def make_feature_arrays(ipl):
params = ipl.get_params()
thisparams = rdict(params['random_forest'])
targetfile = params['resultfolder'] + params['resultsfile']
# Load the necessary images
load_images(ipl)
ipl.logging('\nInitial datastructure: \n\n{}',
ipl.datastructure2string(maxdepth=3))
result = IPL()
evaluation = rdict()
for d, k, v, kl in ipl.data_iterator(yield_short_kl=True):
if k == '0':
ipl.logging(
'===============================\nWorking on group: {}', kl)
# TODO: Implement copy full logger
ipl[kl].set_logger(ipl.get_logger())
# Load the image data into memory
ipl[kl].populate()
# def shp(x):
# return x.shape
# print ipl[kl]['0', 'true']
# print ipl[kl].dss(function=shp)
ipl[kl]['0', 'true'] = libip.rf_make_feature_array(ipl[kl]['0',
'true'])
ipl.logging(
"Computed feature array for ['0', 'true'] with shape {}",
ipl[kl]['0', 'true'].shape)
ipl[kl]['0',
'false'] = libip.rf_make_feature_array(ipl[kl]['0',
'false'])
ipl.logging(
"Computed feature array for ['0', 'false'] with shape {}",
ipl[kl]['0', 'false'].shape)
ipl[kl]['1', 'true'] = libip.rf_make_feature_array(ipl[kl]['1',
'true'])
ipl.logging(
"Computed feature array for ['1', 'true'] with shape {}",
ipl[kl]['1', 'true'].shape)
ipl[kl]['1',
'false'] = libip.rf_make_feature_array(ipl[kl]['1',
'false'])
ipl.logging(
"Computed feature array for ['1', 'false'] with shape {}",
ipl[kl]['1', 'false'].shape)
ipl.write(filepath=params['intermedfolder'] + 'feature_arrays.h5',
keys=[kl])
def make_feature_arrays(ipl):
params = ipl.get_params()
thisparams = rdict(params['random_forest'])
targetfile = params['resultfolder'] + params['resultsfile']
# Load the necessary images
load_images(ipl)
ipl.logging('\nInitial datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
result = IPL()
evaluation = rdict()
for d, k, v, kl in ipl.data_iterator(yield_short_kl=True):
if k == '0':
ipl.logging('===============================\nWorking on group: {}', kl)
# TODO: Implement copy full logger
ipl[kl].set_logger(ipl.get_logger())
# Load the image data into memory
ipl[kl].populate()
# def shp(x):
# return x.shape
# print ipl[kl]['0', 'true']
# print ipl[kl].dss(function=shp)
ipl[kl]['0', 'true'] = libip.rf_make_feature_array(ipl[kl]['0', 'true'])
ipl.logging("Computed feature array for ['0', 'true'] with shape {}", ipl[kl]['0', 'true'].shape)
ipl[kl]['0', 'false'] = libip.rf_make_feature_array(ipl[kl]['0', 'false'])
ipl.logging("Computed feature array for ['0', 'false'] with shape {}", ipl[kl]['0', 'false'].shape)
ipl[kl]['1', 'true'] = libip.rf_make_feature_array(ipl[kl]['1', 'true'])
ipl.logging("Computed feature array for ['1', 'true'] with shape {}", ipl[kl]['1', 'true'].shape)
ipl[kl]['1', 'false'] = libip.rf_make_feature_array(ipl[kl]['1', 'false'])
ipl.logging("Computed feature array for ['1', 'false'] with shape {}", ipl[kl]['1', 'false'].shape)
ipl.write(filepath=params['intermedfolder'] + 'feature_arrays.h5', keys=[kl])
def random_forest(ipl, debug=False):
params = ipl.get_params()
thisparams = rdict(params['random_forest'])
targetfile = params['resultfolder'] + params['resultsfile']
# Load the necessary images
load_images(ipl)
ipl.logging('\nInitial datastructure: \n\n{}', ipl.datastructure2string(maxdepth=3))
result = IPL()
new_eval = rdict()
evaluation = rdict()
for d, k, v, kl in ipl.data_iterator(yield_short_kl=True):
if k == '0':
ipl.logging('===============================\nWorking on group: {}', kl)
# TODO: Implement copy full logger
ipl[kl].set_logger(ipl.get_logger())
# Load the image data into memory
ipl[kl].populate()
# def shp(x):
# return x.shape
# print ipl[kl]['0', 'true']
# print ipl[kl].dss(function=shp)
ipl[kl]['0', 'true'] = libip.rf_make_feature_array(ipl[kl]['0', 'true'])
ipl.logging("Computed feature array for ['0', 'true'] with shape {}", ipl[kl]['0', 'true'].shape)
ipl[kl]['0', 'false'] = libip.rf_make_feature_array(ipl[kl]['0', 'false'])
ipl.logging("Computed feature array for ['0', 'false'] with shape {}", ipl[kl]['0', 'false'].shape)
ipl[kl]['1', 'true'] = libip.rf_make_feature_array(ipl[kl]['1', 'true'])
ipl.logging("Computed feature array for ['1', 'true'] with shape {}", ipl[kl]['1', 'true'].shape)
ipl[kl]['1', 'false'] = libip.rf_make_feature_array(ipl[kl]['1', 'false'])
ipl.logging("Computed feature array for ['1', 'false'] with shape {}", ipl[kl]['1', 'false'].shape)
