├── .gitignore ├── CHANGES.txt ├── LICENSE.txt ├── MANIFEST.in ├── README.txt ├── gpl-3.0.txt ├── propagate ├── __init__.py ├── analysis.py ├── command_line.py ├── cv.py ├── data.py ├── network.py ├── run.py └── test │ ├── __init__.py │ └── data │ ├── cv_prior1.txt │ ├── network1.txt │ ├── network3.txt │ ├── prior1.txt │ ├── prior1a.txt │ └── prior2.txt └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | eggs/ 15 | lib/ 16 | lib64/ 17 | parts/ 18 | sdist/ 19 | var/ 20 | *.egg-info/ 21 | .installed.cfg 22 | *.egg 23 | MANIFEST 24 | 25 | # Installer logs 26 | pip-log.txt 27 | pip-delete-this-directory.txt 28 | 29 | # Unit test / coverage reports 30 | htmlcov/ 31 | .tox/ 32 | .coverage 33 | .cache 34 | nosetests.xml 35 | coverage.xml 36 | 37 | # Translations 38 | *.mo 39 | *.pot 40 | 41 | # Django stuff: 42 | *.log 43 | 44 | # Sphinx documentation 45 | docs/_build/ 46 | -------------------------------------------------------------------------------- /CHANGES.txt: -------------------------------------------------------------------------------- 1 | v0.2.1, July 3rd 2014 -- * The quality of the results can now be evaluated with a validation set (requires scikit.learn) 2 | 3 | v0.2.0, June 25th 2014 -- * The code in now split into modules and can be used as a library. 4 | * Support for creation of data structures from pandas dataframes 5 | * Support for cross validation for the prediction of a given prior 6 | 7 | v0.1.0, June 16th 2014 -- Initial release 8 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Copyright (C) 2014 Nir Atias 2 | 3 | This program is free software: you can redistribute it and/or modify 4 | it under the terms of the GNU General Public License as published by 5 | the Free Software Foundation, either version 3 of the License, or 6 | (at your option) any later version. 7 | 8 | This program is distributed in the hope that it will be useful, 9 | but WITHOUT ANY WARRANTY; without even the implied warranty of 10 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 11 | GNU General Public License for more details. 12 | 13 | You should have received a copy of the GNU General Public License 14 | along with this program. If not, see . 15 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/atiasnir/propagation/02abe6cda7413c68778d236e5911bba8eb78d1b9/MANIFEST.in -------------------------------------------------------------------------------- /README.txt: -------------------------------------------------------------------------------- 1 | ============================== 2 | Belief Propagation in Networks 3 | ============================== 4 | 5 | Belief propagation is a general graph algorithm for ranking nodes 6 | based on their location in the graph and some prior knowledge. 7 | 8 | The network is using in machine learning and bioinformatics in a variety 9 | of applications. 10 | 11 | This package implements the algorithm and provides a command line interface 12 | to run it. 13 | 14 | Dependencies: 15 | 16 | * numpy 17 | * scipy 18 | 19 | 20 | -------------------------------------------------------------------------------- /gpl-3.0.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /propagate/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/atiasnir/propagation/02abe6cda7413c68778d236e5911bba8eb78d1b9/propagate/__init__.py -------------------------------------------------------------------------------- /propagate/analysis.py: -------------------------------------------------------------------------------- 1 | from gurobipy import * 2 | import numpy as np 3 | 4 | def find_connected_ilp(network, scores, k=10, beta=None): 5 | if beta == None: 6 | beta = np.mean(scores) 7 | 8 | # we want to solve the following ILP program 9 | # 10 | # x_i = a 0/1 variable for each node. 11 | # 1 iff node in the solution 12 | # 13 | # y_ij = a 0/1 variable for each edge 14 | # 1 iff edge is in the solution 15 | # 16 | # the goal is to select a subset of k nodes 17 | # such that the sum of nodes score 18 | # minus beta times the number of induce non-edges 19 | # is maximized. 