def main_predict(model_initializer, args):
iterator = model_initializer.load_data(args)
from itertools import tee
iterator, iterator_ = tee(iterator)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
predictions = model.decision_function(iterator)
text = []
for p in predictions:
text.append(str(p) + "\n")
save_output(text=text, output_dir_path=args.output_dir_path, out_file_name='predictions.txt')
text = []
for p in predictions:
if p > 0:
prediction = 1
else:
prediction = -1
text.append(str(prediction) + "\n")
save_output(text=text, output_dir_path=args.output_dir_path, out_file_name='classifications.txt')
text = []
from itertools import izip
info_iterator = model.get_info(iterator_)
for p, info in izip(predictions, info_iterator):
text.append("%.4f\t%s\n" % (p, info))
save_output(text=text, output_dir_path=args.output_dir_path, out_file_name='info.txt')
def main_estimate(model_initializer, args):
pos_test_iterator = model_initializer.load_positive_data(args)
neg_test_iterator = model_initializer.load_negative_data(args)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
apr, rocauc = model.estimate(pos_test_iterator, neg_test_iterator)
def main_feature(model_initializer, args):
iterator = model_initializer.load_data(args)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
X = model._data_matrix(iterator)
store_matrix(matrix=X, output_dir_path=args.output_dir_path, out_file_name='data_matrix', output_format=args.output_format)
def main_estimate(model_initializer, args):
pos_test_iterator = model_initializer.load_positive_data(args)
neg_test_iterator = model_initializer.load_negative_data(args)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
apr, rocauc = model.estimate(pos_test_iterator, neg_test_iterator)
def main_matrix(model_initializer, args):
iterator = model_initializer.load_data(args)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
X = model._data_matrix(iterator)
K = metrics.pairwise.pairwise_kernels(X, metric='linear')
store_matrix(matrix=K, output_dir_path=args.output_dir_path, out_file_name='Gram_matrix', output_format=args.output_format)
def main_feature(model_initializer, args):
iterator = model_initializer.load_data(args)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
data_matrix = model._data_matrix(iterator)
store_matrix(matrix=data_matrix,
output_dir_path=args.output_dir_path,
out_file_name='data_matrix',
output_format=args.output_format)
def main_matrix(model_initializer, args):
iterator = model_initializer.load_data(args)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
X = model._data_matrix(iterator)
K = metrics.pairwise.pairwise_kernels(X, metric='linear')
store_matrix(matrix=K,
output_dir_path=args.output_dir_path,
out_file_name='Gram_matrix',
output_format=args.output_format)
def main_predict(model_initializer, args):
iterator = model_initializer.load_data(args)
from itertools import tee
iterator, iterator_ = tee(iterator)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
text = []
for margin, graph_info in model.decision_function_info(iterator, key='id'):
if margin > 0:
prediction = 1
else:
prediction = -1
text.append("%d\t%s\t%s\n" % (prediction, margin, graph_info))
save_output(text=text, output_dir_path=args.output_dir_path, out_file_name='predictions.txt')
def main_predict(model_initializer, args):
iterator = model_initializer.load_data(args)
from itertools import tee
iterator, iterator_ = tee(iterator)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
text = []
for margin, graph_info in model.decision_function_info(iterator, key='id'):
if margin > 0:
prediction = 1
else:
prediction = -1
text.append("%d\t%s\t%s\n" % (prediction, margin, graph_info))
save_output(text=text,
output_dir_path=args.output_dir_path,
out_file_name='predictions.txt')
def main_predict(model_initializer, args):
iterator = model_initializer.load_data(args)
from itertools import tee
iterator, iterator_ = tee(iterator)
