# tests/testthat/test-cross-price.R
# Test suite for cross-price NLS models per EQUATIONS_CONTRACT.md

# ==============================================================================
# Helper functions for simulating cross-price demand data
# ==============================================================================

#' Simulate cross-price demand data for exponentiated equation
#' Per EQUATIONS_CONTRACT.md:
#' y = Q_alone * 10^(I * exp(-beta * x))
simulate_cp_exponentiated <- function(
    n = 50,
    qalone = 10,
    I = 1.5,
    beta = 0.05,
    sigma = 0.5,
    seed = 42
) {
  set.seed(seed)
  x <- runif(n, min = 1, max = 50)
  # True model: y = qalone * 10^(I * exp(-beta * x))
  y_true <- qalone * 10^(I * exp(-beta * x))
  # Add multiplicative noise
  y <- y_true * exp(rnorm(n, 0, sigma))
  data.frame(x = x, y = y)
}

#' Simulate cross-price demand data for exponential equation
#' Per EQUATIONS_CONTRACT.md:
#' log10(y) = log10(Q_alone) + I * exp(-beta * x)
simulate_cp_exponential <- function(
    n = 50,
    qalone = 10,
    I = 1.5,
    beta = 0.05,
    sigma = 0.2,
    seed = 42
) {
  set.seed(seed)
  x <- runif(n, min = 1, max = 50)
  # True model on log10 scale: log10(y) = log10(qalone) + I * exp(-beta * x)
  log10_y_true <- log10(qalone) + I * exp(-beta * x)
  # Add noise on log10 scale
  log10_y <- log10_y_true + rnorm(n, 0, sigma)
  y <- 10^log10_y
  data.frame(x = x, y = y)
}

#' Simulate cross-price demand data for additive equation
#' Per EQUATIONS_CONTRACT.md:
#' y = Q_alone + I * exp(-beta * x)
simulate_cp_additive <- function(
    n = 50,
    qalone = 5,
    I = 10,
    beta = 0.05,
    sigma = 1,
    seed = 42
) {
  set.seed(seed)
  x <- runif(n, min = 1, max = 50)
  # True model: y = qalone + I * exp(-beta * x)
  y_true <- qalone + I * exp(-beta * x)
  # Add additive noise
  y <- y_true + rnorm(n, 0, sigma)
  y <- pmax(y, 0.1)  # Ensure positive
  data.frame(x = x, y = y)
}


# ==============================================================================
# Test: Basic fitting functionality
# ==============================================================================

test_that("fit_cp_nls fits exponentiated equation", {
  skip_if_not_installed("nls.multstart")

  data <- simulate_cp_exponentiated(n = 30, qalone = 10, I = 1.5, beta = 0.05, seed = 123)

  fit <- fit_cp_nls(data, equation = "exponentiated", return_all = TRUE, iter = 50)

  expect_s3_class(fit, "cp_model_nls")
  expect_equal(fit$equation, "exponentiated")
  expect_false(is.null(fit$model))

  # Check coefficient names match new parameterization
  coefs <- coef(fit$model)
  expect_true("log10_qalone" %in% names(coefs))
  expect_true("I" %in% names(coefs))
  expect_true("log10_beta" %in% names(coefs))
})

test_that("fit_cp_nls fits exponential equation", {
  skip_if_not_installed("nls.multstart")

  data <- simulate_cp_exponential(n = 30, qalone = 10, I = 1.5, beta = 0.05, seed = 123)

  fit <- fit_cp_nls(data, equation = "exponential", return_all = TRUE, iter = 50)

  expect_s3_class(fit, "cp_model_nls")
  expect_equal(fit$equation, "exponential")
  expect_false(is.null(fit$model))

  # Check coefficient names match new parameterization
  coefs <- coef(fit$model)
  expect_true("log10_qalone" %in% names(coefs))
  expect_true("I" %in% names(coefs))
  expect_true("log10_beta" %in% names(coefs))
})

test_that("fit_cp_nls fits additive equation", {
  skip_if_not_installed("nls.multstart")

  data <- simulate_cp_additive(n = 30, qalone = 5, I = 10, beta = 0.05, seed = 123)

  fit <- fit_cp_nls(data, equation = "additive", return_all = TRUE, iter = 50)

  expect_s3_class(fit, "cp_model_nls")
  expect_equal(fit$equation, "additive")
  expect_false(is.null(fit$model))

