Nominal/Categorical Metrics

class CramersV(Metric):
    def __init__(
        self,
        num_classes: int,
        bias_correction: bool = True,
        **kwargs
    ): ...

class TheilsU(Metric):
    def __init__(
        self,
        num_classes: int,
        **kwargs
    ): ...

class TschuprowsT(Metric):
    def __init__(
        self,
        num_classes: int,
        bias_correction: bool = True,
        **kwargs
    ): ...

class PearsonsContingencyCoefficient(Metric):
    def __init__(
        self,
        num_classes: int,
        **kwargs
    ): ...

class FleissKappa(Metric):
    def __init__(
        self,
        mode: str = "counts",
        **kwargs
    ): ...

import torch
from torchmetrics.nominal import CramersV, FleissKappa, TheilsU

# Association between two categorical variables
cramers_v = CramersV(num_classes=3)
theil_u = TheilsU(num_classes=3)

# Sample categorical data
preds = torch.randint(0, 3, (100,))  # Predicted categories
target = torch.randint(0, 3, (100,))  # True categories

# Compute association measures
cv_score = cramers_v(preds, target)
tu_score = theil_u(preds, target)

print(f"Cramer's V: {cv_score:.4f}")
print(f"Theil's U: {tu_score:.4f}")

# Inter-rater agreement
fleiss_kappa = FleissKappa(mode="counts")

# Rating counts: (subjects, categories)
# Each row represents how many raters assigned each category to a subject
ratings = torch.tensor([
    [0, 0, 0, 0, 14],  # Subject 1: all 14 raters chose category 5
    [0, 2, 6, 4, 2],   # Subject 2: mixed ratings
    [0, 0, 3, 5, 6],   # Subject 3: mixed ratings
])

kappa_score = fleiss_kappa(ratings)
print(f"Fleiss' Kappa: {kappa_score:.4f}")

CategoricalData = Tensor  # Integer categorical labels
RatingCounts = Tensor     # Inter-rater agreement count matrix