In [1]:
%load_ext autoreload
%autoreload 2
from eden.util import configure_logging
import logging
configure_logging(logging.getLogger(),verbosity=2)
%matplotlib inline
!pwd
/home/ikea/GraphLearn/example/Abstract
In [7]:
# get data
from eden.converter.graph.gspan import gspan_to_eden
from itertools import islice
def clean(graphs):
for g in graphs:
for n,d in g.nodes(data=True):
d.pop('weight')
yield g
def get_graphs(dataset_fname='../bursi.pos.gspan', size=100):
return islice(gspan_to_eden(dataset_fname),size)
#dataset_names = !cat NCI60/names
#dataset = dataset_names[4]
#dataset_fname = 'NCI60/' + dataset + '_orig_pos.gspan'
#print 'Working with dataset: %s' % dataset
In [18]:
'''DEBUGGING PREPROCESSA'''
from graphlearn.utils import draw
import graphlearn.abstract_graphs.random_molecules as rm
from eden.graph import Vectorizer
v=Vectorizer(triangular_decomposition=False,complexity=3)
pp=rm.PreProcessor()
pp.set_param(v)
inp= list(get_graphs(size=100))
gm=pp.transform(inp)
In [19]:
from graphlearn.utils import draw
gr = [i for i in get_graphs(size=10)]
pp.re_transform_single(gr[5])
for i in range(10):
draw.graphlearn([gm[i+5].graph(),gm[i+5].base_graph(),gm[i+5].abstract_graph()],size=13,contract=True, abstract_color='red')
In [5]:
""" ITS NOT BROKEN SO THERE IS NO NEED TO FIX :)))
'''TESTING EXTRACTION AND GRAPHMANAGER'''
from graphlearn.utils import draw
import graphlearn.abstract_graphs.score_based as sb
from graphlearn.graphlearn import GraphLearnSampler as GLS
from eden.graph import Vectorizer
vectorizer=Vectorizer()
print 'DEMONSTRATING GRAPH MANAGER'
graphs=get_graphs()
for i in range(2):
print 'grammar example %d' % i
g=graphs.next()
gm=mole.MolecularGraphWrapper(g,vectorizer,base_thickness_list=[2])
#g=gm.graph(nested=True)
#print g.nodes(data=True)
#g.node[0].pop('weight')
#vec=vectorizer.transform_single(g)
draw.graphlearn([gm.graph(nested=True),gm.abstract_graph(),gm.base_graph()], size = 15,vertex_label = 'label',contract=False)
print 'DEMONSTRATING EXTRACTION'
radius_list=[0,2]
thickness_list=[2,4]
base_thickness_list=[2]
argz=(gm,radius_list,thickness_list,Vectorizer(),2**20-1,lambda x,y:True, base_thickness_list)
cips=gm.all_core_interface_pairs(thickness_list=[2],radius_list=[0,1],hash_bitmask=2**20-1)
draw.graphlearn(cips[0][0].graph, contract=False)
g=gm.graph(nested=False)
print 'test', g.nodes(data=True)[0][1]
#g.node[0].pop('weight')
vec=vectorizer.transform_single(g)
"""
Out[5]:
" ITS NOT BROKEN SO THERE IS NO NEED TO FIX :)))\n\n\n'''TESTING EXTRACTION AND GRAPHMANAGER'''\nfrom graphlearn.utils import draw\nimport graphlearn.abstract_graphs.score_based as sb\nfrom graphlearn.graphlearn import GraphLearnSampler as GLS\nfrom eden.graph import Vectorizer\n\nvectorizer=Vectorizer()\n\nprint 'DEMONSTRATING GRAPH MANAGER'\ngraphs=get_graphs()\n\nfor i in range(2):\n print 'grammar example %d' % i\n g=graphs.next()\n gm=mole.MolecularGraphWrapper(g,vectorizer,base_thickness_list=[2])\n #g=gm.graph(nested=True)\n #print g.nodes(data=True)\n #g.node[0].pop('weight')\n #vec=vectorizer.transform_single(g)\n draw.graphlearn([gm.graph(nested=True),gm.abstract_graph(),gm.base_graph()], size = 15,vertex_label = 'label',contract=False)\n\n \nprint 'DEMONSTRATING EXTRACTION' \nradius_list=[0,2]\nthickness_list=[2,4]\nbase_thickness_list=[2]\nargz=(gm,radius_list,thickness_list,Vectorizer(),2**20-1,lambda x,y:True, base_thickness_list)\n\ncips=gm.all_core_interface_pairs(thickness_list=[2],radius_list=[0,1],hash_bitmask=2**20-1)\ndraw.graphlearn(cips[0][0].graph, contract=False)\n\n\ng=gm.graph(nested=False)\nprint 'test', g.nodes(data=True)[0][1]\n#g.node[0].pop('weight')\nvec=vectorizer.transform_single(g)\n"
