def getBayesModel(G, p, mixPrior=None): """ Constructs a PWM CSI BayesMixtureModel. @param G: number of components @param p: number of positions of the binding site @return: BayesMixtureModel object """ if not mixPrior: piPrior = mixture.DirichletPrior(G, [1.0] * G) compPrior = [] for i in range(p): compPrior.append( mixture.DirichletPrior(4, [1.02, 1.02, 1.02, 1.02])) # arbitrary values of struct and comp parameters. Values should be # reset by user using the structPriorHeuristic method. mixPrior = mixture.MixtureModelPrior(0.05, 0.05, piPrior, compPrior) DNA = mixture.Alphabet(['A', 'C', 'G', 'T']) comps = [] for i in range(G): dlist = [] for j in range(p): phi = mixture.random_vector(4) dlist.append(mixture.DiscreteDistribution(4, phi, DNA)) comps.append(mixture.ProductDistribution(dlist)) pi = mixture.random_vector(G) m = mixture.BayesMixtureModel(G, pi, comps, mixPrior, struct=1) return m
def getModel(G, p): """ Constructs a PWM MixtureModel. @param G: number of components @param p: number of positions of the binding site @return: MixtureModel object """ DNA = mixture.Alphabet(['A', 'C', 'G', 'T']) comps = [] for i in range(G): dlist = [] for j in range(p): phi = mixture.random_vector(4) dlist.append(mixture.DiscreteDistribution(4, phi, DNA)) comps.append(mixture.ProductDistribution(dlist)) pi = mixture.random_vector(G) m = mixture.MixtureModel(G, pi, comps) return m
def getBackgroundModel(p, dist=None): """ Construct background model @param p: number of positions of the binding site @param dist: background nucleotide frequencies, uniform is default @return: MixtureModel representing the background """ DNA = mixture.Alphabet(['A', 'C', 'G', 'T']) dlist = [] if dist == None: phi = [0.25] * 4 else: phi = dist for j in range(p): dlist.append(mixture.DiscreteDistribution(4, phi, DNA)) comps = [mixture.ProductDistribution(dlist)] m = mixture.MixtureModel(1, [1.0], comps) return m
def getRandomMixture(G, p, KL_lower, KL_upper, dtypes='discgauss', M=4, seed=None): # if seed: # random.seed(seed) # mixture._C_mixextend.set_gsl_rng_seed(seed) # #print '*** seed=',seed # # else: # XXX debug # seed = random.randint(1,9000000) # mixture._C_mixextend.set_gsl_rng_seed(seed) # random.seed(seed) # #print '*** seed=',seed #M = 4 # Alphabet size for discrete distributions min_sigma = 0.1 # minimal std for Normal max_sigma = 1.0 # maximal std for Normal min_mu = -5.0 # minimal mean max_mu = 8.0 # maximal mean if dtypes == 'disc': featureTypes = [0] * p elif dtypes == 'gauss': featureTypes = [1] * p elif dtypes == 'discgauss': # discrete or Normal features for now, chosen uniformly # 0 discrete, 1 Normal featureTypes = [random.choice((0, 1)) for i in range(p)] else: raise TypeError #print featureTypes C = [] for j in range(p): c_j = [] for i in range(G): #print i,j if featureTypes[j] == 0: acc = 0 while acc == 0: cand = mixture.DiscreteDistribution( M, mixture.random_vector(M)) #print 'cand:',cand acc = 1 for d in c_j: KL_dist = mixture.sym_kl_dist(d, cand) if KL_dist > KL_upper or KL_dist < KL_lower: #print ' *', cand, 'rejected:', d , KL_dist acc = 0 break c_j.append(cand) elif featureTypes[j] == 1: acc = 0 while acc == 0: mu = random.uniform(min_mu, max_mu) sigma = random.uniform(min_sigma, max_sigma) cand = mixture.NormalDistribution(mu, sigma) #print 'cand:',cand acc = 1 for d in c_j: KL_dist = mixture.sym_kl_dist(d, cand) if KL_dist > KL_upper or KL_dist < KL_lower: #print ' *', cand, 'rejected:', d , KL_dist acc = 0 c_j.append(cand) else: RuntimeError C.append(c_j) # print '\n' # for cc in C: # print cc comps = [] for i in range(G): comps.append(mixture.ProductDistribution([C[j][i] for j in range(p)])) pi = get_random_pi(G, 0.1) m = mixture.MixtureModel(G, pi, comps, struct=1) m.updateFreeParams() return m
