def _get_parameters_range():
text = []
text.append('\n\n\tParameters range:')
text.append('\nPre_processor:')
text.append(serialize_dict(pre_processor_parameters))
text.append('\nVectorizer:')
text.append(serialize_dict(vectorizer_parameters))
text.append('\nEstimator:')
text.append(serialize_dict(estimator_parameters))
return '\n'.join(text)
def get_parameters(self):
text = []
text.append('\n\tModel parameters:')
text.append('\nPre_processor:')
text.append(serialize_dict(self.pre_processor_args))
text.append('\nVectorizer:')
text.append(serialize_dict(self.vectorizer_args))
text.append('\nEstimator:')
text.append(serialize_dict(self.estimator_args))
return '\n'.join(text)
def get_parameters(self):
text = []
text.append('\n\tModel parameters:')
text.append('\nPre_processor:')
text.append(serialize_dict(self.pre_processor_args))
text.append('\nVectorizer:')
text.append(serialize_dict(self.vectorizer_args))
text.append('\nEstimator:')
text.append(serialize_dict(self.estimator_args))
return '\n'.join(text)
def _get_parameters_range():
text = []
text.append('\n\n\tParameters range:')
text.append('\nPre_processor:')
text.append(serialize_dict(pre_processor_parameters))
text.append('\nVectorizer:')
text.append(serialize_dict(vectorizer_parameters))
text.append('\nEstimator:')
text.append(serialize_dict(estimator_parameters))
return '\n'.join(text)
def __repr__(self):
serial = []
serial.append('Embedder2D:')
if self.compiled is True:
serial.append('compiled: yes')
serial.append('learning_rate: %.6f' % self.deepnet_learning_rate)
serial.append('n_features_hidden_factor: %d' % self.deepnet_n_features_hidden_factor)
else:
serial.append('compiled: no')
serial.append('layout: %s' % (self.layout))
serial.append('layout_prog: %s' % (self.layout_prog))
if self.layout_prog_args:
serial.append('layout_prog_args: %s' % (self.layout_prog_args))
serial.append('n_nearest_neighbor_links: %s' % (self.n_nearest_neighbor_links))
if self.n_nearest_neighbors is None:
serial.append('n_nearest_neighbors: None')
else:
serial.append('n_nearest_neighbors: %d' % self.n_nearest_neighbors)
serial.append('metric: %s' % self.metric)
if self.kwds is None or len(self.kwds) == 0:
pass
else:
serial.append('params:')
serial.append(serialize_dict(self.kwds))
serial.append('selectors [%d]:' % len(self.selectors))
for i, selector in enumerate(self.selectors):
if len(self.selectors) > 1:
serial.append('%d/%d ' % (i + 1, len(self.selectors)))
serial.append(str(selector))
return '\n'.join(serial)
def main_script(prog_name=None, logger=None):
parser = argparse_setup()
args = parser.parse_args()
if args.no_logging:
configure_logging(logger, verbosity=args.verbosity)
else:
configure_logging(logger,
verbosity=args.verbosity,
filename=args.logging_dir +
'logs_gc%.2f_len%d_num%d' %
(args.gc_content, args.length, args.num) + '.log')
logger.debug('-' * 80)
logger.debug('Program: %s' % prog_name)
logger.debug('\n')
logger.debug('Called with parameters:\n\n %s \n\n' %
serialize_dict(args.__dict__))
start_time = time.asctime(time.localtime(time.time()))
logger.info('Initializing program execution %s \n\n' % (start_time))
try:
main(args, logger)
except Exception:
import datetime
curr_time = datetime.datetime.now().strftime("%A, %d. %B %Y %I:%M%p")
logger.exception("Program run failed on %s" % curr_time)
exit(1)
finally:
end_time = time.asctime(time.localtime(time.time()))
logger.info('Executing program execution %s' % (end_time))
logger.info('-' * 80)
def __repr__(self):
serial = []
serial.append('Embedder2D:')
if self.compiled is True:
serial.append('compiled: yes')
serial.append('learning_rate: %.6f' % self.learning_rate)
serial.append('n_features_hidden_factor: %d' %
self.n_features_hidden_factor)
else:
serial.append('compiled: no')
serial.append('layout: %s' % (self.layout))
serial.append('layout_prog: %s' % (self.layout_prog))
if self.layout_prog_args:
serial.append('layout_prog_args: %s' % (self.layout_prog_args))