  # Check coefficient names match new parameterization
  coefs <- coef(fit$model)
  expect_true("log10_qalone" %in% names(coefs))
  expect_true("I" %in% names(coefs))
  expect_true("log10_beta" %in% names(coefs))
})


# ==============================================================================
# Test: Zero handling for exponential equation
# ==============================================================================

test_that("fit_cp_nls removes zeros with warning for exponential equation", {
  skip_if_not_installed("nls.multstart")

  # Create data with some zeros
  data <- simulate_cp_exponential(n = 30, qalone = 10, I = 1.5, beta = 0.05, seed = 123)
  data$y[c(1, 5, 10)] <- 0  # Add some zeros

  # Should warn about removing zeros

  expect_warning(
    fit <- fit_cp_nls(data, equation = "exponential", return_all = TRUE, iter = 50),
    "Removing 3 observation"
  )

  # Model should still fit on remaining data
  expect_s3_class(fit, "cp_model_nls")
  expect_equal(nrow(fit$data), 27)  # 30 - 3 zeros
})

test_that("fit_cp_nls errors with insufficient data after zero removal", {
  # Create data that's mostly zeros
  data <- data.frame(x = 1:10, y = c(rep(0, 8), 1, 2))

  expect_error(
    fit_cp_nls(data, equation = "exponential", return_all = TRUE),
    "Insufficient data"
  )
})

test_that("fit_cp_nls does not remove zeros for exponentiated equation", {
  skip_if_not_installed("nls.multstart")

  # Create data with some zeros
  data <- simulate_cp_exponentiated(n = 30, qalone = 10, I = 1.5, beta = 0.05, seed = 123)
  data$y[c(1, 5, 10)] <- 0.1  # Small values, no zeros (exponentiated naturally handles near-zero)

  # Should not warn
  expect_no_warning(
    fit <- fit_cp_nls(data, equation = "exponentiated", return_all = TRUE, iter = 50)
  )

  expect_s3_class(fit, "cp_model_nls")
})


# ==============================================================================
# Test: Parameter recovery
# ==============================================================================

test_that("fit_cp_nls recovers parameters for exponentiated equation", {
  skip_if_not_installed("nls.multstart")

  # True parameters
  true_qalone <- 10
  true_I <- 1.5
  true_beta <- 0.05

  # Simulate with low noise for parameter recovery
  data <- simulate_cp_exponentiated(
    n = 100,
    qalone = true_qalone,
    I = true_I,
    beta = true_beta,
    sigma = 0.1,  # Low noise
    seed = 42
  )

  fit <- fit_cp_nls(data, equation = "exponentiated", return_all = TRUE, iter = 100)

  coefs <- coef(fit$model)
  est_qalone <- 10^coefs["log10_qalone"]
  est_I <- coefs["I"]
  est_beta <- 10^coefs["log10_beta"]

  # Check parameters are within reasonable bounds (20% tolerance)
  expect_true(abs(est_qalone - true_qalone) / true_qalone < 0.2)
  expect_true(abs(est_I - true_I) / abs(true_I) < 0.2)
  expect_true(abs(est_beta - true_beta) / true_beta < 0.2)
})

test_that("fit_cp_nls recovers parameters for exponential equation", {
  skip_if_not_installed("nls.multstart")

  # True parameters
  true_qalone <- 10
  true_I <- 1.5
  true_beta <- 0.05

  # Simulate with low noise for parameter recovery
  data <- simulate_cp_exponential(
    n = 100,
    qalone = true_qalone,
    I = true_I,
    beta = true_beta,
    sigma = 0.1,  # Low noise
    seed = 42
  )

  fit <- fit_cp_nls(data, equation = "exponential", return_all = TRUE, iter = 100)

  coefs <- coef(fit$model)
  est_qalone <- 10^coefs["log10_qalone"]
  est_I <- coefs["I"]
  est_beta <- 10^coefs["log10_beta"]

  # Check parameters are within reasonable bounds (20% tolerance)
  expect_true(abs(est_qalone - true_qalone) / true_qalone < 0.2)
  expect_true(abs(est_I - true_I) / abs(true_I) < 0.2)
  expect_true(abs(est_beta - true_beta) / true_beta < 0.2)
})


