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