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(args.model_file)
logger.info(model.get_parameters())
predictions = model.decision_function(iterator)
text = []
for p in predictions:
text.append(str(p) + "\n")
save_output(text=text,
output_dir_path=args.output_dir_path,
out_file_name='predictions.txt')
text = []
for p in predictions:
if p > 0:
prediction = 1
else:
prediction = -1
text.append(str(prediction) + "\n")
save_output(text=text,
output_dir_path=args.output_dir_path,
out_file_name='classifications.txt')
text = []
from itertools import izip
info_iterator = model.get_info(iterator_)
for p, info in izip(predictions, info_iterator):
text.append("%.4f\t%s\n" % (p, info))
save_output(text=text,
output_dir_path=args.output_dir_path,
out_file_name='info.txt')
def train_dbox_model(fasta_fname=None, model_fname='eden_model_Dbox',window=4, neg_size_factor=5, train_test_split=0.7, n_jobs=4, n_iter=40):
#transform sequences in a linear graph
def pre_process_graph(iterator):
from eden.converter.fasta import sequence_to_eden
graphs = sequence_to_eden(iterator)
return graphs
#extract box sequence with annotaded header information
def extract_box(data,window=3,box_type='D'):
import re
from eden.converter.fasta import fasta_to_sequence
seqs = fasta_to_sequence(data)
for seq in seqs:
header = seq[0].split('_')
cbox = header[-4]
cpos = int(header[-3])
dbox = header[-2]
dpos = int(header[-1])
nts = re.sub('\n','',seq[1])
if box_type == 'C':
box = nts[cpos-1-window:cpos+6+window]
else:
if (not((len(nts) < dpos+3+window) or (dpos-1-window < 0))):
#box = nts[dpos-1-window:dpos+3+window]
box = nts[dpos-1-window:dpos-1]+'x'+nts[dpos-1:dpos+3]+'y'+nts[dpos+3:dpos+3+window]
yield seq[0],box
#Choose the vectorizer
from eden.graph import Vectorizer
vectorizer = Vectorizer()
#Choose the estimator
from sklearn.linear_model import SGDClassifier
estimator = SGDClassifier(class_weight='auto', shuffle = True, average=True )
import random
from eden import util
################Generate positive samples###############
seqs_d_pos = extract_box(fasta_fname,window,box_type='D')
from itertools import tee
seqs_d_pos,seqs_d_pos_ = tee(seqs_d_pos)
#################Generate negatives samples##############
from eden.modifier.seq import seq_to_seq, shuffle_modifier
seqs_d_neg = seq_to_seq( seqs_d_pos_, modifier=shuffle_modifier, times=neg_size_factor, order=2 )
#####################split train/test####################
from eden.util import random_bipartition_iter
iterable_pos_train, iterable_pos_test = random_bipartition_iter(seqs_d_pos, relative_size=train_test_split)
iterable_neg_train, iterable_neg_test = random_bipartition_iter(seqs_d_neg, relative_size=train_test_split)
iterable_pos_train = list(iterable_pos_train)
iterable_pos_test = list(iterable_pos_test)
iterable_neg_train = list(iterable_neg_train)
iterable_neg_test = list(iterable_neg_test)
print "training pos ",len(iterable_pos_train)
print "training neg ",len(iterable_neg_train)
print "test pos ",len(iterable_pos_test)
print "test neg ",len(iterable_neg_test)
#make predictive model
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel(pre_processor=pre_process_graph,
estimator=estimator,
vectorizer=vectorizer,
n_jobs=n_jobs)
#optimize hyperparameters and fit model
from numpy.random import randint
from numpy.random import uniform
vectorizer_parameters={'complexity':[2,3]}
estimator_parameters={'n_iter':randint(5, 100, size=n_iter),
'penalty':['l1','l2','elasticnet'],
'l1_ratio':uniform(0.1,0.9, size=n_iter),
'loss':['log'],#'hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'],
'power_t':uniform(0.1, size=n_iter),
'alpha': [10**x for x in range(-8,0)],
'eta0': [10**x for x in range(-4,-1)],
'learning_rate': ["invscaling", "constant", "optimal"],
'n_jobs':[n_jobs]}
model.optimize(iterable_pos_train, iterable_neg_train,
model_name=model_fname,
max_total_time=60*30, n_iter=n_iter,
cv=10,
score_func=lambda avg_score,std_score : avg_score - std_score * 2,
