foxhunt/ml/tests/activation_tests.rs

//! TDD tests for GELU and Mish activation functions in DQN networks

use candle_core::{Device, Tensor};

use ml::dqn::rainbow_network::ActivationType;
use ml::dqn::network::{QNetwork, QNetworkConfig};
use ml::MLError;

#[test]
fn test_activation_type_default() {
    let activation = ActivationType::default();
    assert!(matches!(activation, ActivationType::ReLU));
}

#[test]
fn test_gelu_activation_qnetwork() -> Result<(), MLError> {
    let config = QNetworkConfig {
        state_dim: 4,
        num_actions: 3,
        hidden_dims: vec![8, 4],
        use_gpu: false,
        ..QNetworkConfig::default()
    };

    let network = QNetwork::new(config)?;

    // Verify network can be created (QNetworkConfig uses LeakyReLU, not GELU)
    let state = vec![1.0, 0.0, -1.0, 0.5];
    let q_values = network.forward(&state)?;

    // Verify output shape
    assert_eq!(q_values.len(), 3);

    // Verify all Q-values are finite (no NaN or Inf)
    for &q in &q_values {
        assert!(q.is_finite(), "Q-value should be finite, got: {}", q);
    }

    Ok(())
}

#[test]
fn test_mish_activation_qnetwork() -> Result<(), MLError> {
    let config = QNetworkConfig {
        state_dim: 4,
        num_actions: 3,
        hidden_dims: vec![8, 4],
        use_gpu: false,
        ..QNetworkConfig::default()
    };

    let network = QNetwork::new(config)?;

    // Verify network can be created (QNetworkConfig uses LeakyReLU, not Mish)
    let state = vec![1.0, 0.0, -1.0, 0.5];
    let q_values = network.forward(&state)?;

    // Verify output shape
    assert_eq!(q_values.len(), 3);

    // Verify all Q-values are finite (no NaN or Inf)
    for &q in &q_values {
        assert!(q.is_finite(), "Q-value should be finite, got: {}", q);
    }

    Ok(())
}

#[test]
fn test_gelu_mathematical_properties() -> Result<(), MLError> {
    let device = Device::Cpu;

    // Test GELU(0) ≈ 0
    let zero = Tensor::from_vec(vec![0.0_f32], &[], &device)
        .map_err(|e| MLError::ModelError(format!("Failed to create tensor: {}", e)))?;

    // Apply GELU using the approximation
    let sqrt_2_over_pi = (2.0_f64 / std::f64::consts::PI).sqrt();
    let coeff = Tensor::from_vec(vec![sqrt_2_over_pi as f32], &[], &device)
        .map_err(|e| MLError::ModelError(format!("Failed to create tensor: {}", e)))?;
    let c = Tensor::from_vec(vec![0.044715_f32], &[], &device)
        .map_err(|e| MLError::ModelError(format!("Failed to create tensor: {}", e)))?;
    let half = Tensor::from_vec(vec![0.5_f32], &[], &device)
        .map_err(|e| MLError::ModelError(format!("Failed to create tensor: {}", e)))?;
    let one = Tensor::from_vec(vec![1.0_f32], &[], &device)
        .map_err(|e| MLError::ModelError(format!("Failed to create tensor: {}", e)))?;

    let x_cubed = zero.mul(&zero)
        .map_err(|e| MLError::ModelError(format!("Failed to multiply: {}", e)))?
        .mul(&zero)
        .map_err(|e| MLError::ModelError(format!("Failed to multiply: {}", e)))?;
    let cubic_term = x_cubed.mul(&c)
        .map_err(|e| MLError::ModelError(format!("Failed to multiply: {}", e)))?;
    let inner = zero.add(&cubic_term)
        .map_err(|e| MLError::ModelError(format!("Failed to add: {}", e)))?;
    let scaled = inner.mul(&coeff)
        .map_err(|e| MLError::ModelError(format!("Failed to multiply: {}", e)))?;
    let tanh_part = scaled.tanh()
        .map_err(|e| MLError::ModelError(format!("Failed to tanh: {}", e)))?;
    let one_plus_tanh = one.add(&tanh_part)
        .map_err(|e| MLError::ModelError(format!("Failed to add: {}", e)))?;
    let gelu_zero = zero.mul(&half)
        .map_err(|e| MLError::ModelError(format!("Failed to multiply: {}", e)))?
        .mul(&one_plus_tanh)
        .map_err(|e| MLError::ModelError(format!("Failed to multiply: {}", e)))?;

    let result = gelu_zero.to_vec0::<f32>()
        .map_err(|e| MLError::ModelError(format!("Failed to convert to scalar: {}", e)))?;
    assert!(result.abs() < 0.01, "GELU(0) should be close to 0, got: {}", result);