serial.append('n_links: %s' % (self.n_links))
if self.n_nearest_neighbors is None:
serial.append('n_nearest_neighbors: None')
else:
serial.append('n_nearest_neighbors: %d' % self.n_nearest_neighbors)
serial.append('metric: %s' % self.metric)
if self.kwds is None or len(self.kwds) == 0:
pass
else:
serial.append('params:')
serial.append(serialize_dict(self.kwds))
serial.append('selectors [%d]:' % len(self.selectors))
for i, selector in enumerate(self.selectors):
if len(self.selectors) > 1:
serial.append('%d/%d ' % (i + 1, len(self.selectors)))
serial.append(str(selector))
return '\n'.join(serial)
def main_script(model_initializer=None,
description=None,
epilog=None,
prog_name=None,
logger=None):
parser = argparse_setup(model_initializer, description, epilog)
args = parser.parse_args()
if args.no_logging:
configure_logging(logger, verbosity=args.verbosity)
else:
configure_logging(logger,
verbosity=args.verbosity,
filename=prog_name + '.log')
logger.debug('-' * 80)
logger.debug('Program: %s' % prog_name)
logger.debug('Called with parameters:\n %s' %
serialize_dict(args.__dict__))
start_time = time()
try:
main(model_initializer, args)
except Exception:
import datetime
curr_time = datetime.datetime.now().strftime("%A, %d. %B %Y %I:%M%p")
logger.exception("Program run failed on %s" % curr_time)
finally:
end_time = time()
logger.info('Elapsed time: %.1f sec', end_time - start_time)
def visualize(self, data, target, title='', region_only=False):
"""visualize."""
auc = self.score(data, target)
title += 'roc:%.2f' % (auc)
title += '\nparams:%s' % serialize_dict(self.get_params())
x2dim = self.transform(data)
x_min, x_max = x2dim[:, 0].min(), x2dim[:, 0].max()
y_min, y_max = x2dim[:, 1].min(), x2dim[:, 1].max()
b = max((x_max - x_min) / 10, (y_max - y_min) / 10) # border size
x_min, x_max = x_min - b, x_max + b
y_min, y_max = y_min - b, y_max + b
h = b / 20 # step size in the mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
grid2d = np.c_[xx.ravel(), yy.ravel()]
z = self.est2d.predict_proba(grid2d)
z = 1 - z.reshape(xx.shape)
plt.contourf(xx,
yy,
z,
cmap=plt.get_cmap('BrBG'),
alpha=.3,
levels=[0.05, 0.25, 0.5, 0.75, 0.95],
extend='both')
plt.contour(xx,
yy,
z,
levels=[-1, 0.5, 2],
colors='w',
linewidths=[.5, 4, .5],
linestyles=['solid', 'solid', 'solid'],
extend='both')
plt.contour(xx,
yy,
z,
levels=[-1, 0.5, 2],
colors='k',
linewidths=[.5, 2, .5],
linestyles=['solid', 'solid', 'solid'],
extend='both')
if region_only is False:
plt.scatter(x2dim[:, 0],
x2dim[:, 1],
alpha=.8,
c=target,
s=30,
edgecolors='k',
cmap=plt.get_cmap('gray'))
plt.title(title)
plt.grid(False)
plt.axis('off')
return self
def __repr__(self):
serial = []
serial.append('Projector:')
serial.append('metric: %s' % self.metric)
if self.kwds is None or len(self.kwds) == 0:
pass
else:
serial.append('params:')
serial.append(serialize_dict(self.kwds))
serial.append(str(self.selector))
return '\n'.join(serial)
def __repr__(self):
serial = []
serial.append('Projector:')
serial.append('metric: %s' % self.metric)
if self.kwds is None or len(self.kwds) == 0:
pass
else:
serial.append('params:')
serial.append(serialize_dict(self.kwds))
serial.append(str(self.selector))
return '\n'.join(serial)
def __repr__(self):
serial = []
serial.append(self.name)
serial.append('n_instances: %d' % (self.n_instances))
serial.append('metric: %s' % (self.metric))
if self.kwds is None or len(self.kwds) == 0:
pass
else:
serial.append('params:')
serial.append(serialize_dict(self.kwds))
serial.append('random_state: %d' % (self.random_state))
return '\n'.join(serial)
def __repr__(self):
serial = []
serial.append(self.name)
serial.append('n_instances: %d' % (self.n_instances))
serial.append('metric: %s' % (self.metric))
if self.kwds is None or len(self.kwds) == 0:
pass
else:
serial.append('params:')
serial.append(serialize_dict(self.kwds))
serial.append('random_state: %d' % (self.random_state))
