import numpy as np

import pandas as pd

from sklearn import decomposition

from sklearn.manifold import TSNE

import umap

print('Loading data...')

input_path = 'data/PANCAN/GDC-PANCAN_'

sample_id = np.loadtxt(input_path + 'both_samples.tsv', delimiter='\t', dtype='str')

input_df = pd.read_csv(input_path + 'preprocessed_both.tsv', sep='\t', header=0, index_col=0)

input_df = input_df.T

latent_space_dimension = 2

# PCA

print('PCA')

pca = decomposition.PCA(n_components=latent_space_dimension)

z = pca.fit_transform(input_df.values)

latent_code = pd.DataFrame(z, index=sample_id)

output_path = 'results/GDC-PANCAN_' + str(latent_space_dimension) + 'D_PCA_latent_sapce.tsv'

latent_code.to_csv(output_path, sep='\t')

# KPCA

print('KPCA')

kpca = decomposition.KernelPCA(n_components=latent_space_dimension, kernel='rbf')

z = kpca.fit_transform(input_df.values)

latent_code = pd.DataFrame(z, index=sample_id)

output_path = 'results/GDC-PANCAN_' + str(latent_space_dimension) + 'D_KPCA_latent_sapce.tsv'

latent_code.to_csv(output_path, sep='\t')

# TSNE

print('TSNE')

tsne = TSNE(n_components=latent_space_dimension)

z = tsne.fit_transform(input_df.values)

latent_code = pd.DataFrame(z, index=sample_id)

output_path = 'results/GDC-PANCAN_' + str(latent_space_dimension) + 'D_TSNE_latent_sapce.tsv'

latent_code.to_csv(output_path, sep='\t')

# UMAP

print('UMAP')

umap_reducer = umap.UMAP()

z = umap_reducer.fit_transform(input_df.values)

latent_code = pd.DataFrame(z, index=sample_id)

output_path = 'results/GDC-PANCAN_' + str(latent_space_dimension) + 'D_UMAP_latent_sapce.tsv'

latent_code.to_csv(output_path, sep='\t')