In [9]:

    

%matplotlib inline
import numpy as np
import pandas as pd
import networkx as nx
import seaborn as sns
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt

sns.set(style="ticks")
np.random.seed(sum(map(ord, "axis_grids")))
graph = nx.karate_club_graph()
tips = sns.load_dataset("tips")

print tips
# tips = graph.degree()
g = sns.FacetGrid(tips, col="sex")


    

    




     total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
5         25.29  4.71    Male     No   Sun  Dinner     4
6          8.77  2.00    Male     No   Sun  Dinner     2
7         26.88  3.12    Male     No   Sun  Dinner     4
8         15.04  1.96    Male     No   Sun  Dinner     2
9         14.78  3.23    Male     No   Sun  Dinner     2
10        10.27  1.71    Male     No   Sun  Dinner     2
11        35.26  5.00  Female     No   Sun  Dinner     4
12        15.42  1.57    Male     No   Sun  Dinner     2
13        18.43  3.00    Male     No   Sun  Dinner     4
14        14.83  3.02  Female     No   Sun  Dinner     2
15        21.58  3.92    Male     No   Sun  Dinner     2
16        10.33  1.67  Female     No   Sun  Dinner     3
17        16.29  3.71    Male     No   Sun  Dinner     3
18        16.97  3.50  Female     No   Sun  Dinner     3
19        20.65  3.35    Male     No   Sat  Dinner     3
20        17.92  4.08    Male     No   Sat  Dinner     2
21        20.29  2.75  Female     No   Sat  Dinner     2
22        15.77  2.23  Female     No   Sat  Dinner     2
23        39.42  7.58    Male     No   Sat  Dinner     4
24        19.82  3.18    Male     No   Sat  Dinner     2
25        17.81  2.34    Male     No   Sat  Dinner     4
26        13.37  2.00    Male     No   Sat  Dinner     2
27        12.69  2.00    Male     No   Sat  Dinner     2
28        21.70  4.30    Male     No   Sat  Dinner     2
29        19.65  3.00  Female     No   Sat  Dinner     2
..          ...   ...     ...    ...   ...     ...   ...
214       28.17  6.50  Female    Yes   Sat  Dinner     3
215       12.90  1.10  Female    Yes   Sat  Dinner     2
216       28.15  3.00    Male    Yes   Sat  Dinner     5
217       11.59  1.50    Male    Yes   Sat  Dinner     2
218        7.74  1.44    Male    Yes   Sat  Dinner     2
219       30.14  3.09  Female    Yes   Sat  Dinner     4
220       12.16  2.20    Male    Yes   Fri   Lunch     2
221       13.42  3.48  Female    Yes   Fri   Lunch     2
222        8.58  1.92    Male    Yes   Fri   Lunch     1
223       15.98  3.00  Female     No   Fri   Lunch     3
224       13.42  1.58    Male    Yes   Fri   Lunch     2
225       16.27  2.50  Female    Yes   Fri   Lunch     2
226       10.09  2.00  Female    Yes   Fri   Lunch     2
227       20.45  3.00    Male     No   Sat  Dinner     4
228       13.28  2.72    Male     No   Sat  Dinner     2
229       22.12  2.88  Female    Yes   Sat  Dinner     2
230       24.01  2.00    Male    Yes   Sat  Dinner     4
231       15.69  3.00    Male    Yes   Sat  Dinner     3
232       11.61  3.39    Male     No   Sat  Dinner     2
233       10.77  1.47    Male     No   Sat  Dinner     2
234       15.53  3.00    Male    Yes   Sat  Dinner     2
235       10.07  1.25    Male     No   Sat  Dinner     2
236       12.60  1.00    Male    Yes   Sat  Dinner     2
237       32.83  1.17    Male    Yes   Sat  Dinner     2
238       35.83  4.67  Female     No   Sat  Dinner     3
239       29.03  5.92    Male     No   Sat  Dinner     3
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2

[244 rows x 7 columns]






    

In [5]:

    

import matplotlib
matplotlib.style.use('ggplot')
import matplotlib.pyplot as plt
import numpy as np

import networkx as nx

from phoenix import phoenix, helpers

from pprint import PrettyPrinter as pp
pp = pp(2).pprint

cliques = nx.find_cliques(graph)
cliques = list(cliques)


    

In [35]:

    

print 'Summary: '
print ' |V|: ',len(graph.nodes())
print ' |g.cliques()|: ', len(cliques)
print ' largest clique',  max([len(x) for x in cliques])

# this the sampling frequency is twice the largest clique size
fs = 2*max([len(x) for x in cliques])
print fs


    

