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bin/plot_clusters.py
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''' Take a file and plot its data onto a 2d or 3d axis depending on the data. ''' import os import numpy as np from sklearn.cluster import KMeans import matplotlib.pyplot as plt import argparse import json import pandas as pd # Defining useful functions ''' Read the file whose content is metas and vectors. Returns two numpy array : (metas, vectors) ''' def read_vector_file(filename, toy_version=False): vectors = np.empty((0, 1), np.float32) metas = np.empty((0, 4), np.float32) with open(filename, "r") as f: for i, line in enumerate(f): if toy_version == True and i > 100: break spl_line = line.split(" ") if(len(vectors) == 0): vectors = np.empty((0, len(spl_line[1:])), np.float32) metas = np.append( metas, np.asarray([spl_line[0].split(",")]), axis=0) vectors = np.append( vectors, np.asarray([spl_line[1:]], dtype=np.float32), axis=0) return (metas, vectors) ''' Check if the two given files have the same order. ''' def check_files(vector_file, cluster_file): with open(vector_file, "r") as f1, open(cluster_file, "r") as f2: for line1, line2 in zip(f1, f2): line1_str = line1.strip() line2_str = line2.strip() metas1 = line1_str.split(" ")[0].split(",") metas2 = line2_str.split(" ")[0].split(",") if(not metas1[0] == metas2[0] or not metas1[3] == metas2[3]): return False return True |
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from data import read_file, index_by_id |
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# Defining argparse parser = argparse.ArgumentParser(prog='Plotter', description='Plot a file of 2d ou 3d dimension') parser.add_argument('clusterfile', type=str, help='the path of the cluster file') parser.add_argument('vectorfile', type=str, help='the path of the vectors file') parser.add_argument('-o-', '--output', type=str, default='plot.pdf', help='the path of the ploted file') |
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args = parser.parse_args() # Editing global variable CLUSTERFILE_PATH=args.clusterfile VECTORFILE_PATH=args.vectorfile OUTFILE_PATH = args.output |
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data_vector = read_file(VECTORFILE_PATH) features = np.asarray([x[1] for x in data_vector]) features_T = np.transpose(features) data_cluster = read_file(CLUSTERFILE_PATH) data_cluster_ind = index_by_id(data_cluster) clusters = [data_cluster_ind[x[0][0]][x[0][3]][0][1] for x in data_vector] |
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# TODO: compute tsne file # TODO: adapt the script for the new library df = pd.DataFrame(dict( x=features_T[0], y=features_T[1], cluster=np.transpose(clusters)[0] )) exit(1) |
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# Get Vectors metas, vectors = read_vector_file(VECTORFILE_PATH, toy_version = TOY_VERSION) vectors_T = np.transpose(vectors) # Get Clusters metas, clusters = read_vector_file(CLUSTERFILE_PATH, toy_version = TOY_VERSION) #print(np.transpose(clusters)[0]) #print(np.transpose(metas)[0]) |
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groups = df.groupby('cluster') # Plot fig, ax = plt.subplots() for cluster, group in groups: ax.plot(group.x, group.y, marker='o', linestyle='', ms=2, label=cluster) ax.legend() plt.savefig(OUTFILE_PATH) |