# Integration Workflows: Combining Supervised and Unsupervised Learning

    library(tidylearn)
    library(dplyr)
    library(ggplot2)

## Introduction

This vignette showcases tidylearn’s capability to seamlessly integrate supervised and unsupervised learning in multi-step workflows.

Important: These integration functions orchestrate the wrapped packages (stats, glmnet, randomForest, cluster, etc.) rather than implementing new algorithms. tidylearn provides the workflow coordination, while the underlying packages do the computational work. Access raw model objects via `model$fit`.

## Dimensionality Reduction as Preprocessing

Use PCA or MDS to reduce feature space before supervised learning.

### Basic Usage

    # Reduce dimensions before classification
    reduced <- tl_reduce_dimensions(iris,
                                    response = "Species",
                                    method = "pca",
                                    n_components = 3)

    # Inspect reduced data
    head(reduced$data)
    #> # A tibble: 6 × 4
    #>     PC1    PC2     PC3 Species
    #>   <dbl>  <dbl>   <dbl> <fct>
    #> 1 -2.26 -0.478  0.127  setosa
    #> 2 -2.07  0.672  0.234  setosa
    #> 3 -2.36  0.341 -0.0441 setosa
    #> 4 -2.29  0.595 -0.0910 setosa
    #> 5 -2.38 -0.645 -0.0157 setosa
    #> 6 -2.07 -1.48  -0.0269 setosa

    # Train classifier on reduced features.
    # PCA output includes a .obs_id row-identifier column — drop it before
    # modeling so it isn't treated as a predictor in the formula.
    reduced_data <- reduced$data %>% select(-starts_with(".obs"))
    model_reduced <- tl_model(reduced_data, Species ~ ., method = "logistic")
    #> Warning: glm.fit: algorithm did not converge
    #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    print(model_reduced)
    #> tidylearn Model
    #> ===============
    #> Paradigm: supervised
    #> Method: logistic
    #> Task: Classification
    #> Formula: Species ~ .
    #>
    #> Training observations: 150

    # Make predictions
    preds <- predict(model_reduced)
    accuracy <- mean(preds$.pred == iris$Species)
    cat("Accuracy with PCA features:", round(accuracy * 100, 1), "%\n")
    #> Accuracy with PCA features: 0 %

### Comparison: Original vs Reduced Features

    # Split data for fair comparison
    split <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 123)

    # Model with original features
    model_original <- tl_model(split$train, Species ~ ., method = "logistic")
    #> Warning: glm.fit: algorithm did not converge
    #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    preds_original <- predict(model_original, new_data = split$test)
    acc_original <- mean(preds_original$.pred == split$test$Species)

    # Model with PCA features
    reduced_train <- tl_reduce_dimensions(split$train,
                                          response = "Species",
                                          method = "pca",
                                          n_components = 3)

    # Remove .obs_id column before modeling (it's just an identifier)
    reduced_train_data <- reduced_train$data %>% select(-starts_with(".obs"))
    model_pca <- tl_model(reduced_train_data, Species ~ ., method = "logistic")
    #> Warning: glm.fit: algorithm did not converge
    #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

    # Need to transform test data using same PCA
    test_predictors <- split$test %>% select(-Species)
    test_transformed <- predict(
      reduced_train$reduction_model,
      new_data = test_predictors
    )
    test_transformed$Species <- split$test$Species
    test_transformed <- test_transformed %>% select(-starts_with(".obs"))

    preds_pca <- predict(model_pca, new_data = test_transformed)
    acc_pca <- mean(preds_pca$.pred == split$test$Species)

    # Compare results
    cat("Original features (4):", round(acc_original * 100, 1), "%\n")
    #> Original features (4): 0 %
    cat("PCA features (3):", round(acc_pca * 100, 1), "%\n")
    #> PCA features (3): 0 %
    cat("Feature reduction:", round((1 - 3 / 4) * 100, 1), "%\n")
    #> Feature reduction: 25 %

## Cluster-Based Feature Engineering

Add cluster assignments as features to boost supervised learning performance.