def plotKLDistance(ref_dist, objf='sym' ,title='KL Distance', show=True): assert ref_dist.M == 3, 'Only 3 dimensions for now.' # KL distance to be used, either symmetric or the two directions # with respect to ref_dist assert objf in ['sym', 'leftToRight', 'rightToLeft'] # A 2-simplex lives in 3-space. dimension = 3 # XXX dimension fixed to 3 for now # These are the vertex labels, converted to strings. labels = numpy.eye(dimension, dtype=int) labels = map(str, map(tuple, labels)) # Let's create the simplex. simplex = Simplex2D(labels, modify_labels=False) # construct grid dist = [] x = numpy.arange(0.001,1.0,0.01) y = numpy.arange(0.001,1.0,0.01) for p1 in x: d_row = [] for p2 in y: #for p3 in z: p3 = 1.0-p1-p2 d_row.append([p1,p2,p3]) dist.append(d_row) sample_dist = dict(zip(labels, ref_dist.phi)) proj_ref = simplex.project_distribution(sample_dist, use_logs=False) proj_x = [] proj_y = [] distance = [] f = lambda x: round(x,3) for drow in dist: x_row = [] y_row = [] d_row = [] for d in drow: #print d, 1.0 - numpy.sum(map(f,d)) if (1.0 - numpy.sum(map(f,d))) < 1e-15 and d[0] > 0 and d[1] > 0 and d[2] > 0.0: #print ref_dist,mixture.DiscreteDistribution(3, d), mixture.sym_kl_dist( ref_dist, mixture.DiscreteDistribution(3, d)) if objf == 'sym': d_row.append( mixture.sym_kl_dist( ref_dist, mixture.DiscreteDistribution(3, d))) elif objf == 'leftToRight': d_row.append( mixture.kl_dist( ref_dist, mixture.DiscreteDistribution(3, d))) elif objf == 'rightToLeft': d_row.append( mixture.kl_dist( mixture.DiscreteDistribution(3, d), ref_dist )) else: raise TypeError else: d_row.append( 0.0) sample_dist = dict(zip(labels, d)) pp = simplex.project_distribution(sample_dist, use_logs=False) x_row.append(pp[0]) y_row.append(pp[1]) proj_x.append(x_row) proj_y.append(y_row) distance.append(d_row) proj_x = numpy.array(proj_x) proj_y = numpy.array(proj_y) distance = numpy.array(distance) # Create the figure fig = pylab.figure() fig.set_facecolor('w') fig.add_axes([.15,.15,.70,.70], axisbg='w', aspect='equal') axis = pylab.gca() # Plot the simplex simplex_plotter = Simplex2DPlotter(simplex, axis) simplex_plotter.prepare_axes() simplex_plotter.plot_simplex() #axis.set_title(title) axis.text(-0.5, 0.55, title, fontsize=12) # Plot the samples #x = [sample[0] for sample in samples] #y = [sample[1] for sample in samples] max_val = distance.max() step = max_val / 50.0 axis.contourf(proj_x, proj_y,distance,pylab.arange(0,max_val,step)) axis.plot([proj_ref[0]], [proj_ref[1]], 'or') if show: pylab.show()
def plotDirichletDensity(dirichlet_dist,title='DirichletDensity'): assert dirichlet_dist.M == 3, 'Only 3 dimensions for now.' # A 2-simplex lives in 3-space. dimension = 3 # XXX dimension fixed to 3 for now # These are the vertex labels, converted to strings. labels = numpy.eye(dimension, dtype=int) labels = map(str, map(tuple, labels)) # Let's create the simplex. simplex = Simplex2D(labels, modify_labels=False) # construct grid dist = [] x = numpy.arange(0.001,1.0,0.01) y = numpy.arange(0.001,1.0,0.01) for p1 in x: d_row = [] for p2 in y: #for p3 in z: p3 = 1.0-p1-p2 d_row.append([p1,p2,p3]) dist.append(d_row) proj_x = [] proj_y = [] density = [] f = lambda x: round(x,3) for drow in dist: x_row = [] y_row = [] d_row = [] for d in drow: if (1.0 - numpy.sum(map(f,d))) < 1e-15 and d[0] > 0 and d[1] > 0 and d[2] > 0.0: d_row.append( numpy.exp( dirichlet_dist.pdf(mixture.DiscreteDistribution(3, d)) )) else: d_row.append( 0.0) sample_dist = dict(zip(labels, d)) pp = simplex.project_distribution(sample_dist, use_logs=False) x_row.append(pp[0]) y_row.append(pp[1]) proj_x.append(x_row) proj_y.append(y_row) density.append(d_row) proj_x = numpy.array(proj_x) proj_y = numpy.array(proj_y) density = numpy.array(density) # Create the figure fig = pylab.figure() fig.set_facecolor('w') fig.add_axes([.15,.15,.70,.70], axisbg='w', aspect='equal') axis = pylab.gca() # Plot the simplex simplex_plotter = Simplex2DPlotter(simplex, axis) simplex_plotter.prepare_axes() simplex_plotter.plot_simplex() axis.set_title(title) # Plot the samples #x = [sample[0] for sample in samples] #y = [sample[1] for sample in samples] max_val = density.max() step = max_val / 60.0 axis.contourf(proj_x, proj_y,density,pylab.arange(0,max_val,step),norm = pylab.matplotlib.colors.Normalize(proj_x) )