In [6]:
%%time
'''
learning a grammar, DEBUGGER
import graphlearn.abstract_graphs.score_based as sb
from graphlearn.graphlearn import GraphLearnSampler as GLS
graphs = get_graphs(size=100)
sampler=GLS(radius_list=[0,1],thickness_list=[1], min_cip_count=2, min_interface_count=2, preprocessor=sb.PreProcessor(base_thickness_list=[2]))
graphmanagers= sampler.preprocessor.fit_transform(graphs,sampler.vectorizer)
#sampler.fit(graphs,n_jobs=1,batch_size=1)
print sampler.preprocessor.kmeans.predict(3.0)[0]
#draw.graphlearn(graphmanagers[0].graph())
#sampler.estimatorobject.fit(graphmanagers, vectorizer=sampler.vectorizer, nu=.5, n_jobs=1)
'''
CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 300 µs
Out[6]:
'\nlearning a grammar, DEBUGGER\n\nimport graphlearn.abstract_graphs.score_based as sb\nfrom graphlearn.graphlearn import GraphLearnSampler as GLS\n\ngraphs = get_graphs(size=100)\nsampler=GLS(radius_list=[0,1],thickness_list=[1], min_cip_count=2, min_interface_count=2, preprocessor=sb.PreProcessor(base_thickness_list=[2]))\n\n\ngraphmanagers= sampler.preprocessor.fit_transform(graphs,sampler.vectorizer)\n#sampler.fit(graphs,n_jobs=1,batch_size=1)\n\n\nprint sampler.preprocessor.kmeans.predict(3.0)[0]\n#draw.graphlearn(graphmanagers[0].graph())\n#sampler.estimatorobject.fit(graphmanagers, vectorizer=sampler.vectorizer, nu=.5, n_jobs=1)\n'
In [ ]:
In [23]:
%%time
'''
learning a grammar
'''
import graphlearn.abstract_graphs.random_molecules as rm
from graphlearn.graphlearn import Sampler as GLS
graphs = get_graphs(size=300)
sampler=GLS(radius_list=[0,1],thickness_list=[1],postprocessor=rm.PostProcessor(), min_cip_count=2, min_interface_count=2, preprocessor=rm.PreProcessor(base_thickness_list=[2]))
sampler.fit(graphs,grammar_batch_size=1)
sampler.save('../tmp/mole_random.ge')
fitting was asked with n=1
Classifier:
SGDClassifier(alpha=0.000203854348509, average=False, class_weight=None,
epsilon=0.1, eta0=0.893479237267, fit_intercept=True, l1_ratio=0.15,
learning_rate='invscaling', loss='log', n_iter=79, n_jobs=1,
penalty='l1', power_t=0.847836577931, random_state=None,
shuffle=True, verbose=0, warm_start=False)
Predictive performance:
accuracy: 1.000 +- 0.000
precision: 1.000 +- 0.000
recall: 1.000 +- 0.000
f1: 1.000 +- 0.000
average_precision: 1.000 +- 0.000
roc_auc: 1.000 +- 0.000
#instances: 300 #interfaces: 98 #cores: 91 #core-interface-pairs: 404
Saved model: ../tmp/mole_random.ge
CPU times: user 24.7 s, sys: 2.04 s, total: 26.8 s
Wall time: 1min 3s
In [24]:
#draw production rules
draw.draw_grammar(sampler.lsgg.productions,n_productions=5,n_graphs_per_production=5,
n_graphs_per_line=5, size=9, contract=False,
colormap='Paired', invert_colormap=False,node_border=1,
vertex_alpha=0.6, edge_alpha=0.5, node_size=450, abstract_interface=True)
interface id: 232224 [19 options]
interface id: 897568 [14 options]
interface id: 923490 [13 options]
interface id: 929890 [12 options]
interface id: 193890 [12 options]
In [25]:
%%time
'''
Molecule sampling
'''
import os
os.nice(19)
import graphlearn.utils.draw as draw
import graphlearn.abstract_graphs.random_molecules as rm
import itertools
#sampler=mole.MolecularSampler()
#sampler.load('tmp/mole_ubergrammar.ge')
graphs = get_graphs()
id_start=15
id_end=id_start+9
graphs = itertools.islice(graphs,id_start,id_end)
n_steps=100
graphs = sampler.sample(graphs,
n_samples=5,
batch_size=1,
n_steps=n_steps,
n_jobs=1,
quick_skip_orig_cip=True,
probabilistic_core_choice=False,
burnin=0,
improving_threshold=0.5,
max_core_size_diff=6,
select_cip_max_tries=100,
keep_duplicates=True,
omit_seed=False)
scores=[]
ids=range(id_start,id_end)
for i,graph in enumerate(graphs):