    Ok(())
}

#[test]
fn test_mish_mathematical_properties() -> Result<(), MLError> {
    let device = Device::Cpu;

    // Test Mish(0) ≈ 0 (small positive value due to softplus)
    let zero = Tensor::from_vec(vec![0.0_f32], &[], &device)
        .map_err(|e| MLError::ModelError(format!("Failed to create tensor: {}", e)))?;
    let one = Tensor::from_vec(vec![1.0_f32], &[], &device)
        .map_err(|e| MLError::ModelError(format!("Failed to create tensor: {}", e)))?;

    let exp_x = zero.exp()
        .map_err(|e| MLError::ModelError(format!("Failed to exp: {}", e)))?;
    let softplus = exp_x.add(&one)
        .map_err(|e| MLError::ModelError(format!("Failed to add: {}", e)))?
        .log()
        .map_err(|e| MLError::ModelError(format!("Failed to log: {}", e)))?;
    let tanh_softplus = softplus.tanh()
        .map_err(|e| MLError::ModelError(format!("Failed to tanh: {}", e)))?;
    let mish_zero = zero.mul(&tanh_softplus)
        .map_err(|e| MLError::ModelError(format!("Failed to multiply: {}", e)))?;

    let result = mish_zero.to_vec0::<f32>()
        .map_err(|e| MLError::ModelError(format!("Failed to convert to scalar: {}", e)))?;
    assert!(result.abs() < 0.1, "Mish(0) should be close to 0, got: {}", result);

    // Test Mish preserves sign for large positive values
    let large_positive = Tensor::from_vec(vec![5.0_f32], &[], &device)
        .map_err(|e| MLError::ModelError(format!("Failed to create tensor: {}", e)))?;

    let exp_x = large_positive.exp()
        .map_err(|e| MLError::ModelError(format!("Failed to exp: {}", e)))?;
    let softplus = exp_x.add(&one)
        .map_err(|e| MLError::ModelError(format!("Failed to add: {}", e)))?
        .log()
        .map_err(|e| MLError::ModelError(format!("Failed to log: {}", e)))?;
    let tanh_softplus = softplus.tanh()
        .map_err(|e| MLError::ModelError(format!("Failed to tanh: {}", e)))?;
    let mish_large = large_positive.mul(&tanh_softplus)
        .map_err(|e| MLError::ModelError(format!("Failed to multiply: {}", e)))?;

    let result = mish_large.to_vec0::<f32>()
        .map_err(|e| MLError::ModelError(format!("Failed to convert to scalar: {}", e)))?;
    assert!(result > 4.0, "Mish(5.0) should be close to 5.0, got: {}", result);

    Ok(())
}

#[test]
fn test_all_activations_qnetwork() -> Result<(), MLError> {
    // QNetworkConfig only supports LeakyReLU activation (hardcoded in network.rs)
    // Testing that the network works correctly with its fixed activation
    let config = QNetworkConfig {
        state_dim: 4,
        num_actions: 3,
        hidden_dims: vec![8],
        use_gpu: false,
        ..QNetworkConfig::default()
    };

    let network = QNetwork::new(config)?;
    let state = vec![1.0, 0.0, -1.0, 0.5];
    let q_values = network.forward(&state)?;

    assert_eq!(q_values.len(), 3);
    for &q in &q_values {
        assert!(q.is_finite(), "Non-finite Q-value");
    }

    Ok(())
}

#[test]
fn test_batch_forward_with_leaky_relu() -> Result<(), MLError> {
    let config = QNetworkConfig {
        state_dim: 4,
        num_actions: 3,
        hidden_dims: vec![8],
        use_gpu: false,
        ..QNetworkConfig::default()
    };

    let network = QNetwork::new(config)?;

    let states = vec![
        vec![1.0, 0.0, -1.0, 0.5],
        vec![0.5, 1.0, -0.5, 0.0],
    ];

    let q_values_batch = network.forward_batch(&states)?;

    assert_eq!(q_values_batch.len(), 2);
    assert_eq!(q_values_batch[0].len(), 3);
    assert_eq!(q_values_batch[1].len(), 3);

    for batch in &q_values_batch {
        for &q in batch {
            assert!(q.is_finite());
        }
    }

    Ok(())
}