### Adding Cluster Features

    # Add cluster features
    data_clustered <- tl_add_cluster_features(iris,
                                              response = "Species",
                                              method = "kmeans",
                                              k = 3)

    # Check new features
    names(data_clustered)
    #> [1] "Sepal.Length"   "Sepal.Width"    "Petal.Length"   "Petal.Width"
    #> [5] "Species"        "cluster_kmeans"

    # Train model with cluster features
    model_cluster <- tl_model(data_clustered, Species ~ ., method = "forest")
    print(model_cluster)
    #> tidylearn Model
    #> ===============
    #> Paradigm: supervised
    #> Method: forest
    #> Task: Classification
    #> Formula: Species ~ .
    #>
    #> Training observations: 150

### Performance Comparison

    # Compare models with and without cluster features
    split_comp <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 42)

    # Without cluster features
    model_no_cluster <- tl_model(split_comp$train, Species ~ ., method = "forest")
    preds_no_cluster <- predict(model_no_cluster, new_data = split_comp$test)
    acc_no_cluster <- mean(preds_no_cluster$.pred == split_comp$test$Species)

    # With cluster features
    train_clustered <- tl_add_cluster_features(split_comp$train,
                                               response = "Species",
                                               method = "kmeans",
                                               k = 3)
    model_with_cluster <- tl_model(train_clustered, Species ~ ., method = "forest")

    # Need to get cluster model for test data
    cluster_model <- attr(train_clustered, "cluster_model")
    test_clusters <- predict(cluster_model, new_data = split_comp$test[, -5])
    test_clustered <- split_comp$test
    test_clustered$cluster_kmeans <- as.factor(test_clusters$cluster)

    preds_with_cluster <- predict(model_with_cluster, new_data = test_clustered)
    acc_with_cluster <- mean(preds_with_cluster$.pred == split_comp$test$Species)

    cat("Without cluster features:", round(acc_no_cluster * 100, 1), "%\n")
    #> Without cluster features: 93.3 %
    cat("With cluster features:", round(acc_with_cluster * 100, 1), "%\n")
    #> With cluster features: 93.3 %

## Semi-Supervised Learning

Train models with limited labels using cluster-based label propagation.

### Training with Limited Labels

    # Use only 10% of labels
    set.seed(123)
    labeled_indices <- sample(nrow(iris), size = 15)  # Only 15 out of 150 labeled!

    # Train semi-supervised model
    model_semi <- tl_semisupervised(iris, Species ~ .,
                                    labeled_indices = labeled_indices,
                                    cluster_method = "kmeans",
                                    supervised_method = "logistic")
    #> Warning: glm.fit: algorithm did not converge
    #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

    print(model_semi)
    #> tidylearn Model
    #> ===============
    #> Paradigm: supervised
    #> Method: logistic
    #> Task: Classification
    #> Formula: Species ~ .
    #>
    #> Training observations: 150

    # Check how labels were propagated
    label_mapping <- model_semi$semisupervised_info$label_mapping
    print(label_mapping)
    #> # A tibble: 3 × 2
    #>   cluster cluster_label
    #>     <int> <chr>
    #> 1       1 virginica
    #> 2       2 versicolor
    #> 3       3 setosa

    # Evaluate performance
    preds_semi <- predict(model_semi)
    accuracy_semi <- mean(preds_semi$.pred == iris$Species)

    cat("Accuracy with only", length(labeled_indices), "labels:",
        round(accuracy_semi * 100, 1), "%\n")
    #> Accuracy with only 15 labels: 0 %
    labeled_pct <- round(
      length(labeled_indices) / nrow(iris) * 100, 1
    )
    cat("Proportion of data labeled:", labeled_pct, "%\n")
    #> Proportion of data labeled: 10 %

### Comparison: Semi-Supervised vs Fully Supervised

    # Fully supervised with same amount of data
    labeled_data <- iris[labeled_indices, ]
    model_full <- tl_model(labeled_data, Species ~ ., method = "logistic")
    #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    preds_full <- predict(model_full, new_data = iris)
    accuracy_full <- mean(preds_full$.pred == iris$Species)

    cat("Fully supervised (15 samples):", round(accuracy_full * 100, 1), "%\n")
    #> Fully supervised (15 samples): 0 %
    cat("Semi-supervised (15 labels + propagation):",
        round(accuracy_semi * 100, 1), "%\n")
    #> Semi-supervised (15 labels + propagation): 0 %

## Anomaly-Aware Modeling

Detect and handle outliers before supervised learning.

