Benchmarking Optimizers for MLPs in Tabular Deep Learning

1. 1
   Define Benchmarking Scope

   Select specific MLP architectures, diverse tabular datasets (classification/regression, varying sizes), and a comprehensive set of optimizers for comparison. Include gradient descent variants (SGD, NAG), adaptive methods (Adam, AdamW, RMSprop, NAdam), and newer options.
2. 2
   Prepare Data & Model Environment

   Choose a deep learning framework (PyTorch/TensorFlow). Implement a robust data preprocessing pipeline including standardization for numerical features, encoding (e.g., one-hot, embeddings) for categorical features, and strategies for missing values. Define a standard MLP architecture to be used across all tests.
3. 3
   Implement Training & Evaluation Loop

   Develop a reusable training loop that can be easily configured with different optimizers, learning rates, and other hyperparameters. Integrate metrics tracking (e.g., accuracy, loss, F1-score) and a consistent evaluation protocol (e.g., k-fold cross-validation) for fair comparison.
4. 4
   Execute Benchmark Runs

   Automate the execution of training runs for each combination of dataset and optimizer. Ensure consistent initialization, epoch counts, and batch sizes across experiments to isolate the optimizer's impact. Log all relevant metrics and experiment configurations.
5. 5
   Analyze & Visualize Results

   Collect and aggregate the logged performance metrics. Use statistical analysis and visualization tools (e.g., line plots for convergence, bar charts for final accuracy/loss) to compare optimizer performance across different datasets and tasks. Identify trends and standout optimizers.
6. 6
   Document & Share Findings

   Summarize your benchmark results, highlighting which optimizers perform best under specific tabular data characteristics or task types. Provide data-driven recommendations that move beyond default choices, improving future model development.

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.preprocessing import StandardScaler
import numpy as np

# 1. Dummy Tabular Data
np.random.seed(42)
X = np.random.rand(100, 10) * 10 # 100 samples, 10 features
y = (X[:, 0] + X[:, 1] > 10).astype(int) # Binary classification target

# Preprocessing: Standardization
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Convert to PyTorch tensors
X_tensor = torch.tensor(X_scaled, dtype=torch.float32)
y_tensor = torch.tensor(y, dtype=torch.float32).unsqueeze(1) # For BCEWithLogitsLoss

# 2. Simple MLP Model Definition
class SimpleMLP(nn.Module):
    def __init__(self, input_dim):
        super(SimpleMLP, self).__init__()
        self.layers = nn.Sequential(
            nn.Linear(input_dim, 64),
            nn.ReLU(),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, 1)
        )

    def forward(self, x):
        return self.layers(x)

input_dim = X_tensor.shape[1]
model = SimpleMLP(input_dim)

# 3. Optimizer Initialization Examples
# Example 1: AdamW Optimizer
adamw_optimizer = optim.AdamW(model.parameters(), lr=0.001, weight_decay=0.01)
print(f"Initialized AdamW optimizer: {adamw_optimizer}")

# Example 2: SGD with Momentum Optimizer
sgd_optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
print(f"Initialized SGD optimizer: {sgd_optimizer}")

# Example Loss Function (for demonstration)
criterion = nn.BCEWithLogitsLoss()

# This code snippet sets up the basic components. 
# For a full benchmark, integrate these into a proper training loop.