# ==============================================================================
# Test: Equation relationship (exponentiated is 10^ of exponential)
# ==============================================================================

test_that("exponentiated and exponential equations are mathematically related", {
  skip_if_not_installed("nls.multstart")

  # Use same underlying parameters
  qalone <- 10
  I <- 1.5
  beta <- 0.05

  # Generate test x values
  x_test <- c(1, 5, 10, 20, 30)

  # Exponential equation: log10(y) = log10(qalone) + I * exp(-beta * x)
  log10_y_exp <- log10(qalone) + I * exp(-beta * x_test)

  # Exponentiated equation: y = qalone * 10^(I * exp(-beta * x))
  y_expo <- qalone * 10^(I * exp(-beta * x_test))

  # The exponentiated should be 10^(exponential)
  expect_equal(y_expo, 10^log10_y_exp, tolerance = 1e-10)
})


# ==============================================================================
# Test: Summary and predict methods
# ==============================================================================

test_that("summary.cp_model_nls works with log10 parameterization", {
  skip_if_not_installed("nls.multstart")

  data <- simulate_cp_exponentiated(n = 30, qalone = 10, I = 1.5, beta = 0.05, seed = 123)
  fit <- fit_cp_nls(data, equation = "exponentiated", return_all = TRUE, iter = 50)

  summ <- summary(fit)

  expect_s3_class(summ, "summary.cp_model_nls")
  expect_true(!is.null(summ$derived_metrics))
  expect_true("qalone" %in% names(summ$derived_metrics))
  expect_true("beta" %in% names(summ$derived_metrics))
  expect_true("log10_qalone" %in% names(summ$derived_metrics))
  expect_true("log10_beta" %in% names(summ$derived_metrics))
  expect_true("qalone_se" %in% names(summ$derived_metrics))
  expect_true("beta_se" %in% names(summ$derived_metrics))
  expect_true("log10_qalone_se" %in% names(summ$derived_metrics))
  expect_true("log10_beta_se" %in% names(summ$derived_metrics))

  # Check that natural-scale values are back-transformed correctly
  expect_equal(
    summ$derived_metrics$qalone,
    10^summ$derived_metrics$log10_qalone,
    tolerance = 1e-10
  )
  expect_equal(
    summ$derived_metrics$beta,
    10^summ$derived_metrics$log10_beta,
    tolerance = 1e-10
  )

  # Delta-method SE for back-transforms (natural scale)
  expect_equal(
    summ$derived_metrics$qalone_se,
    log(10) * summ$derived_metrics$qalone * summ$derived_metrics$log10_qalone_se,
    tolerance = 1e-10
  )
  expect_equal(
    summ$derived_metrics$beta_se,
    log(10) * summ$derived_metrics$beta * summ$derived_metrics$log10_beta_se,
    tolerance = 1e-10
  )
})

test_that("predict.cp_model_nls works with log10 parameterization", {
  skip_if_not_installed("nls.multstart")

  data <- simulate_cp_exponentiated(n = 30, qalone = 10, I = 1.5, beta = 0.05, seed = 123)
  fit <- fit_cp_nls(data, equation = "exponentiated", return_all = TRUE, iter = 50)

  newdata <- data.frame(x = c(1, 5, 10, 20))
  preds <- predict(fit, newdata = newdata)

  expect_true("x" %in% names(preds))
  expect_true("y_pred" %in% names(preds))
  expect_equal(nrow(preds), 4)
  expect_true(all(preds$y_pred > 0))  # Predictions should be positive
})

test_that("predict.cp_model_nls returns log10 and natural scale for exponential", {
  skip_if_not_installed("nls.multstart")

  data <- simulate_cp_exponential(n = 30, qalone = 10, I = 1.5, beta = 0.05, seed = 123)
  fit <- fit_cp_nls(data, equation = "exponential", return_all = TRUE, iter = 50)

  newdata <- data.frame(x = c(1, 5, 10, 20))
  preds <- predict(fit, newdata = newdata)

  expect_true("y_pred" %in% names(preds))
  expect_true("y_pred_log10" %in% names(preds))

  # y_pred is returned on the natural y scale; y_pred_log10 is the internal scale
  expect_equal(preds$y_pred, 10^preds$y_pred_log10, tolerance = 1e-10)
})


