def train_combined_wdspeck_svm(org_list_file, signal="tss", data_path="SRA-seq"):
"""
train a global classifier for multiple organisms
@args org_list_file: organism name in a text file
@type org_list_file: str
@args signal: genomic signal type (default: tss)
@type signal: str
@args data_path: file path for training data points
@type data_path: str
"""
t0 = time.time()
## getting multiple oragnisms
ORG_LIST = get_orgdb(org_list_file)
## loading data
train_examples = []
train_labels = []
for ORG_NAME in ORG_LIST:
local_data_path = "%s/%s/set_1" % (data_path, ORG_NAME) ## FIXME common data path
data = load_examples_from_fasta(signal, ORG_NAME, local_data_path)
assert len(data["examples"]) == len(data["labels"])
## split the data
train_examples.extend(data["examples"])
train_labels.extend(data["labels"])
## set parameters TODO
param = {}
param["cost"] = 1.0
param["degree"] = 4
param["degree_spectrum"] = 4
param["center_pos"] = 1200
param["center_offset"] = 50
param["shifts"] = 32
param["kernel_cache"] = 10000
## invoke training
svm = ShogunPredictor(param)
svm.train(train_examples, train_labels)
## save the model
fname = "%s_model_%s" % (signal, uuid.uuid1())
compressed_pickle.save(fname, svm)
print ("saving the model in file %s" % fname)
time_taken = time.time() - t0
print ("time taken for the experiment: ", time_taken)
return fname
def train_combined_wdspeck_svm(org_list_file, signal="tss", data_path="SRA-seq"):
"""
train a global classifier for multiple organisms
@args org_list_file: organism name in a text file
@type org_list_file: str
@args signal: genomic signal type (default: tss)
@type signal: str
@args data_path: file path for training data points
@type data_path: str
"""
t0 = time.time()
## getting multiple oragnisms
ORG_LIST = get_orgdb(org_list_file)
## loading data
train_examples = []
train_labels = []
for ORG_NAME in ORG_LIST:
local_data_path = "%s/%s/set_1" % (data_path, ORG_NAME) ## FIXME common data path
data = load_examples_from_fasta(signal, ORG_NAME, local_data_path)
assert(len(data["examples"]) == len(data["labels"]))
## split the data
train_examples.extend(data["examples"])
train_labels.extend(data["labels"])
## set parameters TODO
param = {}
param["cost"] = 1.0
param["degree"] = 4
param["degree_spectrum"] = 4
param["center_pos"] = 1200
param["center_offset"] = 50
param["shifts"] = 32
param["kernel_cache"] = 10000
## invoke training
svm = ShogunPredictor(param)
svm.train(train_examples, train_labels)
## save the model
fname = "%s_model_%s" % (signal, uuid.uuid1())
compressed_pickle.save(fname, svm)
print("saving the model in file %s" % fname)
time_taken = time.time() - t0
print("time taken for the experiment: ", time_taken)
return fname
def train_wdspeck_svm(org_code, signal="tss", data_path="SRA-rnaseq"):
"""
train SVM based on the examples from different sources
@args org_code: organism name (ex: A_thaliana)
@type org_code: str
@args signal: genomic signal type (default: tss)
@type signal: str
@args data_path: file path for training data points
@type data_path: str
"""
t0 = time.time()
## loading data
data = load_examples_from_fasta(signal, org_code, data_path)
assert len(data["examples"]) == len(data["labels"])
## split the data
train_examples = data["examples"]
train_labels = data["labels"]
## set parameters TODO
param = {}
param["cost"] = 1.0
param["degree"] = 4
param["degree_spectrum"] = 4
param["center_pos"] = 1200
param["center_offset"] = 50
param["shifts"] = 32
param["kernel_cache"] = 10000
## invoke training
svm = ShogunPredictor(param)
svm.train(train_examples, train_labels)
## save the model
fname = "%s_%s_model_%s" % (org_code, signal, uuid.uuid1())
compressed_pickle.save(fname, svm)
print ("saving the model in file %s" % fname)
time_taken = time.time() - t0
print ("time taken for the experiment: ", time_taken)
return fname
def train_wdspeck_svm(org_code, signal="tss", data_path="SRA-rnaseq"):
"""
train SVM based on the examples from different sources
@args org_code: organism name (ex: A_thaliana)
@type org_code: str
@args signal: genomic signal type (default: tss)
@type signal: str
@args data_path: file path for training data points
@type data_path: str
"""
t0 = time.time()
## loading data
data = load_examples_from_fasta(signal, org_code, data_path)
assert(len(data["examples"]) == len(data["labels"]))
## split the data
train_examples = data["examples"]
train_labels = data["labels"]
## set parameters TODO
param = {}
param["cost"] = 1.0
param["degree"] = 4
param["degree_spectrum"] = 4
param["center_pos"] = 1200
param["center_offset"] = 50
param["shifts"] = 32
param["kernel_cache"] = 10000
## invoke training
svm = ShogunPredictor(param)
svm.train(train_examples, train_labels)
## save the model
fname = "%s_%s_model_%s" % (org_code, signal, uuid.uuid1())
compressed_pickle.save(fname, svm)
print("saving the model in file %s" % fname)
time_taken = time.time() - t0
print("time taken for the experiment: ", time_taken)
return fname
def train_shifted_wdk_svm(org_code, signal="tss", data_path="SRA-rnaseq"):
"""
train SVM based on the examples from different sources
"""
import time
t0 = time.time()
## loading data
data = load_examples_from_fasta(signal, org_code, data_path)
assert len(data["examples"]) == len(data["labels"])
## split the data
train_examples = data["examples"]
train_labels = data["labels"]
## set parameters
param = {}
param["cost"] = 1.0
param["degree"] = 4
param["degree_spectrum"] = 4
param["center_pos"] = 1200
param["center_offset"] = 50
param["shifts"] = 32
param["kernel_cache"] = 10000
## invoke training
svm = ShogunPredictor(param)
svm.train(train_examples, train_labels)
## save the model
fname = "%s_%s_model_%s" % (org_code, signal, uuid.uuid1())
compressed_pickle.save(fname, svm)
print ("saving the model in file %s" % fname)
time_taken = time.time() - t0
print ("time taken for the experiment: ", time_taken)
return fname
