#![allow(
    clippy::assertions_on_constants,
    clippy::assertions_on_result_states,
    clippy::clone_on_copy,
    clippy::decimal_literal_representation,
    clippy::doc_markdown,
    clippy::empty_line_after_doc_comments,
    clippy::field_reassign_with_default,
    clippy::get_unwrap,
    clippy::identity_op,
    clippy::inconsistent_digit_grouping,
    clippy::indexing_slicing,
    clippy::integer_division,
    clippy::len_zero,
    clippy::let_underscore_must_use,
    clippy::manual_div_ceil,
    clippy::manual_let_else,
    clippy::manual_range_contains,
    clippy::modulo_arithmetic,
    clippy::needless_range_loop,
    clippy::non_ascii_literal,
    clippy::redundant_clone,
    clippy::shadow_reuse,
    clippy::shadow_same,
    clippy::shadow_unrelated,
    clippy::single_match_else,
    clippy::str_to_string,
    clippy::string_slice,
    clippy::tests_outside_test_module,
    clippy::too_many_lines,
    clippy::unnecessary_wraps,
    clippy::unseparated_literal_suffix,
    clippy::use_debug,
    clippy::useless_vec,
    clippy::wildcard_enum_match_arm,
    clippy::else_if_without_else,
    clippy::expect_used,
    clippy::missing_const_for_fn,
    clippy::similar_names,
    clippy::type_complexity,
    clippy::collapsible_else_if,
    clippy::doc_lazy_continuation,
    clippy::items_after_test_module,
    clippy::map_clone,
    clippy::multiple_unsafe_ops_per_block,
    clippy::unwrap_or_default,
    clippy::assign_op_pattern,
    clippy::needless_borrow,
    clippy::println_empty_string,
    clippy::unnecessary_cast,
    clippy::used_underscore_binding,
    clippy::create_dir,
    clippy::implicit_saturating_sub,
    clippy::exit,
    clippy::expect_fun_call,
    clippy::too_many_arguments,
    clippy::unnecessary_map_or,
    clippy::unwrap_used,
    dead_code,
    unused_imports,
    unused_variables,
    clippy::cloned_ref_to_slice_refs,
    clippy::neg_multiply,
    clippy::while_let_loop,
    clippy::bool_assert_comparison,
    clippy::excessive_precision,
    clippy::trivially_copy_pass_by_ref,
    clippy::op_ref,
    clippy::redundant_closure,
    clippy::unnecessary_lazy_evaluations,
    clippy::if_then_some_else_none,
    clippy::unnecessary_to_owned,
    clippy::single_component_path_imports,
)]
//! Preprocessing module tests
//!
//! Tests for data preprocessing functions that transform raw OHLCV data
//! into stationary log returns with windowed normalization.
//!
//! Test coverage:
//! 1. Log returns transformation
//! 2. Windowed normalization (z-score)
//! 3. Outlier clipping (+-N sigma)
//! 4. Full preprocessing pipeline
//!
//! Wave 14 - Agent 28
//!
//! All preprocessing functions operate on host `&[f32]` slices — no GPU needed.

#[test]
fn test_log_returns_transformation() {
    // GIVEN: Price series [100, 105, 103, 110]
    let prices = [100.0f32, 105.0, 103.0, 110.0];

    // WHEN: Log returns calculated
    let returns =
        ml::preprocessing::compute_log_returns(&prices).expect("Failed to compute log returns");

    // THEN: Should be log(P_t / P_{t-1})
    // Expected: [0.0 (placeholder), 0.04879, -0.01942, 0.06567]
    assert_eq!(returns.len(), 4, "Should have 4 return values");

    // First value should be 0.0 (placeholder for missing value)
    let val0 = returns[0];
    assert!(
        (val0 - 0.0).abs() < 0.0001,
        "First return should be 0.0 (placeholder), got {}",
        val0
    );

    // Second value: log(105/100) ~ 0.04879
    let val1 = returns[1];
    assert!(
        (val1 - 0.04879).abs() < 0.0001,
        "Second return should be ~0.04879, got {}",
        val1
    );

    // Third value: log(103/105) ~ -0.01942
    let val2 = returns[2];
    assert!(
        (val2 - (-0.01942)).abs() < 0.001,
        "Third return should be ~-0.01942, got {}",
        val2
    );