import labeledBayesMixture import mixture import copy # Setting up a three component Bayesian mixture over four features. # Two features are Normal distributions, two discrete. # initializing atomar distributions for first component n11 = mixture.NormalDistribution(1.0, 1.5) n12 = mixture.NormalDistribution(2.0, 0.5) d13 = mixture.DiscreteDistribution(4, [0.1, 0.4, 0.4, 0.1]) d14 = mixture.DiscreteDistribution(4, [0.25, 0.25, 0.25, 0.25]) # initializing atomar distributions for second component n21 = mixture.NormalDistribution(4.0, 0.5) n22 = mixture.NormalDistribution(-6.0, 0.5) d23 = mixture.DiscreteDistribution(4, [0.7, 0.1, 0.1, 0.1]) d24 = mixture.DiscreteDistribution(4, [0.1, 0.1, 0.2, 0.6]) # initializing atomar distributions for second component n31 = mixture.NormalDistribution(2.0, 0.5) n32 = mixture.NormalDistribution(-3.0, 0.5) d33 = mixture.DiscreteDistribution(4, [0.1, 0.1, 0.1, 0.7]) d34 = mixture.DiscreteDistribution(4, [0.6, 0.1, 0.2, 0.1]) # creating component distributions c1 = mixture.ProductDistribution([n11, n12, d13, d14]) c2 = mixture.ProductDistribution([n21, n22, d23, d24]) c3 = mixture.ProductDistribution([n31, n32, d33, d34]) # setting up the mixture prior
compPrior= [] for i in range(2): compPrior.append( mixture.DirichletDistribution(4,[1.02,1.02,1.02,1.02]) ) for i in range(2): compPrior.append( mixture.NormalGammaDistribution( 1.0,2.0,3.0,4.0 ) ) mixPrior = mixture.MixturePrior(0.7,0.7,piPrior, compPrior) DNA = mixture.Alphabet(['A','C','G','T']) comps = [] for i in range(G): dlist = [] for j in range(2): phi = mixture.random_vector(4) dlist.append( mixture.DiscreteDistribution(4,phi,DNA)) for j in range(2): mu = j+1.0 sigma = j+0.5 dlist.append( mixture.NormalDistribution(mu,sigma)) comps.append(mixture.ProductDistribution(dlist)) pi = mixture.random_vector(G) m = mixture.BayesMixtureModel(G,pi, comps, mixPrior, struct = 1) mixture.writeMixture(m, 'test.bmix') m2 = mixture.readMixture('test.bmix')
def main(): logger.debug('App started') parser = argparse.ArgumentParser(description='Key processing tool') parser.add_argument('-t', '--threads', dest='threads', type=int, default=None, help='Number of threads to use for cert download') parser.add_argument('--debug', dest='debug', action='store_const', const=True, help='enables debug mode') parser.add_argument('--verbose', dest='verbose', action='store_const', const=True, help='enables verbose mode') parser.add_argument('--dump-json', dest='dump_json', action='store_const', const=True, help='dumps JSON of the filtered certificates') parser.add_argument('--dump-cert', dest='dump_cert', action='store_const', const=True, help='dumps PEM of the filtered certificates') parser.add_argument( '-f', '--filter-org', dest='filter_org', help='Filter out certificates issued with given organization - regex') parser.add_argument( '--filter-domain', dest='filter_domain', help='Filter out certificates issued for the given domain - regex') parser.add_argument('--pubs', dest='pubs', nargs=argparse.ZERO_OR_MORE, help='File with public keys (PEM)') parser.add_argument('--certs', dest='certs', nargs=argparse.ZERO_OR_MORE, help='File with certificates (PEM)') parser.add_argument('--ossl', dest='ossl', type=int, default=None, help='OpenSSL generator') parser.add_argument('--per-key-stat', dest='per_key_stat', action='store_const', const=True, help='Print prob matching for each key') parser.add_argument('--subs', dest='subs', action='store_const', const=True, help='Plot random subgroups charts') parser.add_argument('--subs-k', dest='subs_k', type=int, default=5, help='Size of the subset') parser.add_argument('--subs-n', dest='subs_n', type=int, default=1000, help='Number of subsets to sample') parser.add_argument('--pca-src', dest='pca_src', action='store_const', const=True, help='Plot PCA sampled distribution vs collected one') parser.add_argument( '--pca-src-n', dest='pca_src_n', type=int, default=10000, help='Number of subsets to sample from source distributions') parser.add_argument('--pca-src-k', dest='pca_src_k', type=int, default=3, help='Size of the subset from the source distribution') parser.add_argument('--pca-grp', dest='pca_grp', action='store_const', const=True, help='Plot PCA on the input keys (groups)') parser.add_argument('--mixture', dest='mixture', action='store_const', const=True, help='Mixture distribution on masks - sources') parser.add_argument('--distrib', dest='distrib', action='store_const', const=True, help='Plot distributions - to the PDF') parser.add_argument('--distrib-mix', dest='distribmix', action='store_const', const=True, help='Plot distributions groups mixed with sources') parser.add_argument('--key-dist', dest='plot_key_dist', action='store_const', const=True, help='Plots key mask distribution') parser.add_argument('files', nargs=argparse.ZERO_OR_MORE, default=[], help='file with ssl-dump json output') args = parser.parse_args() last_src_id = 0 src_names = [] masks_db = [] masks_src = [] cert_db = [] keys_db = [] # Input = ssl-dump output if len(args.files) > 0: # Cert Organization Filtering re_org = None if args.filter_org is None else re.compile( args.filter_org, re.IGNORECASE) # Domain filtering re_dom = None if args.filter_domain is None else re.compile( args.filter_domain, re.IGNORECASE) # Process files for fl in args.files: with open(fl, mode='r') as fh: data = fh.read() # Parse json out if '-----BEGIN JSON-----' in data: if '-----END JSON-----' not in data: raise ValueError('BEGIN JSON present but END JSON not') match = re.search( r'-----BEGIN JSON-----(.+?)-----END JSON-----', data, re.MULTILINE | re.DOTALL) if match is None: raise ValueError('Could not extract JSON') data = match.group(1) json_data = json.loads(data) for cert in json_data: org = cert['org'] if org is None: org = '' if re_org is not None and re_org.match(org) is None: if args.verbose: print('Organization filtered out %s' % org) continue if re_dom is not None: dom_match = re_dom.match(cert['cn']) is not None for alt in cert['alts']: dom_match |= re_dom.match(alt) is not None if not dom_match: if args.verbose: print('Domain filtered out %s' % cert['cn']) continue cert_db.append(cert) masks_db.append(cert['pubkey']['mask']) masks_src.append(last_src_id) src_names.append(fl) last_src_id += 1 if args.verbose: print('Certificate database size %d' % len(cert_db)) if args.dump_json: print(json.dumps(cert_db)) if args.dump_cert: for cert in cert_db: print cert['cert'] # public key list processing if args.pubs is not None: for pubf in args.pubs: with open(pubf, mode='r') as fh: data = fh.read() keys = [] for match in re.finditer( r'-----BEGIN PUBLIC KEY-----(.+?)-----END PUBLIC KEY-----', data, re.MULTILINE | re.DOTALL): key = match.group(0) keys.append(key) print('File %s keys num: %d' % (pubf, len(keys))) # pubkey -> mask for key in keys: pub = serialization.load_pem_public_key( key, utils.get_backend()) mask = keys_basic.compute_key_mask(pub.public_numbers().n) keys_db.append(pub) masks_db.append(mask) masks_src.append(last_src_id) src_names.append(pubf) last_src_id += 1 # extract public key from certificate if args.certs is not None: for certf in args.certs: with open(certf, mode='r') as fh: data = fh.read() certs = [] for match in re.finditer( r'-----BEGIN CERTIFICATE-----(.+?)-----END CERTIFICATE-----', data, re.MULTILINE | re.DOTALL): cert = match.group(0) certs.append(cert) # cert -> mask for cert in certs: x509 = utils.load_x509(str(cert)) pub = x509.public_key() mask = keys_basic.compute_key_mask(pub.public_numbers().n) keys_db.append(pub) masks_db.append(mask) masks_src.append(last_src_id) src_names.append(certf) last_src_id += 1 # generate openssl keys on the fly if args.ossl is not None: for i in range(0, args.ossl): print('Generating RSA1024 key %03d' % i) key = OpenSSL.crypto.PKey() key.generate_key(OpenSSL.crypto.TYPE_RSA, 1024) key_pem = OpenSSL.crypto.dump_privatekey( OpenSSL.crypto.FILETYPE_PEM, key) priv = serialization.load_pem_private_key(key_pem, None, utils.get_backend()) mask = keys_basic.compute_key_mask( priv.public_key().public_numbers().n) keys_db.append(priv.public_key()) masks_db.append(mask) masks_src.append(last_src_id) src_names.append('ossl-%d' % args.ossl) last_src_id += 1 # Load statistics st = key_stats.KeyStats() st.load_tables() if args.verbose: print('Source stats: ') for src in st.sources_cn: print(' %30s: %08d' % (src, st.sources_cn[src])) print('Group stats:') for grp in st.groups: print(' %30s: %02d' % (grp, st.get_group_size(grp))) # mask indices mask_map, mask_max, mask_map_x, mask_map_y, mask_map_last_x, mask_map_last_y = keys_basic.generate_pubkey_mask_indices( ) print('Max mask 1D config: [%d]' % mask_max) print('Max mask 2D config: [%d, %d]' % (mask_map_last_x, mask_map_last_y)) # masks processing part if len(masks_db) == 0: return # Simple match if args.per_key_stat: print('Per-key matching: ') for idx, mask in enumerate(masks_db): print('Key %02d, mask: %s' % (idx, mask)) res = [] for src in st.table_prob: val = st.table_prob[src][mask] res.append((src, val if val is not None else 0)) print_res(res, st) # Total key matching use_loglikelihood = True print('Fit for all keys in one distribution:') total_weights = src_total_match = comp_total_match_dict( masks_db, st, loglikelihood=use_loglikelihood) res = key_val_to_list(src_total_match) print_res(res, st, loglikelihood=use_loglikelihood) res = st.res_src_to_group(res) # bar_chart(res=res, title='Fit for all keys') # Avg + mean print('Avg + mean:') src_total_match = {} # source -> [p1, p2, p3, p4, ..., p_keynum] for src in st.table_prob: src_total_match[src] = [] for idx, mask in enumerate(masks_db): val = keys_basic.aggregate_mask(st.sources_masks_prob[src], mask) if use_loglikelihood: if total_weights[src] is not None: src_total_match[src].append(val + total_weights[src]) else: src_total_match[src].append(-9999.9) else: src_total_match[src].append(val * total_weights[src]) pass pass res = [] devs = [] for src in st.sources: m = np.mean(src_total_match[src]) s = np.std(src_total_match[src]) res.append((src, m)) devs.append(s) # Total output print_res(res, st, error=devs, loglikelihood=use_loglikelihood) # bar_chart(res=res, error=devs, title='Avg for all keys + error') # PCA on the keys - groups keys_grp_vec = [] for idx, mask in enumerate(masks_db): keys_grp_vec.append([]) for src in st.groups: keys_grp_vec[idx].append(0) for idxs, src in enumerate(st.sources): grp = st.src_to_group(src) prob = st.table_prob[src][mask] keys_grp_vec[idx][st.get_group_idx(grp)] += prob if args.pca_grp: X = np.array(keys_grp_vec) pca = PCA(n_components=2) pca.fit(X) X_transformed = pca.transform(X) print('PCA mean: %s, components: ' % pca.mean_) print(pca.components_) masks_src_np = np.array(masks_src) plt.rcdefaults() colors = matplotlib.cm.rainbow(np.linspace(0, 1, last_src_id)) for src_id in range(0, last_src_id): plt.scatter(X_transformed[masks_src_np == src_id, 0], X_transformed[masks_src_np == src_id, 1], label=src_names[src_id], color=colors[src_id], alpha=0.25, marker=',') plt.legend(loc="best", shadow=False, scatterpoints=1) plt.show() # Random subset if args.subs: masks_db_tup = [] for idx, mask in enumerate(masks_db): masks_db_tup.append((idx, mask, masks_src[idx])) # Many random subsets, top groups subs_size = args.subs_k subs_count = args.subs_n groups_cnt = {} subs_data = [] subs_data_mark = [] dsrc_num = last_src_id + 1 # Take subs_count samples fro the input masks_db, evaluate it, prepare for PCA for i in range(0, subs_count): masks = random_subset(masks_db_tup, subs_size) src_total_match = comp_total_match_dict([x[1] for x in masks], st) res = key_val_to_list(src_total_match) total = 0.0 for tup in res: total += tup[1] # data vectors for PCA tmp_data = [] for idx, tmp_src in enumerate(st.sources): val = src_total_match[tmp_src] val = long(math.floor(val * (1000.0 / total))) tmp_data.append(val) # PCA on groups. # if want PCA on sources, use subs_data.append(tmp_data) subs_data.append(tmp_data) # res_grp_val = st.res_src_to_group(zip(st.sources, tmp_data)) # subs_data.append([x[1] for x in res_grp_val]) subs_dsources = {} max_dsrc = (0, 0) for dsrc in [x[2] for x in masks]: if dsrc not in subs_dsources: subs_dsources[dsrc] = 0 subs_dsources[dsrc] += 1 for dsrc in subs_dsources: if subs_dsources[dsrc] > max_dsrc[1]: max_dsrc = (dsrc, subs_dsources[dsrc]) tmp_mark = max_dsrc[0] if max_dsrc[1] == subs_size: tmp_mark = max_dsrc[0] else: tmp_mark = last_src_id