import logging import sys import traceback import operator import numpy as np import random from .model import load_model from .encode import load_and_encode, parse_target try: from sklearn import decomposition import matplotlib.pyplot as plt except ImportError: decomposition = None plt = None logger = logging.getLogger("Slither-simil") def plot(args): if decomposition is None or plt is None: logger.error("ERROR: In order to use plot mode in slither-simil, you need to install sklearn and matplotlib:") logger.error("$ pip3 install sklearn matplotlib --user") sys.exit(-1) try: model = args.model model = load_model(model) filename = args.filename #contract = args.contract contract, fname = parse_target(args.fname) #solc = args.solc infile = args.input #ext = args.filter #nsamples = args.nsamples if fname is None or infile is None: logger.error('The plot mode requieres fname and input parameters.') sys.exit(-1) logger.info('Loading data..') cache = load_and_encode(infile, **vars(args)) data = list() fs = list() logger.info('Procesing data..') for (f,c,n),y in cache.items(): if (c == contract or contract is None) and n == fname: fs.append(f) data.append(y) if len(data) == 0: logger.error('No contract was found with function %s', fname) sys.exit(-1) data = np.array(data) pca = decomposition.PCA(n_components=2) tdata = pca.fit_transform(data) logger.info('Plotting data..') plt.figure(figsize=(20,10)) assert(len(tdata) == len(fs)) for ([x,y],l) in zip(tdata, fs): x = random.gauss(0, 0.01) + x y = random.gauss(0, 0.01) + y plt.scatter(x, y, c='blue') plt.text(x-0.001,y+0.001, l) logger.info('Saving figure to plot.png..') plt.savefig('plot.png', bbox_inches='tight') except Exception: logger.error('Error in %s' % args.filename) logger.error(traceback.format_exc()) sys.exit(-1)