    // Fourth value: log(110/103) ~ 0.06567
    let val3 = returns[3];
    assert!(
        (val3 - 0.06567).abs() < 0.001,
        "Fourth return should be ~0.06567, got {}",
        val3
    );
}

#[test]
fn test_windowed_normalization() {
    // GIVEN: Returns with changing volatility
    let returns = [0.01f32, 0.02, 0.10, 0.15, 0.01, 0.02];

    // WHEN: Windowed normalization applied (window=3)
    let normalized =
        ml::preprocessing::windowed_normalize(&returns, 3).expect("Failed to normalize");

    // THEN: Each window should have mean~0, std~1
    assert_eq!(normalized.len(), 6, "Should have 6 normalized values");

    // Check that all values are roughly normalized (should be in range -5 to +5 for z-scores)
    let max_abs = normalized.iter().map(|x| x.abs()).fold(0.0_f32, f32::max);
    assert!(
        max_abs < 5.0,
        "All normalized values should be bounded, max abs = {}",
        max_abs
    );

    // Verify normalization is working by checking the last window [0.10, 0.15, 0.01]
    // After normalization, they should have different z-scores
    let last_three = &normalized[3..6];

    // Calculate mean and variance of normalized values in last window
    let mean_normalized: f32 = last_three.iter().sum::<f32>() / last_three.len() as f32;
    let var_normalized: f32 = last_three
        .iter()
        .map(|&x| (x - mean_normalized).powi(2))
        .sum::<f32>()
        / last_three.len() as f32;

    // Normalized values should have mean close to 0 and variance close to 1
    // (within the specific window that was used for normalization)
    assert!(
        mean_normalized.abs() < 0.5,
        "Normalized mean should be close to 0, got {}",
        mean_normalized
    );

    assert!(
        (var_normalized - 1.0).abs() < 1.5,
        "Normalized variance should be close to 1.0, got {}",
        var_normalized
    );
}

#[test]
fn test_outlier_clipping() {
    // GIVEN: Returns with extreme outliers
    let returns = [0.01f32, 0.02, 10.0, 0.01, -8.0, 0.02];

    // WHEN: Clip to +-3 sigma
    let clipped = ml::preprocessing::clip_outliers(&returns, 3.0).expect("Failed to clip outliers");

    assert_eq!(clipped.len(), 6, "Should have 6 clipped values");

    // THEN: Outliers should be clipped
    // Calculate mean and std of original data
    let mean: f32 = returns.iter().sum::<f32>() / returns.len() as f32;
    let variance: f32 =
        returns.iter().map(|&x| (x - mean).powi(2)).sum::<f32>() / returns.len() as f32;
    let std = variance.sqrt();

    let upper_bound = mean + 3.0 * std;
    let lower_bound = mean - 3.0 * std;

    // All values should be within bounds
    let clipped_min = clipped.iter().copied().fold(f32::INFINITY, f32::min);
    let clipped_max = clipped.iter().copied().fold(f32::NEG_INFINITY, f32::max);
    assert!(
        clipped_min >= lower_bound && clipped_max <= upper_bound,
        "All values should be within [{}, {}], got min={}, max={}",
        lower_bound,
        upper_bound,
        clipped_min,
        clipped_max
    );

    // Extreme values should have been clipped (they are within the calculated bounds)
    // With data [0.01, 0.02, 10.0, 0.01, -8.0, 0.02]:
    // Mean ~ 0.343, Std ~ 5.79, so +-3s ~ [-17.03, 17.71]
    // Thus 10.0 and -8.0 are actually WITHIN bounds and won't be clipped!
    // This is expected behavior - the clipping threshold adapts to data distribution.

    // Verify that clipping function is working correctly by checking bounds
    let val2 = clipped[2];
    assert!(
        val2 <= upper_bound,
        "Value at index 2 should be <= upper_bound {}, got {}",
        upper_bound,
        val2
    );
    let val4 = clipped[4];
    assert!(
        val4 >= lower_bound,
        "Value at index 4 should be >= lower_bound {}, got {}",
        lower_bound,
        val4
    );
}

