التصنيف متعدد الفئات: OvA مقابل OvO

import torch
import torch.nn as nn
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier
from sklearn.svm import SVC

# ── Sample data ────────────────────────────────────────────────────────
X_np, y_np = make_classification(n_samples=300, n_features=8,
                                  n_classes=3, n_informative=6, random_state=42)
X_train_np, X_test_np, y_train_np, _ = train_test_split(
    X_np, y_np, test_size=0.2, random_state=42)

# ── PyTorch multiclass setup ───────────────────────────────────────────
K = 3                                       # number of classes
batch = 16

# Tiny 2-layer net for the demo
class SimpleNet(nn.Module):
    def __init__(self): super().__init__(); self.fc = nn.Linear(8, K)
    def forward(self, x): return self.fc(x)

model = SimpleNet()
x = torch.randn(batch, 8)                   # one mini-batch
y = torch.randint(0, K, (batch,))           # class indices

# Class weights (handle imbalance)
class_weights = torch.tensor([1.0, 2.0, 1.5])   # weight rarer classes higher

# Softmax + Cross-Entropy (combined for numerical stability)
criterion = nn.CrossEntropyLoss(
    weight=class_weights,    # For imbalanced classes
    label_smoothing=0.1      # Prevents overconfident predictions
)

# Model outputs raw logits (no softmax in forward pass)
logits = model(x)            # Shape: (batch, K)
loss = criterion(logits, y)  # y contains class indices
print(f"Multiclass CE loss: {loss.item():.4f}")

# Predictions
probs = torch.softmax(logits, dim=-1)
preds = probs.argmax(dim=-1)

# Sklearn: OvR (OvA) strategy
ovr = OneVsRestClassifier(SVC(kernel='rbf', probability=True))
ovo = OneVsOneClassifier(SVC(kernel='rbf'))
ovr.fit(X_train_np, y_train_np)
print(f"OvR accuracy: {ovr.score(X_test_np, _):.3f}")