return '\n'.join(serial)
def main_script(model_initializer=None, description=None, epilog=None, prog_name=None, logger=None):
parser = argparse_setup(model_initializer, description, epilog)
args = parser.parse_args()
if args.no_logging:
configure_logging(logger, verbosity=args.verbosity)
else:
configure_logging(logger, verbosity=args.verbosity, filename=prog_name + '.log')
logger.debug('-' * 80)
logger.debug('Program: %s' % prog_name)
logger.debug('Called with parameters:\n %s' % serialize_dict(args.__dict__))
start_time = time()
try:
main(model_initializer, args)
except Exception:
import datetime
curr_time = datetime.datetime.now().strftime("%A, %d. %B %Y %I:%M%p")
logger.exception("Program run failed on %s" % curr_time)
finally:
end_time = time()
logger.info('Elapsed time: %.1f sec', end_time - start_time)
def __repr__(self):
"""string."""
return serialize_dict(self.__dict__, offset='large')
def optimize(self, sequences=None, init_params=1):
"""
Function to generate a list of average of ROC mean and ROC standard deviation and its
corresponding parameters
Parameters
----------
sequences: list (default value = None)
the list of sequences generated
no_of_times_of_parameter_initialization: int (default value = 1)
number of times we call parameter generation function to randomly initialize the parameters
no_of_times_fit_predict: int (default value = 1)
number of times for a set of parameters we fit()/predict() the samples using neural network
Returns
--------
list_of_ROC_score_and_parameters:
a list of average of ROC mean and ROC standard deviation and corresponding parameters
"""
opt_net = None
min_mean_ROC = 0.0
min_std_dec_ROC = 0.0
params_to_log = dict()
# Split sequences to train and test
sequences_train = sequences[:len(sequences) / 2]
sequences_test = sequences[len(sequences) / 2:]
# Get training and testing structure matices
struct_matrix_train = self.transformer.seq_to_struct(sequences_train)
struct_matrix_test = self.transformer.seq_to_struct(sequences_test)
min_score = 1
for i in range(init_params):
# Set the network parameters
params = self.randomize(self.random_state)
# Concetenate the parameter dictionaries
parameters = dict(params.items() + self.transformer.params.items())
# instatiate neural network class
deep_neural_network = DeepNeuralNetwork(
params=parameters, seq_pre_processor=self.seq_pre_processor)
# Train the model and get the predicted matrix
predict_matrix = deep_neural_network.fit_predict(
sequences_train, sequences_test, struct_matrix_train)
# Compare the predicted to the original matrix
ROC_mean_score, ROC_std_dev_score = self.estimate_data_representation_equivalence(
struct_matrix_test, predict_matrix)
curr_score = abs(float(ROC_mean_score) - 0.5) + ROC_std_dev_score
if curr_score < min_score:
min_mean_ROC = ROC_mean_score
min_std_dev_ROC = ROC_std_dev_score
params_to_log = parameters
min_score = curr_score
opt_net = deep_neural_network
#deep_neural_network.save()
logger.info('\n\n')
logger.info('On train set:')
logger.info('AUC ROC: %.4f +- %.4f' % (min_mean_ROC, min_std_dev_ROC))
logger.info('\n\n')
logger.info('Trained and tested with parameters:\n\n %s \n\n' %
serialize_dict(params_to_log))
return opt_net
def sample(self, graph_iter,
probabilistic_core_choice=True,
score_core_choice=False,
max_size_diff=-1,
similarity=-1,
n_samples=None,
proposal_probability=False,
batch_size=10,
n_jobs=0,
target_orig_cip=False,
n_steps=50,
quick_skip_orig_cip=False,
improving_threshold=-1,
improving_linear_start=0,
accept_static_penalty=0.0,
accept_min_similarity=0.0,
select_cip_max_tries=20,
burnin=0,
backtrack=0,
include_seed=False,
keep_duplicates=False,
monitor = False):
'''
Parameters
----------
graph_iter : iterator over networkx graphs
the by nw trained preprocessor will turn them into graphwrappers
probabilistic_core_choice : bool
cores are chosen according to their frequency in the grammar...
