42 lines
1.1 KiB
Python
Executable File
42 lines
1.1 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from sklearn.decomposition import PCA
|
|
from sklearn.manifold import TSNE
|
|
|
|
import matplotlib as mpl
|
|
mpl.use('Agg')
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.cm as cm
|
|
plt.style.use('bmh')
|
|
|
|
import os
|
|
import sys
|
|
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('workDir', type=str)
|
|
parser.add_argument('--names', type=str, nargs='+', required=True)
|
|
args = parser.parse_args()
|
|
|
|
y = pd.read_csv("{}/labels.csv".format(args.workDir)).as_matrix()[:, 0]
|
|
X = pd.read_csv("{}/reps.csv".format(args.workDir)).as_matrix()
|
|
|
|
target_names = np.array(args.names)
|
|
colors = cm.gnuplot2(np.linspace(0, 0.7, len(target_names)))
|
|
|
|
X_pca = PCA(n_components=50).fit_transform(X, X)
|
|
tsne = TSNE(n_components=2, init='random', random_state=0)
|
|
X_r = tsne.fit_transform(X_pca)
|
|
|
|
for c, i, target_name in zip(colors,
|
|
list(range(1, len(target_names) + 1)),
|
|
target_names):
|
|
plt.scatter(X_r[y == i, 0], X_r[y == i, 1],
|
|
c=c, label=target_name)
|
|
plt.legend()
|
|
plt.savefig("{}/tsne.pdf".format(args.workDir))
|