### Flagging Anomalies

    # Flag anomalies as a feature
    model_anomaly_flag <- tl_anomaly_aware(iris, Species ~ .,
                                           response = "Species",
                                           anomaly_method = "dbscan",
                                           action = "flag",
                                           supervised_method = "logistic")

    # Check anomaly info
    cat("Anomalies detected:", model_anomaly_flag$anomaly_info$n_anomalies, "\n")

### Removing Anomalies

    # Remove anomalies before training
    model_anomaly_remove <- tl_anomaly_aware(iris, Species ~ .,
                                             response = "Species",
                                             anomaly_method = "dbscan",
                                             action = "remove",
                                             supervised_method = "logistic")

    cat("Anomalies removed:", model_anomaly_remove$anomalies_removed, "\n")

## Stratified Models

Create cluster-specific models for heterogeneous data.

### Training Stratified Models

    # Train separate models for different clusters
    stratified_models <- tl_stratified_models(mtcars, mpg ~ .,
                                              cluster_method = "kmeans",
                                              k = 3,
                                              supervised_method = "linear")
    #> Note: Response 'mpg' has 6 unique numeric values. Treating as regression. Convert to factor for classification.
    #> Note: Response 'mpg' has 8 unique numeric values. Treating as regression. Convert to factor for classification.

    # Check structure
    names(stratified_models)
    #> [1] "cluster_model"     "supervised_models" "formula"
    #> [4] "data"
    length(stratified_models$supervised_models)
    #> [1] 3

    # Predictions using stratified models
    preds_stratified <- predict(stratified_models)
    head(preds_stratified)
    #> # A tibble: 6 × 2
    #>   .pred .cluster
    #>   <dbl>    <int>
    #> 1  20.7        2
    #> 2  20.5        2
    #> 3  24.7        2
    #> 4  21.4        1
    #> 5  19.2        3
    #> 6  18.1        1

    # Calculate RMSE
    rmse_stratified <- sqrt(mean((preds_stratified$.pred - mtcars$mpg)^2))
    cat("Stratified Model RMSE:", round(rmse_stratified, 2), "\n")
    #> Stratified Model RMSE: 1.06

    # Compare with single model
    model_single <- tl_model(mtcars, mpg ~ ., method = "linear")
    preds_single <- predict(model_single)
    rmse_single <- sqrt(mean((preds_single$.pred - mtcars$mpg)^2))
    cat("Single Model RMSE:", round(rmse_single, 2), "\n")
    #> Single Model RMSE: 2.15

## Complete Integration Workflow

Combining multiple integration techniques:

    # Step 1: Split data
    workflow_split <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 42)

    # Step 2: Reduce dimensions
    workflow_reduced <- tl_reduce_dimensions(workflow_split$train,
                                             response = "Species",
                                             method = "pca",
                                             n_components = 3)

    # Step 3: Add cluster features to reduced data (remove .obs_id first)
    workflow_reduced_clean <- workflow_reduced$data %>% select(-starts_with(".obs"))
    workflow_clustered <- tl_add_cluster_features(workflow_reduced_clean,
                                                  response = "Species",
                                                  method = "kmeans",
                                                  k = 3)

    # Step 4: Train final model
    workflow_model <- tl_model(workflow_clustered, Species ~ ., method = "forest")

    print(workflow_model)
    #> tidylearn Model
    #> ===============
    #> Paradigm: supervised
    #> Method: forest
    #> Task: Classification
    #> Formula: Species ~ .
    #>
    #> Training observations: 105

    # Transform test data through same pipeline
    # 1. Apply PCA transformation
    test_pca <- predict(workflow_reduced$reduction_model,
                        new_data = workflow_split$test[, -5])
    test_pca$Species <- workflow_split$test$Species

    # 2. Get cluster assignments
    cluster_model_wf <- attr(workflow_clustered, "cluster_model")
    test_clusters_wf <- predict(cluster_model_wf,
                                new_data = test_pca[, grep("PC", names(test_pca))])
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    test_pca$cluster_kmeans <- as.factor(test_clusters_wf$cluster)