score_core_choice : bool
cores are chosen probabilisticaly according to their score
max_size_diff : int
linear increasing penalty is applied to enforce that the graphs
stays in the desired size range
similarity : float
stop condition for sampling, stop if desired similarity is reached,
similarity meassure is weired due to high dimensionality of eden vector, be warned
n_samples : int
collect this many samples for each seed graph
proposal_probability : bool
if you are not dealing with abstract graphs you get this option;
if you want to comply to Metropolis hastings
batch_size : int
this many graphs will be processed by one instance,
(maybe i should calculate the max effective number and use that)
n_jobs : int (-1)
number of processes created used. -1 is cpu count
target_orig_cip : bool
omly replace low scoring parts of the graph.. see implementation for details
n_steps: int
sample steps
quick_skip_orig_cip : bool
for each cip on the original graph, only try one entry from the grammar.
improving_threshold : float
starting from this fraction we only accept a graph if it is better
improving_linear_start : float
starting from this fraction there is a linearly increasing penalty
to the score until the improving_threshould value
accept_static_penalty : float
decrease probability of accepting a worse graph
accept_min_similarity : in [0,1]
acceptance requirement, graphs musst be at least this similar to be accepted..
zero is ignore this
select_cip_max_tries : int
try this many times to get a cip from the original graph before declaring
the seed dead.
burnin : int
ignore this many graphs until n_samples starts collecting
backtrack : int
sometimes you generate a dead-end graph, a graph that is valid but finding a proposal is impossible.
you can take one step back this many times.
this is of questionable efficiency currently because we cant detecect
the exact place where we went wrong.
include_seed : bool
dont collect the seed as sample
keep_duplicates : bool
metropolice compliance says that we should output duplicates. but otherwise duplicates
are not interesting.
monitor : bool
enabling monitor accessible after sampling. sampler.monitors will contain all the information
Returns
-------
list of graphs
'''
self.maxbacktrack=backtrack
self.monitor = monitor
self.monitors=[]
self.accept_min_similarity=accept_min_similarity
self.proposal_probability = proposal_probability
self.similarity = similarity
if probabilistic_core_choice + score_core_choice + max_size_diff == -1 > 1:
raise Exception('choose max one cip choice strategy')
if n_samples:
self.sampling_interval = int((n_steps - burnin) / (n_samples + include_seed - 1))
else:
self.sampling_interval = 9999
self.n_steps = n_steps
self.quick_skip_orig_cip = quick_skip_orig_cip
self.n_jobs = n_jobs
self.target_orig_cip = target_orig_cip
# the user doesnt know about edge nodes.. so this needs to be done
max_size_diff = max_size_diff * 2
self.max_core_size_diff = max_size_diff
# calculating the actual steps for improving :)
self.improving_threshold = improving_threshold
if improving_threshold > 0:
self.improving_threshold = int(self.improving_threshold * self.n_steps)
self.improving_linear_start = improving_linear_start
if improving_linear_start > 0:
self.improving_linear_start = int(improving_linear_start * n_steps)
if self.improving_linear_start == self.improving_threshold:
self.improving_threshold+=1
self.improving_penalty_per_step = (1 - accept_static_penalty) / float(self.improving_threshold - self.improving_linear_start)