subs_data_mark.append(tmp_mark) for tup in res: src = tup[0] score = long(math.floor(tup[1] * (1000.0 / total))) if score == 0: continue grp = st.src_to_group(src) if grp not in groups_cnt: groups_cnt[grp] = score else: groups_cnt[grp] += score if src not in groups_cnt: groups_cnt[src] = score else: groups_cnt[src] += score # Equalize group sizes for grp in st.groups: grp = grp.lower() if grp in groups_cnt: groups_cnt[grp] /= float(st.get_group_size(grp)) # best group only # best_src = res[0][0] # best_grp = st.src_to_group(best_src) # if best_grp not in groups_cnt: # groups_cnt[best_grp] = 1 # else: # groups_cnt[best_grp] += 1 print('Combinations: (N, k)=(%d, %d) = %d' % (subs_count, subs_size, scipy.misc.comb(subs_count, subs_size))) sources = st.groups values = [] for source in sources: val = groups_cnt[source] if source in groups_cnt else 0 values.append(val) bar_chart(sources, values, xlabel='# of occurrences as top group (best fit)', title='Groups vs. %d random %d-subsets' % (subs_count, subs_size)) # PCA stuff X = np.array(subs_data) pca = PCA(n_components=2) pU, pS, pV = pca._fit(X) X_transformed = pca.transform(X) subs_data_mark_pca = np.array(subs_data_mark) print('Sources: ') print(st.sources) print('PCA input data shape %d x %d' % (len(subs_data), len(subs_data[0]))) print('PCA mean: \n%s \nPCA components: \n' % pca.mean_) print(pca.components_) print('PCA components x: ') for x in pca.components_[0]: print x print('\nPCA components y: ') for y in pca.components_[1]: print y # print('\nPCA U,S,V') # print(pU) # print(pS) # print(pV) colors = ['blue', 'red', 'green', 'gray', 'yellow'] plt.rcdefaults() for src_id in range(0, dsrc_num): plt.scatter(X_transformed[subs_data_mark_pca == src_id, 0], X_transformed[subs_data_mark_pca == src_id, 1], color=colors[src_id], alpha=0.5 if src_id < dsrc_num - 1 else 0.2) plt.legend(loc="best", shadow=False, scatterpoints=1) # plt.scatter([x[0] for x in X_transformed], # [x[1] for x in X_transformed], # alpha=0.5) plt.show() # PCA against defined sources with known distributions? # Creates "background distribution" we want to match to if args.pca_src: # Four axes, returned as a 2-d array plt.rcdefaults() #f, axarr = plt.subplots(len(st.sources), 1) src_k = args.pca_src_k src_n = args.pca_src_n # prepare PDF ppdf = PdfPages('test.pdf') # todo-filenae-from-set sources_to_test = st.sources[20:25] + [ x for x in st.sources if 'micro' in x.lower() ] # compute for each source src_mark_idx = len(subs_data_mark) subs_data_src = subs_data subs_data_mark_src = subs_data_mark for src_idx, source in enumerate(sources_to_test): # cur_plot = axarr[src_idx] cur_plot = plt print('Plotting PCA source %s %d/%d' % (source, src_idx + 1, len(sources_to_test))) # Extend subs_data_src with draws from the source distribution for i in range(0, src_n): masks = [] for tmpk in range(0, src_k): masks.append(st.sample_source_distrib(source)) src_total_match = comp_total_match_dict(masks, st) res = key_val_to_list(src_total_match) total = 0.0 for tup in res: total += tup[1] # data vectors for PCA tmp_data = [] for idx, tmp_src in enumerate(st.sources): val = src_total_match[tmp_src] val = long(math.floor(val * (1000.0 / total))) tmp_data.append(val) # PCA on groups. # if want PCA on sources, use subs_data.append(tmp_data) subs_data_src.append(tmp_data) subs_data_mark_src.append(src_mark_idx) # PCA stuff X = np.array(subs_data_src) pca = PCA(n_components=2) pU, pS, pV = pca._fit(X) X_transformed = pca.transform(X) subs_data_mark_pca = np.array(subs_data_mark_src) colors = ['blue', 'red', 'green', 'gray', 'yellow'] # plot input sources for src_id in range(0, dsrc_num): cur_plot.scatter( X_transformed[subs_data_mark_pca == src_id, 0], X_transformed[subs_data_mark_pca == src_id, 1], color=colors[src_id], alpha=0.5 if src_id < dsrc_num - 1 else 0.2) # plot the source stuff cur_plot.scatter( X_transformed[subs_data_mark_pca == src_mark_idx, 0], X_transformed[subs_data_mark_pca == src_mark_idx, 1], color='gray', marker='+', alpha=0.05) cur_plot.legend(loc="best", shadow=False, scatterpoints=1) cur_plot.title('Src [%s] input: %s' % (source, (', '.join(src_names)))) cur_plot.savefig(ppdf, format='pdf') cur_plot.clf() print('Finalizing PDF...') # plt.savefig(ppdf, format='pdf') ppdf.close() pass if args.distrib: # Plotting distributions for groups, to the PDF plt.rcdefaults() ppdf = PdfPages('groups_distrib.pdf') # Compute for each source range_ = st.masks range_idx = np.arange(len(st.masks)) for grp_idx, grp in enumerate(st.groups): cur_data = st.groups_masks_prob[grp] raw_data = [cur_data[x] for x in st.masks] cur_plot = plt logger.debug('Plotting distribution %02d/%02d : %s ' % (grp_idx + 1, len(st.groups), grp)) axes = cur_plot.gca() axes.set_xlim([0, len(st.masks)]) cur_plot.bar(range_idx, raw_data, linewidth=0, width=0.4) cur_plot.title('%s (%s)' % (grp, get_group_desc(grp, st))) cur_plot.savefig(ppdf, format='pdf') cur_plot.clf() # Print input data - per source max_src = max(masks_src) bars = [] for src_id in range(max_src + 1): axes = plt.gca() axes.set_xlim([0, len(st.masks)]) map_data = {} for mask in st.masks: map_data[mask] = 0.0 for mask_idx, mask in enumerate(masks_db): if masks_src[mask_idx] == src_id: map_data[mask] += 1 raw_data = [] for mask in st.masks: raw_data.append(map_data[mask]) b1 = plt.bar(range_idx, raw_data, linewidth=0, width=0.4) bars.append(b1) plt.title('Source %d' % src_id) plt.savefig(ppdf, format='pdf') plt.clf() # Group distribution + source: if args.distribmix: width = 0.25 range_idx = np.arange(len(st.masks)) # One source to the graph max_src = max(masks_src) cur_plot = plt for src_id in range(max_src + 1): bars = [] logger.debug('Plotting mix distribution src %d ' % src_id) map_data = {} for mask in st.masks: map_data[mask] = 0.0 for mask_idx, mask in enumerate(masks_db): if masks_src[mask_idx] == src_id: map_data[mask] += 1 raw_data = [] for mask in st.masks: raw_data.append(map_data[mask]) raw_data = np.array(raw_data) raw_data /= float(sum(raw_data)) for grp_idx, grp in enumerate(st.groups): logger.debug( ' - Plotting mix distribution %02d/%02d : %s ' % (grp_idx + 1, len(st.groups), grp)) # Source fig, ax = plt.subplots() b1 = ax.bar(range_idx + width, raw_data, linewidth=0, width=width, color='r') bars.append(b1) # Group cur_data2 = st.groups_masks_prob[grp] raw_data2 = [cur_data2[x] for x in st.masks] bar1 = ax.bar(range_idx, raw_data2, linewidth=0, width=width, color='b') bars.append(bar1) ax.legend(tuple([x[0] for x in bars]), tuple(['Src %d' % src_id, grp])) ax.set_xlim([0, len(st.masks)]) cur_plot.title('%s + source %d' % (grp, src_id)) cur_plot.savefig(ppdf, format='pdf') cur_plot.clf() logger.info('Finishing PDF') ppdf.close() pass if args.mixture: # http://www.pymix.org/pymix/index.php?n=PyMix.Tutorial#bayesmix # 1. Create mixture model = add discrete distributions to the package dists = [] alphabet = mixture.Alphabet(st.masks) taken_src = [] for src in st.sources: if 'openssl 1.0.2g' == src or 'microsoft .net' == src: pass else: continue print(' - Source: %s' % src) taken_src.append(src) probs = [] for m in st.masks: probs.append(st.sources_masks_prob[src][m]) d = mixture.DiscreteDistribution(len(alphabet), probs, alphabet=alphabet) dists.append(d) # 2. Create the model, for now, with even distribution among components. comp_weights = [1.0 / len(dists)] * len(dists) mmodel = mixture.MixtureModel(len(dists), comp_weights, dists) print '-' * 80 print mmodel print '-' * 80 # dump mixtures to the file mixture.writeMixture(mmodel, 'src.mix') # 3. Input data - array of input masks masks_data = [[x] for x in masks_db] data = mixture.DataSet() data.fromList(masks_data) data.internalInit(mmodel) print masks_data print data print '---------' # 4. Compute EM # if there is a distribution in the input data which has zero matching inputs, # an exception will be thrown. Later - discard such source from the input... print mmodel.modelInitialization(data, 1) print('EM start: ') ress = [] for r in range(10): mmodel.modelInitialization(data, 1) emres = mmodel.EM(data, 1000, 0.00000000000000001) ress.append(emres) emres = max(ress, key=lambda x: x[1]) # print mmodel.randMaxEM(data, 10, 40, 0.1) print emres # Plot