scoring='roc_auc',
vectorizer_parameters=vectorizer_parameters,
estimator_parameters=estimator_parameters)
#estimate predictive performance
print model.get_parameters()
result,text = model.estimate( iterable_pos_test, iterable_neg_test )
rss=0
i = 0
for prob in result:
i=i+1
print prob
if (prob[1] == 1):
rss = rss + ((1 - prob[0][1])**2)
else:
rss = rss + ((1 - prob[0][0])**2)
avg_rss= rss/i;
text.append('RSS: %.2f' % rss)
text.append('avg RSS: %2f' % avg_rss)
for t in text:
print t
def train_stem_finder_model(fasta,model_stem_name,window_c,model_c_name,window_d, model_d_name,flank_size_l,flank_size_r, train_test_split=0.7,neg_size_factor = 4, n_jobs=4, n_iter=40,fasta_test=None):
########### Pre processor ####################
def pre_process_graph(iterator, **options):
from eden.converter.rna.rnasubopt import rnasubopt_to_eden
graphs = rnasubopt_to_eden(iterator, **options)
return graphs
########## Vectorizer ########################
from eden.graph import Vectorizer
vectorizer = Vectorizer()
######### Estimator #########################
from sklearn.linear_model import SGDClassifier
estimator = SGDClassifier(class_weight='auto', shuffle = True, average=True )
def get_Cbox(seqs,window_c):
import re
for seq in seqs:
header = seq[0].split('_')
cpos = int(header[-3])
nts = re.sub('\n','',seq[1])
if (not((len(nts) < cpos+6+window_c) or (cpos-1-window_c < 0))):
box = nts[cpos-1-window_c:cpos-1]+'x'+nts[cpos-1:cpos+6]+'y'+nts[cpos+6:cpos+6+window_c]
yield [seq,cpos],box
def get_Dbox(seqs_c,window_d):
import re
for [seq,cbox],pred in seqs_c:
header = seq[0][0].split('_')
dpos = int(header[-1])
nts = re.sub('\n','',seq[0][1])
if (not((len(nts) < dpos+3+window_d) or (dpos-1-window_d < 0))):
box = nts[dpos-1-window_d:dpos-1]+'x'+nts[dpos-1:dpos+3]+'y'+nts[dpos+3:dpos+3+window_d]
yield [seq,cbox,pred,dpos],box
######### Get stem #########################
def get_stem(seqs,window_c,model_c_name,window_d, model_d_name,flank_size_l,flank_size_r):
from itertools import izip
import re
from itertools import tee,islice
#1)c_finder
seqs_c = get_Cbox(seqs,window_c)
#2)submit the Cbox candidates to the model
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel()
model.load(model_c_name)
seqs_c_pred = list()
cands_c = list()
max_count = 0
for seq_c in seqs_c:
max_count +=1
cands_c.append(seq_c)
if (max_count == 10000): #in order to not generate memory leak I've restricted the number of samples to be submited to the model
preds = model.decision_function(cands_c)
seqs_c_pred = seqs_c_pred + zip(cands_c,preds)
cands_c = list()
max_count = 0
if (max_count != 0):
preds = model.decision_function(cands_c)
seqs_c_pred = seqs_c_pred + zip(cands_c,preds)
#discard sequences with pred < 0
seqs_c = list()
for cand in seqs_c_pred:
if (cand[1] >= 0.0):
seqs_c.append(cand)
#D_finder
seqs_cd = get_Dbox(seqs_c,window_d)
#submit Dboxes candidate to its model
model = ActiveLearningBinaryClassificationModel()
model.load(model_d_name)
seqs_d_pred = list()
cands_d = list()
max_count = 0
for seq_d in seqs_cd:
max_count +=1
cands_d.append(seq_d)
if (max_count == 10000): #in order to not generate memory leak I've restricted the number of samples to be submited to the model
preds = model.decision_function(cands_d)
seqs_d_pred = seqs_d_pred + zip(cands_d,preds)
cands_d = list()
max_count = 0
if (max_count != 0):
preds = model.decision_function(cands_d)
seqs_d_pred = seqs_d_pred + zip(cands_d,preds)
#Get the stem region from the sequences
stem_cands=[]
stem_info =[]
#(([[(header, seq), pos_c], cand_c, pred_c, pos_d], 'UAAxCUGAyGAU'), 77.000434164559792)
for ([[(header,nts),pos_c],cand_c,pred_c,pos_d],cand_d),pred_d in seqs_d_pred:
#print header,'\t',seq,pos_c,'\t',cand_c,'\t',pred_c,'\t',cand_d,'\t',pred_d,"\n---\n"
if ( int(pos_c) - 10 < 0):
if (int(pos_d)+10 > len(nts)):
stem_cands.append([[header,pos_c,pos_d],nts[0:int(pos_c)+6]+"&"+nts[int(pos_d)-1:len(nts)]])
else:
stem_cands.append([[header,pos_c,pos_d],nts[0:int(pos_c)+6]+"&"+nts[int(pos_d)-1:int(pos_d)+3+10]])
else:
if (int(pos_d)+10 > len(nts)):
stem_cands.append([[header,pos_c,pos_d],nts[int(pos_c)-10:int(pos_c)+6]+"&"+nts[int(pos_d)-1:len(nts)]])
else:
stem_cands.append([[header,pos_c,pos_d],nts[int(pos_c)-10:int(pos_c)+6]+"&"+nts[int(pos_d)-1:int(pos_d)+3+10]])
return stem_cands
#get positive data
pos_cds=[]
from eden.converter.fasta import fasta_to_sequence
seqs = fasta_to_sequence(fasta)
train_pos = get_stem(seqs,window_c,model_c_name,window_d, model_d_name,flank_size_l,flank_size_r)
train_pos = list(train_pos)
#for h,seq in stems_cds:
# print h[0][0:10],'\t',seq
#Generate Negative Dataset
from eden.modifier.seq import seq_to_seq, shuffle_modifier
train_neg = seq_to_seq(train_pos, modifier=shuffle_modifier, times=neg_size_factor, order=2 )
train_neg = list(train_neg)
#######Split the data into training and test
if (fasta_test == None):
print "Training and Test with the same dataset (different sequences)"
#split train/test
from eden.util import random_bipartition_iter
iterable_pos_train, iterable_pos_test = random_bipartition_iter(train_pos, relative_size=train_test_split)
iterable_neg_train, iterable_neg_test = random_bipartition_iter(train_neg, relative_size=train_test_split)
iterable_pos_train = list(iterable_pos_train)
iterable_neg_train = list(iterable_neg_train)
iterable_pos_test = list(iterable_pos_test)
iterable_neg_test = list(iterable_neg_test)
else:
print "test dataset = ",fasta_test,"\n"
pos_test_cds=[]
neg_test_cds=[]
from eden.converter.fasta import fasta_to_sequence
seqs = fasta_to_sequence(fasta_test)
test_pos = get_stem(seqs,window_c,model_c_name,window_d, model_d_name,flank_size_l,flank_size_r)
test_pos = list(test_pos)
#Generate Negative test data
test_neg = seq_to_seq(test_pos, modifier=shuffle_modifier, times=neg_size_factor, order=2 )
test_neg = list(test_neg)
iterable_pos_train = list(train_pos)
iterable_neg_train = list(train_neg)
iterable_pos_test = list(test_pos)
iterable_neg_test = list(test_neg)
print "Positive training samples: ",len(iterable_pos_train)
print "Negative training samples: ",len(iterable_neg_train)
print "--------\nPositive test samples: ",len(iterable_pos_test)
print "Negative test samples: ",len(iterable_neg_test)
#make predictive model
from eden.model import ActiveLearningBinaryClassificationModel
model = ActiveLearningBinaryClassificationModel(pre_processor=pre_process_graph,
estimator=estimator,
vectorizer=vectorizer,
n_jobs=n_jobs)
#optimize hyperparameters and fit model:
from numpy.random import randint
from numpy.random import uniform
pre_processor_parameters={'energy_range':[3,4,5,6,7,8,9,10],
'max_num_subopts':randint(100,200,size=n_iter),
'max_num': [3,4,5,6,7,8]}
vectorizer_parameters={'complexity':[2,3]}
estimator_parameters={'n_iter':randint(5, 100, size=n_iter),
'penalty':['l1','l2','elasticnet'],
'l1_ratio':uniform(0.1,0.9, size=n_iter),
'loss':['log'],#'hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'],
'power_t':uniform(0.1, size=n_iter),
'alpha': [10**x for x in range(-8,0)],
'eta0': [10**x for x in range(-4,-1)],
'learning_rate': ["invscaling", "constant", "optimal"],
'n_jobs':[n_jobs]}
model.optimize(iterable_pos_train, iterable_neg_train,
model_name=model_stem_name,
max_total_time=60*60*24, n_iter=n_iter,
n_active_learning_iterations=3,
cv=10,
score_func=lambda avg_score,std_score : avg_score - std_score * 2,
scoring='roc_auc',
pre_processor_parameters = pre_processor_parameters,
vectorizer_parameters=vectorizer_parameters,
estimator_parameters=estimator_parameters)
#estimate predictive performance
print model.get_parameters()
result,text = model.estimate( iterable_pos_test, iterable_neg_test )
rss=0
i = 0
for prob in result:
i=i+1
#print prob
if (prob[1] == 1):
rss = rss + ((1 - prob[0][1])**2)
else:
rss = rss + ((1 - prob[0][0])**2)
avg_rss= rss/i;
text.append('RSS: %.2f' % rss)
text.append('avg RSS: %2f' % avg_rss)
for t in text:
print t