self.accept_static_penalty = accept_static_penalty
self.select_cip_max_tries = select_cip_max_tries
self.burnin = burnin
self.include_seed = include_seed
self.batch_size = batch_size
self.probabilistic_core_choice = probabilistic_core_choice
self.score_core_choice = score_core_choice
self.keep_duplicates = keep_duplicates
# adapt grammar to task:
self.lsgg.preprocessing(n_jobs,
max_size_diff,
probabilistic_core_choice)
if score_core_choice:
self.score_core_choice_dict = {}
for interface in self.lsgg.productions:
for core in self.lsgg.productions[interface]:
gr = self.lsgg.productions[interface][core].graph.copy()
transformed_graph = self.vectorizer.transform_single(gr)
score = self.estimatorobject.cal_estimator.predict_proba(transformed_graph)[0, 1]
self.score_core_choice_dict[core] = score
logger.debug(serialize_dict(self.__dict__))
if self.random_state is not None:
random.seed(self.random_state)
# sampling
if n_jobs in [0, 1]:
for graph in graph_iter:
#sampled_graph = self._sample(graph)
# yield sampled_graph
a,b=self._sample(graph)
for new_graph in self.return_formatter(a,b):
yield new_graph
else:
if n_jobs > 1:
pool = Pool(processes=n_jobs)
else:
pool = Pool()
sampled_graphs = pool.imap_unordered(_sample_multi, self._argbuilder(graph_iter))
for batch in sampled_graphs:
for graph,moni in batch:
for new_graph in self.return_formatter(graph,moni):
yield new_graph
pool.close()
pool.join()
def __repr__(self):
return serialize_dict(self.__dict__, offset='large')
def __str__(self):
"""String."""
return "%s:\n%s" % (self.__class__, serialize_dict(self.__dict__))
def __repr__(self):
"""string."""
return serialize_dict(self.__dict__, offset='large')
def main(args):
"""Main."""
# read variables
# if no -i is given then read from stdin
seq = args['-i']
seq = (sys.stdin.readline().strip() if args['-i'] == 'stdin' else seq)
k = int(args['-k'])
complexity = int(args['--complexity'][0])
nbits = int(args['--nbits'][0])
window_size = int(args['--window_size'][0])
window_size = min(len(seq), window_size)
max_bp_span = int(args['--max_bp_span'][0])
max_bp_span = min(len(seq), max_bp_span)
avg_bp_prob_cutoff = float(args['--avg_bp_prob_cutoff'][0])
hard_threshold = float(args['--hard_threshold'][0])
max_num_edges = int(args['--max_num_edges'][0])
no_lonely_bps = args['--no_lonely_bps']
no_nesting = args['--no_nesting']
draw = args['--draw']
jpg = args['--jpg']
svg = args['--svg']
png = args['--png']
pdf = args['--pdf']
if no_nesting is True:
nesting = False
else:
nesting = True
# setup logger
if args['--verbose']:
verbosity = 2
else:
verbosity = 1
configure_logging(logger, verbosity=verbosity, filename='log')
logger.debug(serialize_dict(args))
# setup folding algorithm
rase = StructuralStabilityEstimator(seq,
alphabet='ACGU',
k=k,
complexity=complexity,
nbits=nbits,
window_size=window_size,
max_bp_span=max_bp_span,
avg_bp_prob_cutoff=avg_bp_prob_cutoff,
hard_threshold=hard_threshold,
max_num_edges=max_num_edges,
no_lonely_bps=no_lonely_bps,
nesting=nesting)
# print: nt pos, original nt, most de-stabilizing nt, dotbracket, score
for line in rase.transform(seq):
print(line)
# if drawing is required use the folding algorithm to compute the graph
if draw:
suffix = 'pdf'
if jpg:
suffix = 'jpg'
if svg:
suffix = 'svg'
if png:
suffix = 'png'
if pdf:
suffix = 'pdf'
structure_fname = 'structure.' + suffix
score_fname = 'score.' + suffix
all_plots_fname = 'structures.' + suffix
rase.draw(file_name=structure_fname)
rase.plot(file_name=score_fname)
rase.draw_all(file_name=all_plots_fname)