print 'Graph id: %d'%(ids[i])
scores.append(graph.graph['sampling_info']['score_history'])
path_graphs = graph.graph['sampling_info']['graphs_history']
path_graphs= list(path_graphs)
draw.graphlearn(path_graphs,
n_graphs_per_line=5, size=10,
colormap='Paired', invert_colormap=False,node_border=0.5, vertex_color='color_level',
vertex_alpha=0.5, edge_alpha=0.7, node_size=450,edge_label='label',
headlinehook= draw.get_score_of_graph
)
preprocessing grammar
_sample_notes: None
accept_static_penalty: 0.0
batch_size: 1
burnin: 0
estimatorobject: <graphlearn.estimator.Wrapper instance at 0x7f40b3b48cb0>
feasibility_checker: <graphlearn.feasibility.FeasibilityChecker instance at 0x7f40b3b48cf8>
generator_mode: False
hash_bitmask: 1048575
improving_linear_start: 0
improving_penalty_per_step: 0.02
improving_threshold: 50
keep_duplicates: True
lsgg: <graphlearn.localsubstitutablegraphgrammar.LocalSubstitutableGraphGrammar object at 0x7f40b2e568d0>
max_core_size_diff: 12
maxbacktrack: 0
monitor: False
n_jobs: 1
n_steps: 100
nbit: 20
node_entity_check: <function <lambda> at 0x7f40b38daa28>
omit_seed: False
postprocessor: <graphlearn.abstract_graphs.random_molecules.PostProcessor object at 0x7f40b2e56cd0>
preprocessor: <graphlearn.abstract_graphs.random_molecules.PreProcessor object at 0x7f40b2e56450>
probabilistic_core_choice: False
proposal_probability: False
quick_skip_orig_cip: True
radius_list: [0, 2]
random_state: None
sample_path: None
sampling_interval: 25
score_core_choice: False
select_cip_max_tries: 100
similarity: -1
step: None
target_orig_cip: False
thickness_list: [2]
vectorizer: graph.Vectorizer( r = 3, d = 3, n = 1, min_r = 0, min_d = 0, min_n = 2, label_size = 1, nbits = 20, status = None, normalization = True, inner_normalization = True, triangular_decomposition = False )
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Graph id: 15
/home/ikea/.local/lib/python2.7/site-packages/matplotlib/text.py:52: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
if rotation in ('horizontal', None):
/home/ikea/.local/lib/python2.7/site-packages/matplotlib/text.py:54: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
elif rotation == 'vertical':
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Graph id: 23
CPU times: user 1min 7s, sys: 1.93 s, total: 1min 9s
Wall time: 1min 13s
In [10]:
%matplotlib inline
from itertools import islice
import numpy as np
import matplotlib.pyplot as plt
step=1
num_graphs_per_plot=3
num_plots=np.ceil([len(scores)/num_graphs_per_plot])
for i in range(num_plots):
plt.figure(figsize=(10,5))
for j,score in enumerate(scores[i*num_graphs_per_plot:i*num_graphs_per_plot+num_graphs_per_plot]):
data = list(islice(score,None, None, step))
plt.plot(data, label='graph %d'%(j+i*num_graphs_per_plot+id_start))
plt.legend(loc='lower right')
plt.grid()
plt.ylim(-0.1,1.1)
plt.show()
In [11]:
import graphlearn.abstract_graphs.molecules as mole
from graphlearn.utils import draw
from eden.graph import Vectorizer
v=Vectorizer()
from graphlearn.abstract_graphs.molecules import node_to_cycle
import networkx as nx
import eden
def make_abstract(graph):
'''
make sure this is not expanded
'''
# prepare fast hash function
def fhash(stuff):
return eden.fast_hash(stuff, 2 ** 20 - 1)
# all nodes get their cycle calculated
for n, d in graph.nodes(data=True):
d['cycle'] = list(node_to_cycle(graph, n))
d['cycle'].sort()
#if 'parent'in d:
# d.pop('parent')
# make sure most of the abstract nodes are created.
abstract_graph = nx.Graph()
for n, d in graph.nodes(data=True):
cyclash = fhash(d['cycle'])
if cyclash not in abstract_graph.node:
abstract_graph.add_node(cyclash)
abstract_graph.node[cyclash]['contracted'] = set(d['cycle'])