    




Summary: 
 |V|:  1589
 |g.cliques()|:  741
 largest clique 20
40

In [85]:

    

from matplotlib import pyplot, patches

def draw_adjacency_matrix(G, node_order=None, partitions=[], colors=[]):
    """
    - G is a networkx graph
    - node_order (optional) is a list of nodes, where each node in G
          appears exactly once
    - partitions is a list of node lists, where each node in G appears
          in exactly one node list
    - colors is a list of strings indicating what color each
          partition should be
    If partitions is specified, the same number of colors needs to be
    specified.
    """
    adjacency_matrix = nx.to_numpy_matrix(G, dtype=np.bool, nodelist=node_order)

    #Plot adjacency matrix in toned-down black and white
    fig = pyplot.figure(figsize=(5, 5)) # in inches
    pyplot.imshow(adjacency_matrix,
                  cmap="Greys",
                  interpolation="none")
    
    # The rest is just if you have sorted nodes by a partition and want to
    # highlight the module boundaries
    assert len(partitions) == len(colors)
    ax = pyplot.gca()
    for partition, color in zip(partitions, colors):
        current_idx = 0
        for module in partition:
            ax.add_patch(patches.Rectangle((current_idx, current_idx),
                                          len(module), # Width
                                          len(module), # Height
                                          facecolor="none",
                                          edgecolor=color,
                                          linewidth="1"))
            current_idx += len(module)


A = nx.adjacency_matrix(graph);
G = nx.from_scipy_sparse_matrix(A);
draw_adjacency_matrix(G); 
plt.title('Input Graph\'s Adjacency Matrix');

data = [len(x) for x in cliques]
count,bins = np.histogram(data)

fig, ax = plt.subplots()
rects1 = ax.bar(bins[:-1],count,width = np.mean(np.diff(bins))/2.)
ax.set_ylabel('Cliques Count')
ax.set_xlabel('Clique Size')


    

    
Out[85]:





<matplotlib.text.Text at 0x11848d7d0>






    













    

In [87]:

    

one_to_two_model, two_to_one_intxn_model = phoenix.compress(cliques)

one_to_two_model = helpers.normalize_distributions(one_to_two_model)
two_to_one_intxn_model = helpers.normalize_distributions(two_to_one_intxn_model)


clq_numb_dist = helpers.clique_number_distribution(cliques)
clqs_x_clqs_dist = helpers.cliques_x_cliques_distribution(cliques)

clq_numb_dist = helpers.normalize_distribution(clq_numb_dist)
clqs_x_clqs_dist = helpers.normalize_distributions(clqs_x_clqs_dist)


    

In [92]:

    

generated_graph = nx.Graph()
generated_graph.add_edge(0,1)
seeds = list(nx.find_cliques(generated_graph))
#seeds
# generated_graph
# clq_numb_dist
# clqs_x_clqs_dist
# pheonix.generate_hypergraph
gen_graph, pwrgrid_hedges =phoenix.generate_hypergraph(generated_graph,
                                                       one_to_two_model, two_to_one_intxn_model,
                                                       clq_numb_dist, clqs_x_clqs_dist, 1000);


    

    




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('node_count', 319)






    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-92-4c6b2985d512> in <module>()
      9 gen_graph, pwrgrid_hedges =phoenix.generate_hypergraph(generated_graph,
     10                                                        one_to_two_model, two_to_one_intxn_model,
---> 11                                                        clq_numb_dist, clqs_x_clqs_dist, 1000);

/Users/saguinag/ToolSet/Phoenix/Phoenix/PhoenixPython/phoenix/phoenix.pyc in generate_hypergraph(seed_graph, one_to_two_model, two_to_one_intxn_model, clique_substitution_model, outer_intxn_size_model, node_limit, debug)
     69     while node_count < node_limit:
     70 
---> 71         node_count = len(set(nx.utils.flatten(clqs)))
     72 
     73         if node_count >= node_limit:

/Users/saguinag/ToolSet/anaconda/lib/python2.7/site-packages/networkx/utils/misc.pyc in flatten(obj, result)
     55             result.append(item)
     56         else:
---> 57             flatten(item, result)
     58     return obj.__class__(result)
     59 

/Users/saguinag/ToolSet/anaconda/lib/python2.7/site-packages/networkx/utils/misc.pyc in flatten(obj, result)
     56         else:
     57             flatten(item, result)
---> 58     return obj.__class__(result)
     59 
     60 def is_list_of_ints( intlist ):

KeyboardInterrupt: 

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print 'Seed size (Nbr of nodes)', len(seed)


    

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