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)