Exemple #1
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def main():
    """
    Doc
    """
    data = load_data()
    humanppi_G = nx.Graph(data.humanppi, name="HumanPPI")
    write_to_hdf5(humanppi_G)
    return 0
def main():
    data = load_data()
    hgraph = nx.Graph(data.humanppi)
    fgraph = nx.Graph(data.functions)

    # plot_degree(hgraph)
    # plot_cp_degree(hgraph)
    #plot_fn_degree(hgraph, fgraph)
    plot_fn_cp_weight(hgraph, fgraph)
Exemple #3
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def main():
    data = load_data()

    if args.edges == 0:
        ppiGraph = nx.Graph(data.humanppi, name="HumanPPI")
    else:
        ppiGraph = nx.Graph(data.humanppi[0:args.edges], name="HumanPPI")
    draw_kwargs = dict(node_size=5, font_size=6,
                       with_labels=False, linewidths=0.1, width=0.2)
    layout_kw = dict(iterations=args.iterations)
    img_title = 'Nodes: %i  Edges: %i  Iterations: %i  Runtime: %s'

    if args.iterations is None:
        # Number of iters will be set to 10, check the get_iterations method.
        iters = get_iterations()

        for iteration in iters:
            start_time = time.time()

            fig = plt.figure(figsize=(10, 10))
            ax = fig.add_subplot(111)
            nx.draw(ppiGraph, pos=nx.spring_layout(
                ppiGraph, iterations=iteration), **draw_kwargs)

            run_time = time.time() - start_time
            runtime_str = str(round(run_time, 2)) + ' seconds'

            print("Iterations: %i  Runt-time: %s" % (iteration, runtime_str))

            subs = (len(ppiGraph), args.edges, iteration, runtime_str)
            ax.set_title(img_title % subs)
            filepath = 'report/graphs' + '/' + \
                       zeros(iteration, padlength=4) + '.png'
            plt.savefig(filepath, bbox_inches='tight')
            print("Done! Saved at : " + filepath)

            fig.clf()
            plt.close()
    else:
        start_time = time.time()
        fig = plt.figure(figsize=(10, 10))
        ax = fig.add_subplot(111)
        nx.draw(ppiGraph, pos=nx.spring_layout(
            ppiGraph, **layout_kw), **draw_kwargs)

        runtime = time.time() - start_time
        runtime_str = str(round(runtime, 2)) + ' seconds'

        subs = (len(ppiGraph), args.edges, args.iterations, runtime_str)
        ax.set_title(img_title % subs)

        filepath = 'report/graphs' + '/' + \
                   zeros(args.iterations, padlength=4) + '.png'
        plt.savefig(filepath, bbox_inches='tight')
        print("Done! Saved at " + filepath)
        fig.clf()
        plt.close()
def main():
    if (len(sys.argv) != 3):
        print('Usage: $ python %s in.pickle outmax' % sys.argv[0])
        quit()
    data_file = sys.argv[1]
    outmax = int(sys.argv[2])

    all_dataset = misc.load_data(data_file)
    html_dataset = {}
    c_dataset = {}
    for doc in all_dataset:
        if re.match(r'.+\.(c|h)$', doc):
            c_dataset[doc] = all_dataset[doc]
        elif  re.match(r'.+\.(html)$', doc):
            html_dataset[doc] = all_dataset[doc]
    
    pp = pprint.PrettyPrinter(indent=4)

    
    for doc,val in sorted(html_dataset.items(), key=lambda x:(x[0])[::-1], reverse=True):
        neighbor = get_neighbor_c(html_dataset[doc], c_dataset, outmax)
        print '--%s--' % doc
        pp.pprint(neighbor)
        print ''
    start = datetime.now()
    clf = linear_model.LogisticRegression()
    clf.fit(X, y)
    score = np.mean(clf.predict(T) == valid)
    return score, datetime.now() - start


if __name__ == '__main__':
    import sys, misc

    # don't bother me with warnings
    import warnings; warnings.simplefilter('ignore')
    np.seterr(all='ignore')

    print __doc__ + '\n'
    if not len(sys.argv) == 2:
        print misc.USAGE % __file__
        sys.exit(-1)
    else:
        dataset = sys.argv[1]

    print 'Loading data ...'
    data = misc.load_data(dataset)

    print 'Done, %s samples with %s features loaded into ' \
      'memory' % data[0].shape

    res_skl = misc.bench(bench_skl, data)
    print 'MLPy: mean %.2f, std %.2f\n' % (
        np.mean(res_skl), np.std(res_skl))
    import misc

    # don't bother me with warnings
    import warnings
    warnings.simplefilter('ignore')
    np.seterr(all='ignore')

    print __doc__ + '\n'
    if not len(sys.argv) == 2:
        print misc.USAGE % __file__
        sys.exit(-1)
    else:
        dataset = sys.argv[1]

    print 'Loading data ...'
    data = misc.load_data(dataset)