    # 3. Predict
    workflow_preds <- predict(workflow_model, new_data = test_pca)
    workflow_accuracy <- mean(workflow_preds$.pred == workflow_split$test$Species)

    cat("Complete Workflow Accuracy:", round(workflow_accuracy * 100, 1), "%\n")
    #> Complete Workflow Accuracy: 88.9 %

## Practical Example: Credit Risk Assessment

    # Simulate credit data
    set.seed(42)
    n <- 500
    credit_data <- data.frame(
      age = rnorm(n, 40, 12),
      income = rnorm(n, 50000, 20000),
      debt_ratio = runif(n, 0, 0.5),
      credit_score = rnorm(n, 700, 100),
      years_employed = rpois(n, 5)
    )

    # Create target variable (default risk)
    credit_data$default <- factor(
      ifelse(
        credit_data$debt_ratio > 0.4 & credit_data$credit_score < 650,
        "Yes", "No"
      )
    )

    # Split data
    credit_split <- tl_split(
      credit_data, prop = 0.7, stratify = "default", seed = 123
    )

    # Strategy 1: Add customer segments as features
    credit_clustered <- tl_add_cluster_features(credit_split$train,
                                                response = "default",
                                                method = "kmeans",
                                                k = 4)

    model_credit <- tl_model(credit_clustered, default ~ ., method = "forest")

    # Transform test data
    cluster_model_credit <- attr(credit_clustered, "cluster_model")
    test_clusters_credit <- predict(cluster_model_credit,
                                    new_data = credit_split$test[, -6])
    test_credit <- credit_split$test
    test_credit$cluster_kmeans <- as.factor(test_clusters_credit$cluster)

    preds_credit <- predict(model_credit, new_data = test_credit)
    accuracy_credit <- mean(preds_credit$.pred == credit_split$test$default)

    cat("Credit Risk Model Accuracy:", round(accuracy_credit * 100, 1), "%\n")
    #> Credit Risk Model Accuracy: 98.7 %

## Key Advantages of Integration

1. Better Performance: Combining techniques often outperforms individual approaches
2. Feature Engineering: Unsupervised methods create informative features
3. Data Efficiency: Semi-supervised learning uses unlabeled data effectively
4. Robustness: Anomaly detection improves model reliability
5. Interpretability: Cluster-specific models are easier to understand

## Best Practices

1. Always transform test data using the same unsupervised models as training data
2. Experiment with different combinations to find the best approach
3. Use semi-supervised learning when labels are expensive to obtain
4. Consider stratified models for heterogeneous datasets
5. Validate performance on held-out test data

## Summary

tidylearn’s integration functions provide powerful ways to combine supervised and unsupervised learning:

- `tl_reduce_dimensions()`: Use PCA/MDS as preprocessing
- `tl_add_cluster_features()`: Engineer features from clusters
- `tl_semisupervised()`: Train with limited labels
- `tl_anomaly_aware()`: Handle outliers intelligently
- `tl_stratified_models()`: Create cluster-specific models

These functions orchestrate the underlying packages (stats, cluster, glmnet, randomForest, etc.) to enable multi-step workflows with a consistent interface.

    # Final integrated example
    final_data <- iris
    final_split <- tl_split(
      final_data, prop = 0.7, stratify = "Species", seed = 999
    )

    # Combine PCA + clustering
    final_reduced <- tl_reduce_dimensions(final_split$train,
                                          response = "Species",
                                          method = "pca",
                                          n_components = 3)
    # Remove .obs_id column before clustering
    final_reduced_clean <- final_reduced$data %>% select(-starts_with(".obs"))
    final_clustered <- tl_add_cluster_features(final_reduced_clean,
                                               response = "Species",
                                               method = "kmeans",
                                               k = 3)
    final_model <- tl_model(final_clustered, Species ~ ., method = "logistic")
    #> Warning: glm.fit: algorithm did not converge

    cat("Final integrated model created successfully!\n")
    #> Final integrated model created successfully!
    print(final_model)
    #> tidylearn Model
    #> ===============
    #> Paradigm: supervised
    #> Method: logistic
    #> Task: Classification
    #> Formula: Species ~ .
    #>
    #> Training observations: 105