## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)

## ----setup--------------------------------------------------------------------
library(leakr)

## ----detector_overview--------------------------------------------------------
# View available detectors
available_detectors <- list_registered_detectors()
print(available_detectors)

## ----medical_example----------------------------------------------------------
# Create a medical dataset with subtle leakage
set.seed(456)
n <- 500

medical_data <- data.frame(
  patient_id = 1:n,
  age = sample(25:75, n, replace = TRUE),
  bmi = rnorm(n, 25, 4),
  blood_pressure = rnorm(n, 120, 15),
  diagnosis = factor(sample(c("healthy", "diseased"), n, replace = TRUE, prob = c(0.8, 0.2)))
)

# Add a leaky feature: treatment_received (only available post-diagnosis)
medical_data$treatment_received <- ifelse(
  medical_data$diagnosis == "diseased", 
  sample(c("yes", "no"), sum(medical_data$diagnosis == "diseased"), replace = TRUE, prob = c(0.9, 0.1)),
  "no"
)

# Audit the medical data
medical_report <- leakr_audit(
  data = medical_data,
  target = "diagnosis",
  id = "patient_id"
)

print(medical_report)

## ----financial_example--------------------------------------------------------
# Create financial data with temporal leakage
set.seed(789)
dates <- seq(as.Date("2020-01-01"), as.Date("2023-12-31"), by = "month")

financial_data <- data.frame(
  account_id = 1:200,
  transaction_date = sample(dates, 200, replace = TRUE),
  amount = rlnorm(200, 4, 1),
  account_balance = rnorm(200, 1000, 500),
  default_risk = factor(sample(c("low", "high"), 200, replace = TRUE))
)

# Sort by date
financial_data <- financial_data[order(financial_data$transaction_date), ]

# Add feature that uses future information (credit score after default assessment)
financial_data$credit_score_updated <- ifelse(
  financial_data$default_risk == "high",
  rnorm(sum(financial_data$default_risk == "high"), 450, 50),
  rnorm(sum(financial_data$default_risk == "low"), 750, 75)
)

# Create temporal split
financial_data$split <- ifelse(
  financial_data$transaction_date < as.Date("2022-01-01"), 
  "train", 
  "test"
)

# Audit financial data
financial_report <- leakr_audit(
  data = financial_data,
  target = "default_risk",
  split = "split",
  id = "account_id"
)

print(financial_report)

## ----customer_duplicates------------------------------------------------------
# Create customer dataset with near-duplicates
set.seed(321)

# Original customers
customers <- data.frame(
  name = c("John Smith", "Jane Doe", "Bob Johnson", "Alice Brown", "Charlie Davis"),
  email = c("john@email.com", "jane@email.com", "bob@email.com", "alice@email.com", "charlie@email.com"),
  age = c(35, 28, 42, 31, 39),
  income = c(50000, 45000, 75000, 55000, 62000),
  purchase_category = factor(c("electronics", "books", "clothing", "electronics", "books"))
)

# Create near-duplicates with slight variations
near_dupes <- customers[1:3, ]
near_dupes$name <- c("J Smith", "Jane D", "Robert Johnson")  # Name variations
near_dupes$email <- c("john.smith@email.com", "j.doe@email.com", "bob.johnson@email.com")  # Email variations
near_dupes$age <- near_dupes$age + c(1, 0, -1)  # Age variations

# Combine datasets
all_customers <- rbind(customers, near_dupes)
all_customers$customer_id <- 1:nrow(all_customers)

# Audit for duplicates
dup_report <- leakr_audit(
  data = all_customers,
  target = "purchase_category",
  id = "customer_id"
)

print(dup_report)

## ----configuration------------------------------------------------------------
# Example of custom configuration for sensitive detection
sensitive_config <- list(
  sample_size = 5000,           # Limit sample size for large datasets
  correlation_threshold = 0.7,   # Lower threshold for correlation-based detection
  duplicate_threshold = 0.9      # Threshold for considering records as duplicates
)

# Apply custom configuration
iris_sensitive <- leakr_audit(
  data = iris,
  target = "Species",
  config = sensitive_config
)

print(iris_sensitive)

