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Introduction to R package unifiedml

library(unifiedml) # this package

## Loading required package: doParallel

## Loading required package: foreach

## Loading required package: iterators

## Loading required package: parallel

## Loading required package: R6

library(glmnet)

## Loading required package: Matrix

## Loaded glmnet 4.1-10

library(randomForest)

## randomForest 4.7-1.2

## Type rfNews() to see new features/changes/bug fixes.

library(e1071)

# ------------------------------------------------------------
# REGRESSION EXAMPLES
# ------------------------------------------------------------

cat("\n=== REGRESSION EXAMPLES ===\n\n")

## 
## === REGRESSION EXAMPLES ===

# Example 1: Synthetic data (numeric y → automatic regression)
set.seed(123)
X <- MASS::Boston[, -ncol(MASS::Boston)]
y <- MASS::Boston$medv

# glmnet regression
cat("1. Ridge Regression (glmnet) - Auto-detected: Regression\n")

## 1. Ridge Regression (glmnet) - Auto-detected: Regression

mod1 <- Model$new(glmnet::glmnet)  # No task parameter needed!
mod1$fit(X, y, alpha = 0, lambda = 0.1)
mod1$print()

## Model Object
## ------------
## Model function: self$model_fn 
## Fitted: TRUE 
## Task: regression 
## Training samples: 506 
## Features: 13

cat("\nPredictions:\n")

## 
## Predictions:

print(head(mod1$predict(X)))

##        1        2        3        4        5        6 
## 30.12476 25.01360 30.57030 28.68765 28.04710 25.31151

cat("\n")

mod1$summary()

## 
## Model Summary - Numerical Derivatives
## ======================================
## Task: regression 
## Samples: 506 | Features: 13 
## Step size (h): 0.01 
## 
##  Feature Mean_Derivative    Std_Error       t_value p_value Significance
##     crim   -1.032452e-01 7.837905e-15 -1.317256e+13       0          ***
##       zn    4.322719e-02 5.216968e-15  8.285883e+12       0          ***
##    indus    2.743581e-03 1.273890e-14  2.153704e+11       0          ***
##     chas    2.753495e+00 6.050646e-15  4.550745e+14       0          ***
##      nox   -1.656232e+01 1.293028e-14 -1.280894e+15       0          ***
##       rm    3.868607e+00 9.300418e-15  4.159605e+14       0          ***
##      age   -4.129908e-04 1.108270e-14 -3.726444e+10       0          ***
##      dis   -1.411492e+00 4.776813e-15 -2.954882e+14       0          ***
##      rad    2.655385e-01 7.851465e-15  3.382025e+13       0          ***
##      tax   -1.038490e-02 8.323530e-15 -1.247656e+12       0          ***
##  ptratio   -9.325559e-01 6.254096e-15 -1.491112e+14       0          ***
##    black    9.272792e-03 3.489110e-15  2.657638e+12       0          ***
##    lstat   -5.149643e-01 4.667193e-15 -1.103371e+14       0          ***
## 
## Significance codes: 0 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1

mod1$plot(feature = 1)

(cv1 <- cross_val_score(mod1, X, y, cv = 5L))  # Auto-uses RMSE

##   |                                                                              |                                                                      |   0%  |                                                                              |==============                                                        |  20%  |                                                                              |============================                                          |  40%  |                                                                              |==========================================                            |  60%  |                                                                              |========================================================              |  80%  |                                                                              |======================================================================| 100%

## [1] 5.255488 6.050598 6.147428 5.054657 4.942869

cat("\nMean RMSE:", mean(cv1), "\n\n")

## 
## Mean RMSE: 5.490208

# randomForest regression
cat("\n2. Random Forest Regression - Auto-detected: Regression\n")

## 
## 2. Random Forest Regression - Auto-detected: Regression

mod2 <- Model$new(randomForest::randomForest)  # No task parameter!
mod2$fit(X, y, ntree = 50)
mod2$print()

## Model Object
## ------------
## Model function: self$model_fn 
## Fitted: TRUE 
## Task: regression 
## Training samples: 506 
## Features: 13

cat("\n")

mod2$summary(h = 0.01)