abstract_graph.node[cyclash]['node'] = True
# it is possible that a node belongs to more than 1 cycle, so...
# each node gets parents
for e in d['cycle']:
node = graph.node[e]
if 'parent' not in node:
node['parent'] = set()
node['parent'].add(cyclash)
# HERE THE ACTUAL ABSTRACTION BEGINS
# connect nodes in the abstract graph
get_element = lambda x: list(x)[0]
# FOR ALL ABSTRACT NODES
for n, d in abstract_graph.nodes(data=True):
# FIND A LABEL
if len(d['contracted']) > 1:
labels = [ord(graph.node[childid]['label']) for childid in d['contracted']]
labels.sort()
d['label'] = "cycle" #fhash(labels)
else:
d['label'] = graph.node[get_element(d['contracted'])]['label']
# THEN LOOK AT ALL CONTRACTED NODES TO FIND OUT WHAT CONNECTION WE HAVE TO OUR NEIGHBORS
for base_node in d['contracted']:
base_neighbors = graph.neighbors(base_node)
# for all the neighbors
for neigh in base_neighbors:
# find out if we have to build a connector node
if len(graph.node[neigh]['cycle']) > 1 and len(d['contracted']) > 1:
for other in graph.node[neigh]['parent']:
if other != n:
l = [other, n]
l.sort()
connector = fhash(l)
shared_nodes = abstract_graph.node[other]['contracted'] & d['contracted']
if len(shared_nodes)==0:
label='e'
else:
labels = [ord(graph.node[sid]['label']) for sid in shared_nodes]
labels.sort()
share_hash = fhash(labels)
label='share:'+str(share_hash)
abstract_graph.add_edge(other,n,label=label)
'''
if connector not in abstract_graph.node:
# we need to consider making the edge the actual intersect of the two...
abstract_graph.add_node(connector)
abstract_graph.node[connector]['edge'] = True
# abstract_graph.node[connector]['label']='edge'
shared_nodes = abstract_graph.node[other]['contracted'] & d['contracted']
labels = [ord(graph.node[sid]['label']) for sid in shared_nodes]
labels.sort()
share_hash = fhash(labels)
abstract_graph.node[connector]['label'] = "shared" + str(share_hash)
abstract_graph.add_edge(other, connector)
abstract_graph.add_edge(connector, n)
'''
else:
for e in graph.node[neigh]['parent']:
abstract_graph.add_edge(n, e, label='e')
return abstract_graph
def graphor(base,abstr,nested=True):
g= nx.disjoint_union(base, abstr)
node_id= len(g)
for n,d in g.nodes(data=True):
if 'contracted' in d and 'edge' not in d:
for e in d['contracted']:
if 'edge' not in g.node[e]:
# we want an edge from n to e
g.add_node(node_id,edge=True,label='e')
g.add_edge( n, node_id, nesting=True)
g.add_edge( node_id, e, nesting=True)
#g.add_edge( n, e, nesting=True)
node_id+=1
return g
gr=get_graphs()
for i in range(1):
g=gr.next()
ab=make_abstract(g)
g=v._edge_to_vertex_transform(g)
ab=v._edge_to_vertex_transform(ab)
zz=graphor(g,ab)
draw.graphlearn([g,ab], size=20)
v.transform_single(zz)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-11-582fd786c936> in <module>()
143 zz=graphor(g,ab)
144 draw.graphlearn([g,ab], size=20)
--> 145 v.transform_single(zz)
146
147
/home/ikea/.local/lib/python2.7/site-packages/eden/graph.pyc in transform_single(self, graph)
313
314 self._test_goodness(graph)
--> 315 return self._convert_dict_to_sparse_matrix(self._transform(0, graph))
316
317 def predict(self, graphs, estimator):
/home/ikea/.local/lib/python2.7/site-packages/eden/graph.pyc in _transform(self, instance_id, original_graph)
619
620 def _transform(self, instance_id, original_graph):
--> 621 graph = self._graph_preprocessing(original_graph)
622 # collect all features for all vertices for each label_index
623 feature_list = defaultdict(lambda: defaultdict(float))
/home/ikea/.local/lib/python2.7/site-packages/eden/graph.pyc in _graph_preprocessing(self, original_graph)
615 self._compute_neighborhood_graph_hash_cache(graph)
616 if graph.graph.get('weighted', False):
--> 617 self._compute_neighborhood_graph_weight_cache(graph)
618 return graph
619
/home/ikea/.local/lib/python2.7/site-packages/eden/graph.pyc in _compute_neighborhood_graph_weight_cache(self, graph)
747 for u, d in graph.nodes_iter(data=True):
748 if d.get('node', False):
--> 749 self._compute_neighborhood_graph_weight(u, graph)
750
751 def _compute_neighborhood_graph_weight(self, root, graph):
/home/ikea/.local/lib/python2.7/site-packages/eden/graph.pyc in _compute_neighborhood_graph_weight(self, root, graph)
767 # extract array of weights at given distance
768 weight_array_at_d = np.array(
--> 769 [graph.node[v][self.key_weight] for v in node_set])
770 if distance % 2 == 0: # nodes
771 node_weight_list = np.concatenate(
KeyError: 'weight'
In [3]:
import random
l=[1,2,3,4]
random.shuffle(l)
In [5]:
5/2
Out[5]:
2
In [ ]:
Content source: fabriziocosta/GraphLearn_examples
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