## ----large_dataset------------------------------------------------------------
# Create a large imbalanced dataset
set.seed(555)
large_n <- 10000

large_data <- data.frame(
  feature1 = rnorm(large_n),
  feature2 = sample(letters[1:10], large_n, replace = TRUE),
  feature3 = rnorm(large_n, 100, 20),
  # Imbalanced target
  target = factor(sample(c("rare", "common"), large_n, replace = TRUE, prob = c(0.05, 0.95)))
)

# Use stratified sampling to ensure representation
sample_indices <- stratified_sample(large_data$target, 1000)
sampled_data <- large_data[sample_indices, ]

# Verify sampling maintained class balance
table(large_data$target)
table(sampled_data$target)

# Audit sampled data
large_report <- leakr_audit(
  data = sampled_data,
  target = "target"
)

print(large_report)

## ----detailed_analysis--------------------------------------------------------
# Create complex dataset for comprehensive analysis
complex_data <- data.frame(
  id = 1:300,
  timestamp = seq(as.POSIXct("2023-01-01"), as.POSIXct("2023-12-31"), length.out = 300),
  feature_a = rnorm(300),
  feature_b = sample(LETTERS[1:5], 300, replace = TRUE),
  feature_c = rnorm(300, 50, 10),
  outcome = factor(sample(c("success", "failure"), 300, replace = TRUE))
)

# Add intentional leakage for demonstration
complex_data$leaky_feature <- ifelse(complex_data$outcome == "success", 1, 0)

# Generate comprehensive audit
detailed_report <- leakr_audit(
  data = complex_data,
  target = "outcome",
  id = "id"
)

# Generate detailed summary
detailed_summary <- leakr_summarise(detailed_report, top_n = 10, show_config = TRUE)
print(detailed_summary)

## ----multi_stage--------------------------------------------------------------
# Multi-stage validation function
comprehensive_validation <- function(data, target, id = NULL, split = NULL) {
  
  cat("Stage 1: Basic data validation\n")
  # Basic preprocessing and validation
  clean_data <- validate_and_preprocess_data(data, target, split, id)
  
  cat("Stage 2: Initial leakage screening\n")
  # Quick initial screening
  initial_report <- leakr_audit(clean_data, target = target, split = split, id = id)
  
  cat("Stage 3: Detailed analysis\n")
  # Generate detailed summary
  summary_report <- leakr_summarise(initial_report, top_n = 15, show_config = TRUE)
  
  # Count critical issues
  if(length(initial_report$issues) > 0) {
    critical_count <- sum(sapply(initial_report$issues, function(x) 
      !is.null(x$severity) && x$severity == "high"))
    
    if(critical_count > 0) {
      cat("WARNING:", critical_count, "critical issues detected!\n")
    }
  }
  
  return(list(
    data = clean_data,
    audit = initial_report,
    summary = summary_report
  ))
}

# Example usage
# validation_result <- comprehensive_validation(your_data, "target_column")

## ----domain_specific----------------------------------------------------------
# Example: E-commerce specific validation
ecommerce_validation <- function(data, target) {
  
  # Standard audit
  base_report <- leakr_audit(data, target = target)
  
  # Domain-specific checks
  issues <- list()
  
  # Check for post-purchase features
  post_purchase_patterns <- c("return", "refund", "satisfaction", "rating")
  feature_names <- names(data)
  
  for(pattern in post_purchase_patterns) {
    matching_features <- grep(pattern, feature_names, value = TRUE, ignore.case = TRUE)
    if(length(matching_features) > 0) {
      issues <- append(issues, paste("Potential post-purchase feature:", 
                                   paste(matching_features, collapse = ", ")))
    }
  }
  
  if(length(issues) > 0) {
    cat("Domain-specific warnings:\n")
    for(issue in issues) {
      cat("-", issue, "\n")
    }
  }
  
  return(base_report)
}

# Example e-commerce data
ecommerce_data <- data.frame(
  customer_id = 1:100,
  purchase_amount = rlnorm(100, 4, 1),
  product_category = sample(c("electronics", "books", "clothing"), 100, replace = TRUE),
  customer_satisfaction = sample(1:5, 100, replace = TRUE),  # Post-purchase!
  will_repurchase = factor(sample(c("yes", "no"), 100, replace = TRUE))
)

# Validate e-commerce data
ecommerce_report <- ecommerce_validation(ecommerce_data, "will_repurchase")