## 
## Model Summary - Numerical Derivatives
## ======================================
## Task: regression 
## Samples: 506 | Features: 13 
## Step size (h): 0.01 
## 
##  Feature Mean_Derivative   Std_Error    t_value      p_value Significance
##     crim      0.27808959 0.329662424  0.8435587 3.993155e-01             
##       zn      0.00000000 0.000000000        NaN          NaN         <NA>
##    indus      0.03961792 0.030839187  1.2846616 1.994995e-01            *
##     chas      0.00000000 0.000000000        NaN          NaN         <NA>
##      nox     -7.59855104 1.939451552 -3.9178865 1.016265e-04          ***
##       rm      4.33226614 0.538906676  8.0389914 6.471192e-15          ***
##      age     -0.02569829 0.023089867 -1.1129682 2.662516e-01             
##      dis     -0.71310569 0.405677973 -1.7578122 7.938528e-02           **
##      rad      0.01739130 0.017453435  0.9964402 3.195135e-01             
##      tax     -0.01361660 0.008303022 -1.6399573 1.016368e-01            *
##  ptratio     -0.03075099 0.024143264 -1.2736881 2.033599e-01             
##    black      0.05649539 0.045334095  1.2462009 2.132684e-01             
##    lstat     -0.47499012 0.124407440 -3.8180202 1.512437e-04          ***
## 
## Significance codes: 0 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1

# ------------------------------------------------------------
# CLASSIFICATION EXAMPLES
# ------------------------------------------------------------

cat("\n\n=== CLASSIFICATION EXAMPLES ===\n\n")

## 
## 
## === CLASSIFICATION EXAMPLES ===

# Example: Iris dataset (factor y → automatic classification)
data(iris)

# Binary classification with factor
cat("3. Binary Classification with Factor Response\n")

## 3. Binary Classification with Factor Response

iris_binary <- iris[iris$Species %in% c("setosa", "versicolor"), ]
X_binary <- as.matrix(iris_binary[, 1:4])
y_binary <- iris_binary$Species  # factor → classification

# Multi-class classification
cat("4. Multi-class Classification\n")

## 4. Multi-class Classification

X_multi <- as.matrix(iris[, 1:4])
y_multi <- iris$Species  # factor with 3 levels → multi-class classification

mod4 <- Model$new(randomForest::randomForest)  # No task parameter!
mod4$fit(X_multi, y_multi, ntree = 50)
mod4$print()

## Model Object
## ------------
## Model function: self$model_fn 
## Fitted: TRUE 
## Task: classification 
## Training samples: 150 
## Features: 4 
## Classes: setosa, versicolor, virginica 
## Class distribution:
## 
##     setosa versicolor  virginica 
##         50         50         50

(cv4 <- cross_val_score(mod4, X_multi, y_multi, cv = 5L))  # Auto-uses accuracy

##   |                                                                              |                                                                      |   0%  |                                                                              |==============                                                        |  20%  |                                                                              |============================                                          |  40%  |                                                                              |==========================================                            |  60%  |                                                                              |========================================================              |  80%  |                                                                              |======================================================================| 100%

## [1] 0.9666667 0.9333333 1.0000000 0.9666667 0.9333333

cat("\nMean Accuracy:", mean(cv4), "\n")

## 
## Mean Accuracy: 0.96

y_multi_numeric <- as.numeric(y_multi)
mod4 <- Model$new(glmnet::glmnet)  # No task parameter!
mod4$fit(X_multi, y_multi_numeric, family="multinomial")
mod4$print()

## Model Object
## ------------
## Model function: self$model_fn 
## Fitted: TRUE 
## Task: regression 
## Training samples: 150 
## Features: 4

(cv4 <- cross_val_score(mod4, X_multi, y_multi_numeric, cv = 5L))  # Auto-uses accuracy

##   |                                                                              |                                                                      |   0%  |                                                                              |==============                                                        |  20%  |                                                                              |============================                                          |  40%  |                                                                              |==========================================                            |  60%  |                                                                              |========================================================              |  80%  |                                                                              |======================================================================| 100%

## [1] 0.3186251 0.3500136 0.3191357 0.2776998 0.3485864

cat("\nMean Accuracy:", mean(cv4), "\n")

## 
## Mean Accuracy: 0.3228121

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