_values))
0.0030860877925181344
>>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
@ -459,17 +459,17 @@ def mean_absolute_percentage_error(
Examples:
>>> y_true = np.array([10, 20, 30, 40])
>>> y_pred = np.array([12, 18, 33, 45])
>>> mean_absolute_percentage_error(y_true, y_pred)
>>> float(mean_absolute_percentage_error(y_true, y_pred))
0.13125
>>> y_true = np.array([1, 2, 3, 4])
>>> y_pred = np.array([2, 3, 4, 5])
>>> mean_absolute_percentage_error(y_true, y_pred)
>>> float(mean_absolute_percentage_error(y_true, y_pred))
0.5208333333333333
>>> y_true = np.array([34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24])
>>> y_pred = np.array([37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23])
>>> mean_absolute_percentage_error(y_true, y_pred)
>>> float(mean_absolute_percentage_error(y_true, y_pred))
0.064671076436071
"""
if len(y_true) != len(y_pred):
@ -511,7 +511,7 @@ def perplexity_loss(
... [[0.03, 0.26, 0.21, 0.18, 0.30],
... [0.28, 0.10, 0.33, 0.15, 0.12]]]
... )
>>> perplexity_loss(y_true, y_pred)
>>> float(perplexity_loss(y_true, y_pred))
5.0247347775367945
>>> y_true = np.array([[1, 4], [2, 3]])
>>> y_pred = np.array(
@ -600,17 +600,17 @@ def smooth_l1_loss(y_true: np.ndarray, y_pred: np.ndarray, beta: float = 1.0) ->
>>> y_true = np.array([3, 5, 2, 7])
>>> y_pred = np.array([2.9, 4.8, 2.1, 7.2])
>>> smooth_l1_loss(y_true, y_pred, 1.0)
>>> float(smooth_l1_loss(y_true, y_pred, 1.0))
0.012500000000000022
>>> y_true = np.array([2, 4, 6])
>>> y_pred = np.array([1, 5, 7])
>>> smooth_l1_loss(y_true, y_pred, 1.0)
>>> float(smooth_l1_loss(y_true, y_pred, 1.0))
0.5
>>> y_true = np.array([1, 3, 5, 7])
>>> y_pred = np.array([1, 3, 5, 7])
>>> smooth_l1_loss(y_true, y_pred, 1.0)
>>> float(smooth_l1_loss(y_true, y_pred, 1.0))
0.0
>>> y_true = np.array([1, 3, 5])
@ -647,7 +647,7 @@ def kullback_leibler_divergence(y_true: np.ndarray, y_pred: np.ndarray) -> float
>>> true_labels = np.array([0.2, 0.3, 0.5])
>>> predicted_probs = np.array([0.3, 0.3, 0.4])
>>> kullback_leibler_divergence(true_labels, predicted_probs)
>>> float(kullback_leibler_divergence(true_labels, predicted_probs))
0.030478754035472025
>>> true_labels = np.array([0.2, 0.3, 0.5])
>>> predicted_probs = np.array([0.3, 0.3, 0.4, 0.5])