20 | # 21 | # Formally: 22 | # 23 | # max sum x * s^T - beta * (k * (k-1) / 2 - sum y_ij) 24 | # 25 | # s.t. 26 | # y_ij <= x_i, x_j 27 | # y_ij >= x_i + x_j - 1 28 | # 29 | # sum x_i == k 30 | # 31 | 32 | 33 | try: 34 | i, j = network.graph.nonzero() 35 | mask = i= x[u] + x[v] - 1) 52 | 53 | m.addConstr(quicksum(x) == k) 54 | 55 | m.setObjective(quicksum(x[i]*scores[i] for i in range(len(x))), GRB.MAXIMIZE) 56 | 57 | m.update() 58 | m.optimize() 59 | 60 | vals = m.getAttr("x", x) 61 | cluster = [i for i in range(len(x)) if vals[i] > 0.5] 62 | 63 | return cluster 64 | 65 | except GurobiError: 66 | print "ERROR" 67 | 68 | 69 | 70 | 71 | 72 | def find_connected(network, scores, beta = None): 73 | """ search for connected highscoring nodes """ 74 | 75 | if beta == None: 76 | beta = np.mean(scores) 77 | 78 | # get the indices for all the edges 79 | i, j = network.graph.nonzero() 80 | mask = i= 0): 97 | continue 98 | 99 | nodes = [r,c] 100 | 101 | # construct neighbors list 102 | neighbors = np.unique(np.append(network.graph[r,:].nonzero()[1],network.graph[c,:].nonzero()[1])) 103 | neighbors = np.setdiff1d(neighbors, nodes, assume_unique=True) 104 | 105 | while True: 106 | in_edges = network.graph[neighbors,:][:,nodes] 107 | mask = (in_edges>0).toarray() 108 | neighbor_score = np.sum(mask*scores[neighbors] - ~mask * beta, axis=1) 109 | found = False 110 | 111 | while not found: 112 | neighbor_idx = np.argmax(neighbor_score) 113 | 114 | to_add = neighbors[neighbor_idx] 115 | if assigned[to_add]>=0: 116 | neighbor_score[neighbor_idx] = -beta * neighbor_score.shape[0] 117 | else: 118 | found = True 119 | 120 | if neighbor_score[neighbor_idx] <= 0: 121 | break 122 | 123 | 124 | idx = np.searchsorted(nodes, to_add) 125 | nodes.insert(idx, to_add) 126 | 127 | new_neighbors = network.graph[to_add,:].nonzero()[1] 128 | neighbors = np.union1d(neighbors, new_neighbors) 129 | neighbors = np.setdiff1d(neighbors, nodes, assume_unique=True) 130 | 131 | if len(nodes) == 2: 132 | continue 133 | 134 | assigned[nodes] = len(clusters) 135 | clusters.append(nodes) 136 | 137 | return clusters 138 | -------------------------------------------------------------------------------- /propagate/command_line.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | def create_parser(): 4 | parser = argparse.ArgumentParser(description="Compute a smoothed score over network nodes") 5 | 6 | # positional 7 | parser.add_argument('network', type=str, help="network filename (tsv format: from/to/weight)") 8 | parser.add_argument('prior', type=str, default=None, help="prior filename(s) (each in tsv format: from/value)", nargs='+') 9 | # named 10 | parser.add_argument('-a,--alpha', dest="alpha", type=float, default=0.6, help="relative weight for the network") 11 | parser.add_argument('-i,--iterations', dest="iterations", type=int, default=1000, help="maximum number of iterations to execute") 12 | parser.add_argument('-e,--epsilon', dest="epsilon", type=float, default=1e-5, help="convergence threshold") 13 | parser.add_argument('-c,--report-clusters', dest="clusters", action='store_true', default=False, help="report high scoring clusters of nodes") 14 | parser.add_argument('-v,--verbose', dest="verbose", action='store_true', default=False, help="verbose output") 15 | 16 | return parser 17 | 18 | def is_valid_args(opts): 19 | import sys 20 | import os.path 21 | 22 | if opts.verbose: 23 | sys.stderr.write(str(opts) + "\n") 24 | 25 | failed = False 26 | if opts.network is None or not os.path.exists(opts.network): 27 | print "Invalid network filename" 28 | failed = True 29 | 30 | if opts.prior is not None and not all(os.path.exists(x) for x in opts.prior): 31 | print "Invalid prior filename" 32 | failed = True 33 | 34 | if opts.epsilon > 0.5: 35 | print "Epsilon is too large" 36 | failed = True 37 | 38 | if not 0 < opts.alpha < 1: 39 | print "alpha should be in (0,1)" 40 | failed = True 41 | 42 | return not failed 43 | 44 | if __name__ == '__main__': 45 | parser = create_parser() 46 | opts = parser.parse_args() 47 | succeeded = is_valid_args(opts) 48 | 49 | print 50 | print "(V) Valid " if succeeded else "(X) Invalid", "Command line" 51 | print opts 52 | -------------------------------------------------------------------------------- /propagate/cv.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sklearn.metrics import roc_curve, auc 3 | 4 | import command_line 5 | import data 6 | 7 | def parse_commandline(args): 8 | parser = command_line.create_parser() 9 | 10 | parser.add_argument('-s,--seed', dest="seed", type=int, default=None, help="a seed for the random number generator (default: use internal logic)") 11 | parser.add_argument('-f,--folds', dest="folds", type=int, default=5, help="the number of cross-validation folds") 12 | parser.add_argument('--validation', dest="validation", type=str, default=None, help="A validation dataset, that is use 'prior' +'network' to predict 'validation'") 13 | parser.add_argument('-l,--leave-one-out', dest="loo", action='store_true', default=False, help="Perform leave-one-out cross validation") 14 | 15 | return parser.parse_args(args) 16 | 17 | def leave_one_out(network, prior, cv_func=None, **kwargs): 18 | prior_ind = np.nonzero(prior)[0] 19 | result = np.zeros(shape=(len(prior_ind),len(prior))) 20 | 21 | prior_cpy = np.copy(prior) 22 | 23 | for i in range(len(prior_ind)): 24 | prior_cpy[prior_ind[i]] = 0 25 | if i > 0: 26 | prior_cpy[prior_ind[i-1]] = prior[prior_ind[i-1]] 27 | 28 | if cv_func is None: 29 | result[i,:] = network.smooth(prior_cpy, **kwargs).flat 30 | else: 31 | result[i,:] = cv_func(network, prior_cpy, **kwargs).flat 32 | result[i,np.where((prior_cpy>0).flat)] = None 33 | 34 | return result 35 | 36 | def kfold_m(network, priors, folds, seed=None, **kwargs): 37 | np.random.seed(seed) 38 | nzind, pind = np.nonzero(priors) 39 | 40 | nz_per_prior = np.bincount(pind) 41 | if np.any(folds > nz_per_prior): 42 | raise ValueError("Folds should be less than the number of nonzero elements in prior") 43 | 44 | #for i in range(priors.shape[1]): 45 | perm = np.random.permutation(nzind.shape[0]) 46 | nzind = nzind[perm] 47 | pind = pind[perm] 48 | 49 | results = np.zeros(shape=(folds, priors.shape[0], priors.shape[1])) 50 | cv_chunks = nz_per_prior // folds 51 | 52 | for i in range(folds): 53 | prior_cpy = np.copy(priors) 54 | if i0)] = None 65 | 66 | return results 67 | 68 | def kfold(network, prior, folds, seed=None, cv_func=None, **kwargs): 69 | if prior.shape[1] > 1: 70 | return kfold_m(network, prior, folds, seed, **kwargs) 71 | 72 | # shuffle the non zero indices so that each cv iteration 73 | # is contiguous in prior_ind 74 | np.random.seed(seed) 75 | prior_ind = np.copy(np.nonzero(prior)[0]) 76 | np.random.shuffle(prior_ind) 77 | 78 | if folds > len(prior_ind): 79 | raise ValueError("Folds should be less than the number of elements in the prior") 80 | 81 | result = np.zeros(shape=(folds, len(prior))) 82 | cv_chunk = len(prior_ind) // folds 83 | 84 | for i in range(folds): 85 | prior_cpy = np.copy(prior) 86 | if i < folds-1: 87 | prior_cpy[prior_ind[(i*cv_chunk):((i+1)*cv_chunk)]] = 