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bin/tsne_pvector.py
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''' The goal of this script is to display calculate tsne of pvectors. ''' import os import argparse import numpy as np from sklearn.manifold import TSNE # Defining argparse parser = argparse.ArgumentParser(prog='pvector tsne', description='Calculate the tsne representation of pvector in 3 or 2d') parser.add_argument('filepath', type=str, help='the path of the file you want to calculate tsne') parser.add_argument('-o', '--output', type=str, default='.', help='the path of the output file.') parser.add_argument('-n', '--n-comp', type=int, choices=[2, 3], default='2', help='number of components output of tsne') parser.add_argument('-t', '--toy', action='store_true', help='test the script on a toy example. Do not test all the file content.') args = parser.parse_args() # Editing global variable FILE_PATH=args.filepath OUTFILE_PATH=args.output TOY_VERSION=args.toy N_COMP=args.n_comp # Defining pvectors with default number of column pvectors = np.empty((0, 64), np.float32) metas = np.empty((0, 4), np.float32) # READ DATA with open(os.path.join(FILE_PATH), "r") as f: for i, line in enumerate(f): if TOY_VERSION == True and i > 100: break spl_line = line.split(" ") if(len(pvectors) == 0): pvectors = np.empty((0, len(spl_line[1:])), np.float32) metas = np.append( metas, np.asarray([spl_line[0].split(",")]), axis=0) pvectors = np.append( pvectors, np.asarray([spl_line[1:]], dtype=np.float32), axis=0) # PREPARE SAVE FILE FUNCTION def save_file(filepath, metas, values): with open(filepath, "w") as f: for i, value in enumerate(values): metas_str = ",".join(str(v) for v in metas[i]) try: infos_str = " ".join(str(v) for v in values[i]) except TypeError as te: infos_str = str(values[i]) f.write(metas_str + " " + infos_str + " ") # CALCULATE T-SNE X_embedded = TSNE(n_components=N_COMP).fit_transform(pvectors) save_file(OUTFILE_PATH, metas, X_embedded) |