# print '...'
# print ipl[kl]['0']
result[kl + ['0']] = libip.random_forest(ipl[kl]['0'], ipl[kl]['1'], debug=debug)
result[kl + ['1']] = libip.random_forest(ipl[kl]['1'], ipl[kl]['0'], debug=debug)
new_eval[kl + ['0']] = libip.new_eval([x[0] for x in result[kl]['0']], [x[1] for x in result[kl]['0']])
new_eval[kl + ['1']] = libip.new_eval([x[0] for x in result[kl]['1']], [x[1] for x in result[kl]['1']])
evaluation[kl + ['0']] = libip.evaluation(result[kl]['0'])
evaluation[kl + ['1']] = libip.evaluation(result[kl]['1'])
ipl.logging('+++ RESULTS +++')
ipl.logging("[kl]['0']")
# for i in result[kl]['0']:
# ipl.logging('{}', i)
for key, value in evaluation[kl]['0'].iteritems():
ipl.logging('{} = {}', key, value)
for key, value in new_eval[kl]['0'].iteritems():
ipl.logging('{} = {}', key, value)
ipl.logging('+++')
ipl.logging("[kl]['1']")
# for i in result[kl]['1']:
# ipl.logging('{}', i)
for key, value in evaluation[kl]['1'].iteritems():
ipl.logging('{} = {}', key, value)
for key, value in new_eval[kl]['1'].iteritems():
ipl.logging('{} = {}', key, value)
# # Write the result to file
# ipl.write(filepath=targetfile, keys=[kl])
# Free memory
ipl[kl] = None
return IPL(data=result), IPL(data=evaluation)
def random_forest(ipl, debug=False):
params = ipl.get_params()
thisparams = rdict(params['random_forest'])
targetfile = params['resultfolder'] + params['resultsfile']
# Load the necessary images
load_images(ipl)
ipl.logging('\nInitial datastructure: \n\n{}',
ipl.datastructure2string(maxdepth=3))
result = IPL()
new_eval = rdict()
evaluation = rdict()
for d, k, v, kl in ipl.data_iterator(yield_short_kl=True):
if k == '0':
ipl.logging(
'===============================\nWorking on group: {}', kl)
# TODO: Implement copy full logger
ipl[kl].set_logger(ipl.get_logger())
# Load the image data into memory
ipl[kl].populate()
# def shp(x):
# return x.shape
# print ipl[kl]['0', 'true']
# print ipl[kl].dss(function=shp)
ipl[kl]['0', 'true'] = libip.rf_make_feature_array(ipl[kl]['0',
'true'])
ipl.logging(
"Computed feature array for ['0', 'true'] with shape {}",
ipl[kl]['0', 'true'].shape)
ipl[kl]['0',
'false'] = libip.rf_make_feature_array(ipl[kl]['0',
'false'])
ipl.logging(
"Computed feature array for ['0', 'false'] with shape {}",
ipl[kl]['0', 'false'].shape)
ipl[kl]['1', 'true'] = libip.rf_make_feature_array(ipl[kl]['1',
'true'])
ipl.logging(
"Computed feature array for ['1', 'true'] with shape {}",
ipl[kl]['1', 'true'].shape)
ipl[kl]['1',
'false'] = libip.rf_make_feature_array(ipl[kl]['1',
'false'])
ipl.logging(
"Computed feature array for ['1', 'false'] with shape {}",
ipl[kl]['1', 'false'].shape)
# print '...'
# print ipl[kl]['0']
result[kl + ['0']] = libip.random_forest(ipl[kl]['0'],
ipl[kl]['1'],
debug=debug)
result[kl + ['1']] = libip.random_forest(ipl[kl]['1'],
ipl[kl]['0'],
debug=debug)
new_eval[kl +
['0']] = libip.new_eval([x[0] for x in result[kl]['0']],
[x[1] for x in result[kl]['0']])
new_eval[kl +
['1']] = libip.new_eval([x[0] for x in result[kl]['1']],
[x[1] for x in result[kl]['1']])
evaluation[kl + ['0']] = libip.evaluation(result[kl]['0'])
evaluation[kl + ['1']] = libip.evaluation(result[kl]['1'])
ipl.logging('+++ RESULTS +++')
ipl.logging("[kl]['0']")
# for i in result[kl]['0']:
# ipl.logging('{}', i)
for key, value in evaluation[kl]['0'].iteritems():
ipl.logging('{} = {}', key, value)
for key, value in new_eval[kl]['0'].iteritems():
ipl.logging('{} = {}', key, value)
ipl.logging('+++')
ipl.logging("[kl]['1']")
# for i in result[kl]['1']:
# ipl.logging('{}', i)
for key, value in evaluation[kl]['1'].iteritems():
ipl.logging('{} = {}', key, value)
for key, value in new_eval[kl]['1'].iteritems():
ipl.logging('{} = {}', key, value)
# # Write the result to file
# ipl.write(filepath=targetfile, keys=[kl])
# Free memory
ipl[kl] = None
return IPL(data=result), IPL(data=evaluation)
