EntropyRegularizedLogisticRegressionSSLR: General Interface for EntropyRegularizedLogisticRegression...

In SSLR: Semi-Supervised Classification, Regression and Clustering Methods

Description

model from RSSL package R Implementation of entropy regularized logistic regression implementation as proposed by Grandvalet & Bengio (2005). An extra term is added to the objective function of logistic regression that penalizes the entropy of the posterior measured on the unlabeled examples.

Usage

EntropyRegularizedLogisticRegressionSSLR(
  lambda = 0,
  lambda_entropy = 1,
  intercept = TRUE,
  init = NA,
  scale = FALSE,
  x_center = FALSE
)

Arguments

lambda	l2 Regularization
lambda_entropy	Weight of the labeled observations compared to the unlabeled observations
intercept	logical; Whether an intercept should be included
init	Initial parameters for the gradient descent
scale	logical; Should the features be normalized? (default: FALSE)
x_center	logical; Should the features be centered?

References

Grandvalet, Y. & Bengio, Y., 2005. Semi-supervised learning by entropy minimization. In L. K. Saul, Y. Weiss, & L. Bottou, eds. Advances in Neural Information Processing Systems 17. Cambridge, MA: MIT Press, pp. 529-536.

Examples

library(tidyverse)
library(caret)
library(tidymodels)
library(SSLR)

data(breast)

set.seed(1)
train.index <- createDataPartition(breast$Class, p = .7, list = FALSE)
train <- breast[ train.index,]
test  <- breast[-train.index,]

cls <- which(colnames(breast) == "Class")

#% LABELED
labeled.index <- createDataPartition(breast$Class, p = .2, list = FALSE)
train[-labeled.index,cls] <- NA


m <- EntropyRegularizedLogisticRegressionSSLR() %>% fit(Class ~ ., data = train)


#Accuracy
predict(m,test) %>%
  bind_cols(test) %>%
  metrics(truth = "Class", estimate = .pred_class)

Example output

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
✔ ggplot2 3.3.2     ✔ purrr   0.3.4
✔ tibble  3.0.4     ✔ dplyr   1.0.2
✔ tidyr   1.1.2     ✔ stringr 1.4.0
✔ readr   1.4.0     ✔ forcats 0.5.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
Loading required package: lattice

Attaching package: ‘caret’

The following object is masked from ‘package:purrr’:

    lift

── Attaching packages ────────────────────────────────────── tidymodels 0.1.2 ──
✔ broom     0.7.2      ✔ recipes   0.1.15
✔ dials     0.0.9      ✔ rsample   0.0.8 
✔ infer     0.5.3      ✔ tune      0.1.2 
✔ modeldata 0.1.0      ✔ workflows 0.2.1 
✔ parsnip   0.1.4      ✔ yardstick 0.0.7 
── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
✖ scales::discard()        masks purrr::discard()
✖ dplyr::filter()          masks stats::filter()
✖ recipes::fixed()         masks stringr::fixed()
✖ dplyr::lag()             masks stats::lag()
✖ caret::lift()            masks purrr::lift()
✖ yardstick::precision()   masks caret::precision()
✖ yardstick::recall()      masks caret::recall()
✖ yardstick::sensitivity() masks caret::sensitivity()
✖ yardstick::spec()        masks readr::spec()
✖ yardstick::specificity() masks caret::specificity()
✖ recipes::step()          masks stats::step()
# A tibble: 2 x 3
  .metric  .estimator .estimate
  <chr>    <chr>          <dbl>
1 accuracy binary        0.706 
2 kap      binary        0.0899
Warning message:
In matrix(object@w, nrow = ncol(X)) :
  data length [34] is not a sub-multiple or multiple of the number of rows [35]

SSLR documentation built on July 22, 2021, 9:08 a.m.