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