plt.rcdefaults() # plt.plot(range(0, len(emres[0][3])), [2.71828**x for x in emres[0][3]], 'o') # plt.plot(range(0, len(emres[0][3])), emres[0][3], 'k') # plt.show() for i in range(0, 5): print('-------') for idx, src in enumerate(emres[0]): print('- i:%02d src: %02d, val: %s' % (i, idx, src[i])) colors = matplotlib.cm.rainbow(np.linspace(0, 1, len(taken_src))) range_ = range(0, len(emres[0][0])) bars = [] for idx, src in enumerate(emres[0]): b1 = plt.bar(range_, [2.71828**x for x in src], color=colors[idx]) bars.append(b1) plt.legend(tuple(bars), tuple(taken_src)) plt.grid(True) plt.show() # for src in emres[0]: # plt.plot(range(0, len(src)), [2.71828**x for x in src], 'o') # # plt.grid(True) # # plt.show() # # # plt.scatter(mask_map_last_x, mask_map_last_y, c='red', s=scale, alpha=0.3) # # plt.legend() # plt.grid(True) # plt.show() # Chisquare for source in st.sources_masks: cn = st.sources_cn[source] # chi = chisquare() # gen = keys_basic.generate_pubkey_mask() # 2D Key plot if args.plot_key_dist: plot_key_mask_dist(masks_db, st)
# Example for context-specific independence (CSI) structure learning. # First we generate a data set from a three component mixture with a CSI like structure # in the distribution parameters. Then a five component CSI mixture is trained. # The training should recover the true number of components (three), # the CSI structure of the generating model as well as the distribution parameters. # Setting up the generating model. This is a benign case in the # sense that the components are reasonably well separated and we # allow ourselves plenty of training data. # Component distributions n11 = mixture.NormalDistribution(1.0, 0.5) n12 = mixture.NormalDistribution(2.0, 1.5) n13 = mixture.NormalDistribution(3.0, 0.7) d14 = mixture.DiscreteDistribution(4, [0.4, 0.3, 0.1, 0.2]) c1 = mixture.ProductDistribution([n11, n12, n13, d14]) n21 = mixture.NormalDistribution(1.0, 0.5) n22 = mixture.NormalDistribution(-6.0, 0.5) n23 = mixture.NormalDistribution(3.0, 0.7) d24 = mixture.DiscreteDistribution(4, [0.1, 0.1, 0.4, 0.4]) c2 = mixture.ProductDistribution([n21, n22, n23, d24]) n31 = mixture.NormalDistribution(2.0, 0.5) n32 = mixture.NormalDistribution(-3.0, 0.5) n33 = mixture.NormalDistribution(3.0, 0.7) d34 = mixture.DiscreteDistribution(4, [0.4, 0.3, 0.1, 0.2])
def clustering(k, feature_cols, feature_domains, header, table, seeds, result_file): best_loglike = None best_model = None # Giant random seeding loop, data = mx.DataSet() data.fromArray(table) for r in range(1): # weights = np.random.random_sample(k) # weights_norm = weights / sum(weights) weights_norm = [1.0 / k] * k components = [] for i in range(k): products = [] for j in range(table.shape[1]): col_type = prep.get_col_type(feature_cols[j], header) col_id = feature_cols[j] if col_type == 'cat': vals = feature_domains[col_id].keys() cnt_vals = len(vals) rand_dist = np.random.random_sample(cnt_vals) dist = mx.DiscreteDistribution(cnt_vals, rand_dist / sum(rand_dist), mx.Alphabet(vals)) elif col_type == 'num': min_val = feature_domains[col_id]['min'] max_val = feature_domains[col_id]['max'] # mean = random.uniform(min_val, max_val) mean = seeds[header[col_id][0]][i] stdev = (max_val - min_val) / 2.0 / k dist = mx.NormalDistribution(mean, stdev) else: sys.exit(1) products.append(dist) comp = mx.ProductDistribution(products) components.append(comp) mix_table = mx.MixtureModel(k, weights_norm, components) print mix_table #loglike = mix_table.randMaxEM(data,1,50,50) #print loglike #print mix_table if not best_loglike or loglike > best_loglike: # best_loglike = loglike best_model = copy.copy(mix_table) #data.internalInit(mix) # mix_table.modelInitialization(data) # print best_loglike # print best_model labels = best_model.classify(data, None, None, 1) ## output clustering results # count cluster sizes on sampled data f = open(result_file + '.stats', 'w') cnt = {} for l in labels: cnt[l] = 1 if l not in cnt else cnt[l] + 1 for l in cnt: f.write('%s %d %f%%\n' % (l, cnt[l], cnt[l] * 100.0 / sum(cnt.values()))) f.close() mx.writeMixture(best_model, result_file + '.model') return best_model