sk_manifold: SK Manifold

In abresler/sklearn: sklearn port

SK Manifold

Description

Manifold learning is an approach to non-linear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high.

Usage

sk_manifold()

Details

• manifold functions

• manifold examples

Value

python object

Examples

library(sklearn)
library(tidyverse)
manifold <- sk_manifold()
data_sets <- sk_datasets()

## isomap
digits <- data_sets$load_digits(n_class = 6)
X <- digits$data %>% as.matrix()
y <- digits$target
n_neighbors <- 30L
n_features <- ncol(X) %>% as.integer()
n_samples <- nrow(X) %>% as.integer()
isomap <- manifold$Isomap(n_neighbors = n_neighbors, n_components = 2L)
model <- isomap$fit(X = X, y = y)

df <-
model$fit_transform(X = X) %>%
sk_tibble(c("iso1", "iso2")) %>%
mutate(digit = glue::glue("# {y}") %>% as.character())

df %>%
ggplot(aes(x = iso1, y= iso2, color = digit)) +
geom_jitter(size = .25) +
theme_minimal() +
ggtitle("MNIST Digit Isomap Embeddings")


### Linear Embedding
linear_embedding <- manifold$LocallyLinearEmbedding(n_neighbors = n_neighbors, n_components = 2L)

model <- linear_embedding$fit(X = X, y = y)
df_linear_embed <-
model$fit_transform(X = X) %>%
sk_tibble(c("linear1", "linear2")) %>%
mutate(digit = glue::glue("# {y}") %>% as.character())

df_linear_embed %>%
ggplot(aes(x = linear1, y= linear2, color = digit)) +
geom_jitter(size = .25) +
theme_minimal() +
ggtitle("MNIST Digit Linear Embeddings")


### MDS
clf <-  manifold$MDS(n_components = 2L,
n_init = 1L,
max_iter = 100L)

model <- clf$fit(X = X)

df_clf <-
model$fit_transform(X = X) %>%
sk_tibble(c("mds1", "mds2")) %>%
mutate(digit = glue::glue("# {y}") %>% as.character())

df_clf %>%
ggplot(aes(x = mds1, y= mds2, color = digit)) +
geom_jitter(size = .05) +
theme_minimal() +
ggtitle("MNIST Digit MDS Embeddings")

### Spectral Clustering

embedder <-
manifold$SpectralEmbedding(
n_components = 2L,
random_state = 0L,
eigen_solver = "arpack"
)

df_spectral <-
embedder$fit_transform(X = X) %>%
sk_tibble(c("spectral1", "spectral2")) %>%
mutate(digit = glue::glue("# {y}") %>% as.character())

df_spectral %>%
ggplot(aes(x = spectral1, y= spectral2, color = digit)) +
geom_jitter(size = .05) +
theme_minimal() +
ggtitle("MNIST Digit Spectral Clustering Embeddings")

## TSNE


tsne <- manifold$TSNE(n_components = 2L,
init = 'pca',
random_state = 0L)

df_tsne <-
tsne$fit_transform(X = X) %>%#'
sk_tibble(c("tsne1", "tsne2")) %>%
mutate(digit = glue::glue("# {y}") %>% as.character())

df_tsne %>%
ggplot(aes(x = tsne1, y= tsne2, color = digit)) +
geom_jitter(size = .05) +
theme_minimal() +
ggtitle("MNIST Digit T-SNE Embeddings")

abresler/sklearn documentation built on July 27, 2023, 7:07 a.m.