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function roc_curve

roc_curve(
    y_true: 'Sequence[numbers.Number]',
    y_probas: 'Sequence[Sequence[float]] | None' = None,
    labels: 'list[str] | None' = None,
    classes_to_plot: 'list[numbers.Number] | None' = None,
    title: 'str' = 'ROC Curve',
    split_table: 'bool' = False
) → CustomChart
Constructs Receiver Operating Characteristic (ROC) curve chart. Args:
  • y_true: The true class labels (ground truth) for the target variable. Shape should be (num_samples,).
  • y_probas: The predicted probabilities or decision scores for each class. Shape should be (num_samples, num_classes).
  • labels: Human-readable labels corresponding to the class indices in y_true. For example, if labels=['dog', 'cat'], class 0 will be displayed as ‘dog’ and class 1 as ‘cat’ in the plot. If None, the raw class indices from y_true will be used. Default is None.
  • classes_to_plot: A subset of unique class labels to include in the ROC curve. If None, all classes in y_true will be plotted. Default is None.
  • title: Title of the ROC curve plot. Default is “ROC Curve”.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.
Returns:
  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().
Raises:
  • wandb.Error: If numpy, pandas, or scikit-learn are not found.
Example: 
import numpy as np
import wandb

# Simulate a medical diagnosis classification problem with three diseases
n_samples = 200
n_classes = 3

# True labels: assign "Diabetes", "Hypertension", or "Heart Disease" to
# each sample
disease_labels = ["Diabetes", "Hypertension", "Heart Disease"]
# 0: Diabetes, 1: Hypertension, 2: Heart Disease
y_true = np.random.choice([0, 1, 2], size=n_samples)

# Predicted probabilities: simulate predictions, ensuring they sum to 1
# for each sample
y_probas = np.random.dirichlet(np.ones(n_classes), size=n_samples)

# Specify classes to plot (plotting all three diseases)
classes_to_plot = [0, 1, 2]

# Initialize a W&B run and log a ROC curve plot for disease classification
with wandb.init(project="medical_diagnosis") as run:
   roc_plot = wandb.plot.roc_curve(
        y_true=y_true,
        y_probas=y_probas,
        labels=disease_labels,
        classes_to_plot=classes_to_plot,
        title="ROC Curve for Disease Classification",
   )
   run.log({"roc-curve": roc_plot})
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