def oneclasstest_fraction(fraction=0.1, repeats=2):
# choosing some graphs,
# having array to save results
for i in range(repeats):
badscores = []
goodscores = []
graphs = get_sequences_with_names(size=923)
graphs, not_used = random_bipartition_iter(
graphs, fraction, random_state=random.random() * i * 1000)
estimator = Wrapper(nu=.27, cv=3, n_jobs=-1)
sampler = rna.AbstractSampler(radius_list=[0, 1],
thickness_list=[2],
min_cip_count=1,
min_interface_count=2,
preprocessor=rna.PreProcessor(
base_thickness_list=[1],
ignore_inserts=True),
postprocessor=rna.PostProcessor(),
estimator=estimator)
sampler.preprocessor.set_param(sampler.vectorizer)
graphmanagers = sampler.preprocessor.fit_transform(graphs)
sampler.estimatorobject.fit(graphmanagers,
vectorizer=sampler.vectorizer,
random_state=sampler.random_state)
#test
for graphman in graphmanagers:
struct = evaltools.dotbracket_to_shape(graphman.structure,
shapesversion=SHAPEVERSION)
score = sampler.estimatorobject.score(graphman)
if struct == "[[][][]]":
goodscores.append(score)
else:
badscores.append(score)
print "afraction=%f , instances=%f, good=%d , bad=%d" % (
fraction, fraction * 923, len(goodscores), len(badscores))
a = numpy.array(badscores)
print 'bad:mean/std ', numpy.mean(a, axis=0), ' ', numpy.std(a, axis=0)
a = numpy.array(goodscores)
print 'cgood:mean/std ', numpy.mean(a, axis=0), ' ', numpy.std(a,
axis=0)
a = numpy.array(goodscores + badscores)
print 'dbad+good:mean/std ', numpy.mean(a,
axis=0), ' ', numpy.std(a,
axis=0)
print ''
def fit_sample(graphs, random_state=random.random()):
'''
graphs -> more graphs
'''
graphs = list(graphs)
estimator = estimatorwrapper(nu=.5, cv=2, n_jobs=-1)
sampler = rna.AbstractSampler(radius_list=[0, 1],
thickness_list=[2],
min_cip_count=1,
min_interface_count=2,
preprocessor=rna.PreProcessor(
base_thickness_list=[1],
ignore_inserts=True),
postprocessor=rna.PostProcessor(),
estimator=estimator
#feasibility_checker=feasibility
)
sampler.fit(graphs, grammar_n_jobs=4, grammar_batch_size=1)
logger.info('graph grammar stats:')
dataset_size, interface_counts, core_counts, cip_counts = sampler.grammar(
).size()
logger.info(
'#instances:%d #interfaces: %d #cores: %d #core-interface-pairs: %d'
% (dataset_size, interface_counts, core_counts, cip_counts))
graphs = [b for a, b in graphs]
graphs = sampler.sample(graphs,
n_samples=3,
batch_size=1,
n_steps=50,
n_jobs=4,
quick_skip_orig_cip=True,
probabilistic_core_choice=True,
burnin=10,
improving_threshold=0.9,
improving_linear_start=0.3,
max_size_diff=20,
accept_min_similarity=0.65,
select_cip_max_tries=30,
keep_duplicates=False,
include_seed=False,
backtrack=10,
monitor=False)
result = []
for graphlist in graphs:
result += graphlist
# note that this is a list [('',sequ),..]
return result
def fit_sample(graphs, random_state=random.random()):
'''
graphs -> more graphs
arguments are generated above Oo
'''
global arguments
graphs = list(graphs)
estimator = estimatorwrapper(nu=.5, cv=2, n_jobs=NJOBS)
sampler = rna.AbstractSampler(
radius_list=[0, 1],
thickness_list=[2],
min_cip_count=arguments['mincipcount'],
min_interface_count=arguments['mininterfacecount'],
preprocessor=rna.PreProcessor(base_thickness_list=[1],
ignore_inserts=True),
postprocessor=rna.PostProcessor(),
estimator=estimator
#feasibility_checker=feasibility
)
sampler.fit(graphs, grammar_n_jobs=NJOBS, grammar_batch_size=1)
graphs = [b for a, b in graphs]
graphs = sampler.sample(graphs,
n_samples=arguments['n_samples'],
batch_size=1,
n_steps=arguments['n_steps'],
n_jobs=NJOBS,
quick_skip_orig_cip=arguments['quick_skip'],
probabilistic_core_choice=arguments['core_choice'],
burnin=arguments['burnin'],
improving_threshold=arguments['imp_thresh'],
improving_linear_start=arguments['imp_lin_start'],
max_size_diff=arguments['maxsizediff'],
accept_min_similarity=arguments['acc_min_sim'],
select_cip_max_tries=30,
keep_duplicates=False,
include_seed=False,
backtrack=2,
monitor=False)
result = []
for graphlist in graphs:
result += graphlist
# note that this is a list [('',sequ),..]
return result