    # set sigma to something useful
    from milk.unsupervised import pdist
    sigma = np.median(pdist(data[0]))

    print 'Done, %s samples with %s features loaded into ' \
      'memory' % data[0].shape

    score, res_shogun = misc.bench(bench_shogun, data)
    print 'Shogun: mean %.2f, std %.2f' % (
        np.mean(res_shogun), np.std(res_shogun))
    print 'Score: %.2f\n' % score

    score, res_mdp = misc.bench(bench_mdp, data)
    print 'MDP: mean %.2f, std %.2f' % (
    model.add(BatchNormalization((256,)))
    model.add(Dropout(0.5))

    model.add(Dense(256, n_classes, init='glorot_uniform'))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy', optimizer="adam")

    return model


model_name = 'dump_keras_ensemble.0.5.0.5.0.5'
n_models = 20

print("Loading data...")
X, labels = load_data(paths.train_file, train=True)
y = preprocess_labels(labels)
X_test, ids = load_data(paths.test_file, train=False)
X = preprocess_data(X)
X_test = preprocess_data(X_test)

n_classes = y.shape[1]
n_dims = X.shape[1]

print("Training %d models..." % n_models)

proba = 0
models = range(1, n_models+1)
for i in models:
    print("\n-------------- Model %d --------------\n" % i)
    model = model_factory(n_classes, n_dims)
Exemple #8
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"""PCA benchmarks"""

import numpy as np
from datetime import datetime

#
#       .. Load dataset ..
#
from misc import load_data, bench
print 'Loading data ...'
X, y, T = load_data()
print 'Done, %s samples with %s features loaded into ' \
      'memory' % X.shape
n_components = 9



def bench_skl():
#
#       .. scikits.learn ..
#
    from scikits.learn import pca as skl_pca
    start = datetime.now()
    clf = skl_pca.RandomizedPCA(n_components=n_components)
    clf.fit(X)
    return datetime.now() - start


def bench_pybrain():
#
#       .. pybrain ..
Exemple #9
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def main():
    data = load_data()

    if args.edges == 0:
        ppiGraph = nx.Graph(data.humanppi, name="HumanPPI")
    else:
        ppiGraph = nx.Graph(data.humanppi[0:args.edges], name="HumanPPI")

    draw_kwargs = dict(node_size=5,
                       font_size=6,
                       with_labels=False, 
                       linewidths=0.1, 
                       width=0.2)

    layout_kw = dict(iterations=args.iterations)
    img_title = 'Nodes: %i  Edges: %i  Iterations: %i  Runtime: %s'

    if args.iterations == None:

        iters = get_iterations()

        for iteration in iters:
            starttime = time.time()

            fig = plt.figure(figsize=(10,10))
            ax = fig.add_subplot(111)
            nx.draw(ppiGraph, pos=nx.spring_layout(ppiGraph, iterations=iteration ), **draw_kwargs )

            runtime = time.time() - starttime
            runtime_str = str(round(runtime, 2))+' seconds' 

            print "Iterations: %i  Run-time: %s "%(iteration, runtime_str)

            subs = (len(ppiGraph), args.edges, iteration, runtime_str)
            ax.set_title(img_title%subs)

            filepath = 'graphs'+'/'+zeros(iteration, padlength=4)+'.png'
            
            plt.savefig(filepath,  bbox_inches='tight')
            print 'Written to: '+filepath

            fig.clf()
            plt.close()
    else:
        starttime = time.time()

        fig = plt.figure(figsize=(10,10))
        ax = fig.add_subplot(111)
        nx.draw(ppiGraph, pos=nx.spring_layout(ppiGraph, **layout_kw ), **draw_kwargs )

        runtime = time.time() - starttime
        runtime_str = str(round(runtime, 2))+' seconds' 

        subs = (len(ppiGraph), args.edges, args.iterations, runtime_str)
        ax.set_title(img_title%subs)

        filepath = 'graphs'+'/'+zeros(args.iterations, padlength=4)+'.png'
        plt.savefig(filepath,  bbox_inches='tight')
        print 'Written to: '+filepath
        fig.clf()
        plt.close()
Exemple #10
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            misc.msg_log[len(misc.msg_log) - config.logsize:] = []
        for i in self.client_roster:
            if i != except_jid and self.client_roster[i]['to'] and self.client_roster[i]['subscription'] == 'both' and self.client_roster[i].resources:
                misc.check_time(self, misc.data['quiet'], i)
                if misc.check_time(self, misc.data['stop'], i) and (i not in misc.data['block'] or except_jid not in misc.data['block'][i]):
                    try:
                        self.send_message(mto=i, mbody=body, mtype='chat')
                    except Exception:
                        pass

if __name__ == '__main__':
    signal.signal(signal.SIGTERM, lambda signal, frame: sys.exit())  # handle SIGTERM, graceful exit

    misc.restarting = False
    misc.quiting = False
    misc.load_data()

    logging.basicConfig(level=config.loglevel)

    for i in ('stop', 'quiet', 'block'):
        if i not in misc.data:
            misc.data[i] = {}
    if config.store_log:
        if 'msg_log' in misc.data:
            misc.msg_log = misc.data['msg_log']
        else:
            misc.data['msg_log'] = misc.msg_log
        if 'cmd_log' in misc.data:
            misc.cmd_log = misc.data['cmd_log']
        else:
            misc.data['cmd_log'] = misc.cmd_log