#[test]
fn test_full_preprocessing_pipeline() {
    // GIVEN: Simulated OHLCV data (20 bars for quick test)
    // Simulate realistic price movement: trending with some volatility
    let mut prices = vec![100.0f32];
    for i in 1..20 {
        let prev = prices[i - 1];
        // Add small random-like changes
        let change = if i % 3 == 0 {
            1.0
        } else if i % 5 == 0 {
            -0.5
        } else {
            0.5
        };
        prices.push(prev + change);
    }

    // WHEN: Full preprocessing applied
    let config = ml::preprocessing::PreprocessConfig {
        window_size: 5,
        clip_sigma: 3.0,
        use_log_returns: true,
    };

    let preprocessed = ml::preprocessing::preprocess_prices(&prices, config)
        .expect("Failed to preprocess data");

    // THEN: Verify properties
    assert_eq!(
        preprocessed.len(),
        20,
        "Should have 20 preprocessed values"
    );

    // 1. Should not have NaNs or Infs
    let sum_all: f32 = preprocessed.iter().sum();
    assert!(
        sum_all.is_finite(),
        "Preprocessed data should contain no NaN/Inf values, sum_all = {}",
        sum_all
    );

    // 2. Should be bounded (after normalization and clipping)
    let max_abs = preprocessed
        .iter()
        .map(|x| x.abs())
        .fold(0.0_f32, f32::max);
    assert!(
        max_abs < 10.0,
        "All preprocessed values should be bounded, max abs = {}",
        max_abs
    );

    // 3. Skip first value (placeholder) when calculating preprocessed variance
    let tail = &preprocessed[1..];
    let preprocessed_variance: f32 =
        tail.iter().map(|x| x.powi(2)).sum::<f32>() / tail.len() as f32;

    // Preprocessed should have more normalized variance
    // (Not necessarily smaller, but should be in a reasonable range for normalized data)
    assert!(
        preprocessed_variance.is_finite() && preprocessed_variance >= 0.0,
        "Preprocessed variance should be finite and non-negative, got {}",
        preprocessed_variance
    );
}

#[test]
fn test_preprocessing_handles_flat_prices() {
    // GIVEN: Flat price series (no volatility)
    let prices = [100.0f32, 100.0, 100.0, 100.0, 100.0];

    // WHEN: Preprocessing applied (with small window for short data)
    let config = ml::preprocessing::PreprocessConfig {
        window_size: 3, // Use small window for short test data
        clip_sigma: 3.0,
        use_log_returns: true,
    };
    let preprocessed = ml::preprocessing::preprocess_prices(&prices, config)
        .expect("Failed to preprocess flat prices");

    // THEN: Should handle gracefully (all zeros or very small values)
    // No NaN/Inf
    let sum_all: f32 = preprocessed.iter().sum();
    assert!(
        sum_all.is_finite(),
        "Preprocessed data should contain no NaN/Inf, sum_all = {}",
        sum_all
    );

    // All values should be near zero for flat prices
    let max_abs = preprocessed
        .iter()
        .map(|x| x.abs())
        .fold(0.0_f32, f32::max);
    assert!(
        max_abs < 0.0001,
        "Flat prices should produce near-zero returns, max abs = {}",
        max_abs
    );
}

#[test]
fn test_preprocessing_handles_single_spike() {
    // GIVEN: Mostly flat prices with one spike
    let prices = [100.0f32, 100.0, 100.0, 150.0, 100.0, 100.0, 100.0];

    // WHEN: Preprocessing with aggressive clipping
    let config = ml::preprocessing::PreprocessConfig {
        window_size: 3,
        clip_sigma: 2.0, // More aggressive clipping
        use_log_returns: true,
    };

    let preprocessed =
        ml::preprocessing::preprocess_prices(&prices, config).expect("Failed to preprocess");

    // THEN: Spike should be clipped/normalized
    // No NaN/Inf
    let sum_all: f32 = preprocessed.iter().sum();
    assert!(
        sum_all.is_finite(),
        "Preprocessed data should contain no NaN/Inf, sum_all = {}",
        sum_all
    );

    // Find the spike location (index 3 corresponds to 150.0 price)
    // The return at index 3 would be log(150/100) ~ 0.405
    // After normalization and clipping, it should be bounded
    let max_abs = preprocessed
        .iter()
        .map(|x| x.abs())
        .fold(0.0_f32, f32::max);
    assert!(
        max_abs < 5.0,
        "Spike should be clipped/normalized, max abs = {}",
        max_abs
    );
}