# ==============================================================================
# Test: Formula consistency with EQUATIONS_CONTRACT.md
# ==============================================================================

test_that("equation_text in summary matches EQUATIONS_CONTRACT.md", {
  skip_if_not_installed("nls.multstart")

  data <- simulate_cp_exponentiated(n = 30, qalone = 10, I = 1.5, beta = 0.05, seed = 123)

  # Exponentiated
  fit_expo <- fit_cp_nls(data, equation = "exponentiated", return_all = TRUE, iter = 50)
  summ_expo <- summary(fit_expo)
  expect_true(grepl("10\\^log10_qalone", summ_expo$equation_text))
  expect_true(grepl("10\\^\\(I", summ_expo$equation_text))
  expect_true(grepl("10\\^log10_beta", summ_expo$equation_text))

  # Exponential
  data_exp <- simulate_cp_exponential(n = 30, qalone = 10, I = 1.5, beta = 0.05, seed = 123)
  fit_exp <- fit_cp_nls(data_exp, equation = "exponential", return_all = TRUE, iter = 50)
  summ_exp <- summary(fit_exp)
  expect_true(grepl("log10\\(y\\)", summ_exp$equation_text))
  expect_true(grepl("log10_qalone", summ_exp$equation_text))

  # Additive
  data_add <- simulate_cp_additive(n = 30, qalone = 5, I = 10, beta = 0.05, seed = 123)
  fit_add <- fit_cp_nls(data_add, equation = "additive", return_all = TRUE, iter = 50)
  summ_add <- summary(fit_add)
  expect_true(grepl("10\\^log10_qalone", summ_add$equation_text))
})


# ==============================================================================
# Test: Edge cases
# ==============================================================================

test_that("fit_cp_nls handles single price point gracefully", {
  data <- data.frame(x = rep(5, 10), y = runif(10, 1, 10))

  # Should either fail gracefully or fit with warning
  result <- tryCatch(
    fit_cp_nls(data, equation = "exponentiated", return_all = TRUE, iter = 20),
    error = function(e) e
  )

  # Either error or failed fit is acceptable for degenerate data

  expect_true(inherits(result, "error") || inherits(result, "cp_model_nls"))
})

test_that("fit_cp_nls handles negative I (substitutes)", {
  skip_if_not_installed("nls.multstart")

  # Simulate data with negative I (substitute relationship)
  set.seed(42)
  n <- 50
  x <- runif(n, min = 1, max = 50)
  qalone <- 20
  I <- -0.5  # Negative: alternative price increase decreases target consumption
  beta <- 0.05

  y_true <- qalone * 10^(I * exp(-beta * x))
  y <- y_true * exp(rnorm(n, 0, 0.1))
  data <- data.frame(x = x, y = y)

  fit <- fit_cp_nls(data, equation = "exponentiated", return_all = TRUE, iter = 50)

  # I should be estimated as negative
  coefs <- coef(fit$model)
  expect_true(coefs["I"] < 0)
})

# ==============================================================================
# Tests for *_var argument mapping (new in 0.3.0)
# ==============================================================================

test_that("fit_cp_nls works with non-default x_var and y_var", {
  skip_if_not_installed("nls.multstart")

  data_canon <- simulate_cp_exponentiated(n = 50, seed = 1)
  # Rename canonical columns to custom names
  data_custom <- data_canon
  names(data_custom)[names(data_custom) == "x"] <- "price"
  names(data_custom)[names(data_custom) == "y"] <- "qty"

  fit_canon <- fit_cp_nls(
    data_canon,
    equation = "exponentiated",
    return_all = TRUE,
    iter = 50
  )
  fit_custom <- fit_cp_nls(
    data_custom,
    equation = "exponentiated",
    x_var = "price",
    y_var = "qty",
    return_all = TRUE,
    iter = 50
  )

  expect_s3_class(fit_custom, "cp_model_nls")
  # Canonical names in stored data
  expect_true("x" %in% names(fit_custom$data))
  expect_true("y" %in% names(fit_custom$data))
  # Coefficients should be approximately equal
  expect_equal(
    coef(fit_custom$model),
    coef(fit_canon$model),
    tolerance = 1e-4
  )
})

test_that("fit_cp_nls emits deprecation warning for start_vals", {
  skip_if_not_installed("nls.multstart")
  skip_if_not_installed("lifecycle")

