def trainSVMHistory(configFilename, paramsFilename, outputHistoryFilename,
className):
config = yaml.load(open(configFilename).read())
params = yaml.load(open(paramsFilename).read())['model']
if params.pop('classifier') != 'svm':
raise Exception('Can only use this script on SVM config parameters.')
preproc = params.pop('preprocessing')
ds = DataSet()
ds.load(
join(
split(configFilename)[0], # base dir
config['datasetsDirectory'], # datasets dir
'%s-%s.db' % (config['className'], preproc))) # dataset name
gt = GroundTruth.fromFile(config['groundtruth'])
if className:
gt.className = className
# add 'highlevel.' in front of the descriptor, this is what will appear in the final Essentia sigfile
gt.className = 'highlevel.' + gt.className
# do the whole training
h = trainSVM(ds, gt, **params)
h.save(outputHistoryFilename)
def train_svm_history(project, params, output_file_path):
params_model = params["model"]
if params_model.get("classifier") != "svm":
raise GaiaWrapperException(
"Can only use this script on SVM config parameters.")
ds = DataSet()
ds.load(
os.path.join(
project["datasetsDirectory"], "%s-%s.db" %
(project["className"], params_model["preprocessing"])))
gt = GroundTruth.fromFile(project["groundtruth"])
gt.className = "highlevel." + project["className"]
history = train_svm(
ds,
gt,
type=params_model["type"],
kernel=params_model["kernel"],
C=params_model["C"],
gamma=params_model["gamma"]) # doing the whole training
if isinstance(output_file_path, unicode):
output_file_path = output_file_path.encode("utf-8")
history.save(output_file_path)
def train_svm_history(project, params, output_file_path):
params_model = params["model"]
if params_model.pop("classifier") != "svm":
raise GaiaWrapperException("Can only use this script on SVM config parameters.")
ds = DataSet()
ds.load(os.path.join(
project["datasetsDirectory"],
"%s-%s.db" % (project["className"], params_model.pop("preprocessing"))
))
gt = GroundTruth.fromFile(project["groundtruth"])
gt.className = "highlevel." + project["className"]
history = train_svm(ds, gt, **params_model) # doing the whole training
history.save(output_file_path)
def loadGroundTruth(self, name=None):
gttypes = self._config['groundTruth'].keys()
if name is None:
name = gttypes[0]
if len(gttypes) > 1:
print 'WARNING: more than 1 GroundTruth file, selecting default "%s" (out of %s)' % (
name, gttypes)
else:
if name not in gttypes:
print 'WARNING: invalid ground truth: "%s", selecting default one instead: "%s" (out of %s)' % (
name, gttypes[0], gttypes)
name = gttypes[0]
self._groundTruthFile = self.groundTruthFilePath(name)
self.groundTruth = GroundTruth.fromFile(self._groundTruthFile)
def loadGroundTruth(self, name=None):
gttypes = self._config["groundTruth"].keys()
if name is None:
name = gttypes[0]
if len(gttypes) > 1:
print 'WARNING: more than 1 GroundTruth file, selecting default "%s" (out of %s)' % (name, gttypes)
else:
if name not in gttypes:
print 'WARNING: invalid ground truth: "%s", selecting default one instead: "%s" (out of %s)' % (
name,
gttypes[0],
gttypes,
)
name = gttypes[0]
self._groundTruthFile = self.groundTruthFilePath(name)
self.groundTruth = GroundTruth.fromFile(self._groundTruthFile)
def train_svm_history(project, params, output_file_path):
params_model = params["model"]
if params_model.get("classifier") != "svm":
raise GaiaWrapperException("Can only use this script on SVM config parameters.")
ds = DataSet()
ds.load(os.path.join(
project["datasetsDirectory"],
"%s-%s.db" % (project["className"], params_model["preprocessing"])
))
gt = GroundTruth.fromFile(project["groundtruth"])
gt.className = "highlevel." + project["className"]
history = train_svm(ds, gt, type=params_model["type"], kernel=params_model["kernel"],
C=params_model["C"], gamma=params_model["gamma"]) # doing the whole training
if isinstance(output_file_path, unicode):
output_file_path = output_file_path.encode("utf-8")
history.save(output_file_path)