0 # remove associations 88 | else: 89 | prior_cpy[prior_ind[(i*cv_chunk):]] = 0 # remove associations 90 | if cv_func is None: 91 | result[i,:] = network.smooth(prior_cpy, **kwargs).flat 92 | else: 93 | result[i,:] = cv_func(network, prior_cpy, **kwargs).flat 94 | result[i,np.where((prior_cpy>0).flat)] = None 95 | 96 | return result 97 | 98 | def _extract_scores(labels, scores): 99 | fpr, tpr, _ = roc_curve(labels, scores) 100 | return {'tpr': tpr, 'fpr': fpr, 'auc': auc(fpr,tpr)} 101 | 102 | def roc(result, prior, aggregate=np.nanmean): 103 | scores = aggregate(result, axis=0) 104 | if scores.ndim > 1 and scores.shape[1] > 1: 105 | if prior.shape == scores.shape: 106 | return [_extract_scores(prior[:,i], scores[:,i]) for i in range(scores.shape[1])] 107 | 108 | return [_extract_scores(prior, scores[:,i]) for i in range(scores.shape[1])] 109 | 110 | return [_extract_scores(prior, scores)] 111 | 112 | 113 | if __name__ == '__main__': 114 | import sys 115 | 116 | opts = parse_commandline(sys.argv[1:]) 117 | succeeded = command_line.is_valid_args(opts) 118 | 119 | if opts.prior is None: 120 | print "Prior filename must be specfied for cross validation" 121 | succeeded = False 122 | 123 | if opts.folds <= 1: 124 | print "Folds should be at least 2" 125 | succeeded = False 126 | 127 | if opts.validation is not None and not os.path.exists(opts.validation): 128 | succeeded = False 129 | print "Invalid validation filename" 130 | 131 | if succeeded: 132 | network = data.read_ppi(opts.network) 133 | prior = data.read_prior(opts.prior, network.names) 134 | 135 | if np.any(np.abs(prior) > 1.0): 136 | sys.stderr.write("Scaling prior, values outside [-1,1]\n") 137 | prior /= np.max(np.abs(prior), axis=0) 138 | 139 | network = network.normalize() 140 | 141 | if opts.loo: 142 | result = leave_one_out(network, prior, alpha=opts.alpha, eps=opts.epsilon, max_iter=opts.iterations) 143 | else: 144 | result = kfold(network, prior, opts.folds, opts.seed, alpha=opts.alpha, eps=opts.epsilon, max_iter=opts.iterations) 145 | 146 | # handle the results array 147 | roc_results = roc(result, prior != 0) 148 | 149 | for i,res in enumerate(roc_results): 150 | print opts.prior[i] 151 | print "tpr:", res['tpr'] 152 | print "fpr:", res['fpr'] 153 | print "auc:", res['auc'] 154 | print 155 | -------------------------------------------------------------------------------- /propagate/data.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import scipy.sparse as sps 4 | from network import Network 5 | 6 | def _build_index(dataset, *columns): 7 | mapset = pd.DataFrame(columns=('name', 'index'), dtype=(str,int)) 8 | for name_col, idx_col in columns: 9 | subset = dataset[[name_col, idx_col]] 10 | subset.columns = 'name', 'index' 11 | mapset = mapset.append(subset, ignore_index=True) 12 | 13 | mapset.drop_duplicates(inplace=True) 14 | return mapset 15 | 16 | def read_ppi_from_dataframe(dataset, from_column='from', to_column='to', confidence_column='confidence'): 17 | all_genes = pd.concat((dataset[from_column], dataset[to_column])).unique() 18 | n = len(all_genes) 19 | 20 | names = pd.DataFrame(data={'name': all_genes, 'index': range(n)}) 21 | 22 | d1 = dataset.merge(names, left_on=from_column, right_on='name') 23 | d1.rename(columns={'index': 'from_index'}, inplace=True) 24 | d1.drop('name', axis=1, inplace=True) 25 | d2 = d1.merge(names, left_on=to_column, right_on='name') 26 | d2.rename(columns={'index': 'to_index'}, inplace=True) 27 | d2.drop('name', axis=1, inplace=True) 28 | 29 | d2[confidence_column].fillna(1, inplace=True) 30 | d = sps.coo_matrix((d2[confidence_column], (d2['from_index'], d2['to_index'])), shape=(n,n)) 31 | sym = (d + d.T) 32 | 33 | names = _build_index(d2, (from_column, 'from_index'), (to_column, 'to_index')) 34 | names.sort(columns=('index'), inplace=True) 35 | 36 | # for some strange reason the following: 37 | # > names_series = pd.Series(index=names['name'].values, data=names['index'].values) 38 | # will convert the index to int, while the current version won't. 39 | # it does not reproduce in a 'simple' case. 40 | names_series = pd.Series(index=names.name.values, data=names['index'].values) 41 | return Network(sym, names_series) 42 | 43 | def read_ppi(filename): 44 | """ reads a ppi network from file """ 45 | 46 | column_names = ('from', 'to', 'confidence', 'flag') 47 | dataset = pd.read_table(filename, header=None, names=column_names) 48 | 49 | return read_ppi_from_dataframe(dataset) 50 | 51 | def create_prior(index, names, scores): 52 | n = 1 53 | if isinstance(names, list) or isinstance(names, tuple): 54 | n = len(names) 55 | mapped_names = [index[names[x]] for x in range(n)] 56 | else: 57 | mapped_names = [index[names]] 58 | 59 | prior = np.zeros(shape=(index.shape[0], n)) 60 | #mapped_names = [index[names[x]] for x in range(n)] 61 | #na_mask = (~np.isnan(mapped_names)).values 62 | #if hasattr(scores, '__getitem__'): 63 | # prior[mapped_names[na_mask].values.astype(np.int)] = scores[na_mask].reshape((np.count_nonzero(na_mask),1)) 64 | #else: 65 | 66 | for i in range(n): 67 | prior[mapped_names[i].values.astype(np.int),i] = scores 68 | 69 | return np.vstack(prior) 70 | 71 | def read_prior(filenames, index): 72 | if not isinstance(filenames, list) and not isinstance(filenames, tuple): 73 | filenames = [filenames] 74 | 75 | prior = np.zeros((len(index),len(filenames))) 76 | for i, filename in enumerate(filenames): 77 | with open(filename, 'r') as f: 78 | for line in f: 79 | arr = line.strip().split() 80 | if len(arr)>1: 81 | name, score = arr 82 | prior[index[name],i] = float(score) 83 | else: 84 | prior[index[arr[0]],i] = 1.0 85 | 86 | return prior 87 | -------------------------------------------------------------------------------- /propagate/network.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import numpy.linalg as la 3 | import scipy.sparse as sps 4 | 5 | class Network: 6 | def __init__(self, graph, names): 7 | self.graph = graph 8 | self.names = names 9 | 10 | def count_edges(self): 11 | return (np.count_nonzero(self.graph.diagonal()) + self.graph.nnz)/2 12 | 13 | def count_nodes(self): 14 | return self.graph.shape[0] 15 | 16 | def normalize(self): 17 | data = 1.0 / np.sqrt(self.graph.sum(1)) 18 | d = sps.dia_matrix( (data.T,[0]), (len(data),len(data)) ) 19 | self.graph = d * self.graph * d 20 | 21 | return self 22 | 23 | def smooth(self, y, alpha=0.6, eps=1e-5, max_iter=1000): 24 | f = np.copy(y) 25 | for i in range(max_iter): 26 | fold, f = f, alpha * self.graph * f + (1-alpha) * y 27 | if la.norm(f-fold) < eps: 28 | break 29 | 30 | return f 31 | -------------------------------------------------------------------------------- /propagate/run.py: -------------------------------------------------------------------------------- 1 | import os.path 2 | 3 | import numpy as np 4 | 5 | import data 6 | import command_line 7 | 8 | def parse_commandline(args): 9 | parser = command_line.create_parser() 10 | 11 | parser.add_argument('--validation', dest="validation", type=str, default=None, help="A validation dataset, that is use 'prior' +'network' to predict 'validation'") 12 | return parser.parse_args(args) 13 | 14 | def validate(result, validation): 15 | import sklearn.metrics as metrics 16 | fpr, tpr, _ = metrics.roc_curve(validation>0, result) 17 | return tpr, fpr, metrics.auc(fpr, tpr) 18 | 19 | if __name__ == '__main__': 20 | import sys 21 | 22 | opts = parse_commandline(sys.argv[1:]) 23 | succeeded = command_line.is_valid_args(opts) 24 | 25 | if opts.validation is not None and not os.path.exists(opts.validation): 26 | succeeded = False 27 | print "Invalid validation filename" 28 | 29 | if succeeded: 30 | network = data.read_ppi(opts.network) 31 | if opts.prior is not None: 32 | prior = data.read_prior(opts.prior, network.names) 33 | if np.any(np.abs(prior) > 1.0): 34 | sys.stderr.write("Scaling prior, values outside [-1,1]\n") 35 | prior /= np.max(np.abs(prior), axis=0) 36 | else: 37 | prior = np.ones((network.graph.shape[0],1)) 38 | 39 | if opts.validation is not None: 40 | validation = data.read_prior(opts.validation, network.names) 41 | 42 | result = network.normalize().smooth(prior, 43 | alpha=opts.alpha, 44 | eps=opts.epsilon, 45 | max_iter=opts.iterations) 46 | 47 | if opts.validation is not None: 48 | tpr, fpr, auc = validate(result, validation) 49 | print "fpr: ", fpr 50 | print "tpr: ", tpr 51 | print "auc: ", auc 52 | elif opts.clusters: 53 | import analysis 54 | clusters = analysis.find_connected_ilp(network, result) 55 | 56 | for c in clusters: 57 | print ','.join(str(x) for x in c) 58 | else: 59 | o = np.argsort(result[:,0]) 60 | result = result[o,:] 61 | names = network.names.index[o] 62 | for n, r in zip(names, result): 63 | print "\t".join((n, np.str.join("\t", r.astype(str)))) 64 | -------------------------------------------------------------------------------- /propagate/test/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/atiasnir/propagation/02abe6cda7413c68778d236e5911bba8eb78d1b9/propagate/test/__init__.py -------------------------------------------------------------------------------- /propagate/test/data/cv_prior1.txt: -------------------------------------------------------------------------------- 1 | a 1 2 | b 1 3 | c 1 4 | d 1 5 | -------------------------------------------------------------------------------- /propagate/test/data/network1.txt: -------------------------------------------------------------------------------- 1 | a b 1 2 | a c 1 3 | a d 1 4 | b c 1 5 | b d 1 6 | c d 1 7 | d e 0.5 8 | e f 1 9 | f g 1 10 | -------------------------------------------------------------------------------- /propagate/test/data/network3.txt: -------------------------------------------------------------------------------- 1 | a b 2 | a c 3 | -------------------------------------------------------------------------------- /propagate/test/data/prior1.txt: -------------------------------------------------------------------------------- 1 | a 1 2 | d 1 3 | -------------------------------------------------------------------------------- /propagate/test/data/prior1a.txt: -------------------------------------------------------------------------------- 1 | a 1 2 | d 1 3 | b 1 4 | -------------------------------------------------------------------------------- /propagate/test/data/prior2.txt: -------------------------------------------------------------------------------- 1 | 1956 1 2 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | setup( 4 | name='propagate', 5 | version='0.2.1', 6 | author='Nir Atias', 7 | author_email='rinatias@gmail.com', 8 | packages=find_packages(), 9 | url='https://github.com/atiasnir/propagation', 10 | license='LICENSE.txt', 11 | description='Propagation belief graph algorithm', 12 | long_description=open('README.txt').read(), 13 | install_requires=[ 14 | "numpy >= 1.8.1", 15 | "scipy >= 0.14.0", 16 | "pandas >= 0.13.0", 17 | "scikit-learn >= 0.14.1", 18 | ], 19 | package_data={'test.data' : ['*.txt']}, 20 | keywords="belief propagation diffusion graph algorithm", 21 | ) 22 | 23 | --------------------------------------------------------------------------------