  data <- simulate_cp_exponentiated(n = 50, seed = 42)
  sv <- list(log10_qalone = 1, I = 1.5, log10_beta = -2)

  lifecycle::expect_deprecated(
    fit_cp_nls(
      data,
      equation = "exponentiated",
      start_vals = sv,
      return_all = TRUE
    )
  )
})

test_that("fit_cp_nls accepts start_values without warning", {
  skip_if_not_installed("nls.multstart")

  data <- simulate_cp_exponentiated(n = 50, seed = 42)
  sv <- list(log10_qalone = 1, I = 1.5, log10_beta = -2)

  expect_no_warning(
    fit_cp_nls(
      data,
      equation = "exponentiated",
      start_values = sv,
      return_all = TRUE
    )
  )
})

test_that("fit_cp_nls errors on column collision", {
  data <- simulate_cp_exponentiated(n = 20, seed = 1)
  # data already has x and y; add a "price" col and request x_var = "price"
  # but ALSO have "x" present -> collision
  data$price <- data$x * 2

  expect_error(
    fit_cp_nls(data, x_var = "price"),
    regexp = "x_var.*price.*column named.*x",
    perl = TRUE
  )
})

test_that("fit_cp_linear works with non-default *_var mapping (fixed)", {
  skip_if_not_installed("lme4")

  # Build data with canonical names, then rename
  set.seed(99)
  n <- 40
  canon <- data.frame(
    id     = rep(1:4, each = 10),
    x      = runif(n, 1, 50),
    y      = runif(n, 0, 20),
    group  = rep(c("A", "B"), n / 2),
    target = "alt"
  )
  custom <- canon
  names(custom)[names(custom) == "x"]      <- "price"
  names(custom)[names(custom) == "y"]      <- "qty"
  names(custom)[names(custom) == "group"]  <- "grp"
  names(custom)[names(custom) == "target"] <- "tgt"

  fit <- fit_cp_linear(
    custom,
    type        = "fixed",
    x_var       = "price",
    y_var       = "qty",
    group_var   = "grp",
    target_var  = "tgt",
    return_all  = TRUE
  )

  expect_s3_class(fit, "cp_model_lm")
  # Canonical names in stored data
  expect_true(all(c("x", "y") %in% names(fit$data)))
  expect_false("price" %in% names(fit$data))
  expect_false("qty" %in% names(fit$data))
})

test_that("fit_cp_linear explicit filter_target and target_level behave like defaults", {
  set.seed(7)
  n <- 30
  data_with_target <- data.frame(
    x      = runif(n, 1, 50),
    y      = runif(n, 0, 20),
    target = sample(c("alt", "own"), n, replace = TRUE)
  )

  fit_default <- fit_cp_linear(
    data_with_target,
    type       = "fixed",
    return_all = TRUE
  )
  fit_explicit <- fit_cp_linear(
    data_with_target,
    type          = "fixed",
    filter_target = TRUE,
    target_level  = "alt",
    return_all    = TRUE
  )

  expect_identical(fit_default$data, fit_explicit$data)
})

test_that("fit_cp_linear errors on formula conflict with non-default *_var", {
  set.seed(42)
  data <- data.frame(
    price  = runif(20, 1, 50),
    qty    = runif(20, 0, 20),
    target = "alt"
  )

  expect_error(
    fit_cp_linear(
      data,
      type    = "fixed",
      x_var   = "price",
      y_var   = "qty",
      formula = qty ~ price
    ),
    regexp = "Custom formulas must use canonical column names"
  )
})

test_that("S3 methods work after fitting with non-default *_var", {
  skip_if_not_installed("nls.multstart")

  data_custom <- simulate_cp_exponentiated(n = 50, seed = 5)
  names(data_custom) <- c("alt_price", "target_cons")

  fit <- fit_cp_nls(
    data_custom,
    equation = "exponentiated",
    x_var    = "alt_price",
    y_var    = "target_cons",
    return_all = TRUE,
    iter = 50
  )

  expect_s3_class(fit, "cp_model_nls")
  # coef() should work without error
  expect_no_error(coef(fit$model))
  # predict() via S3 method (if available)
  expect_no_error({
    newdata <- data.frame(x = c(5, 10, 20))
    predict(fit, newdata = newdata)
  })
})