def __init__(
self,
change_params,
lab_panel,
num_episodes,
use_cache=None,
random_state=None,
build_raw_only=False,
):
SupervisedLearningPipeline.__init__(self, lab_panel, num_episodes,
use_cache, random_state)
self._change_params = change_params
self._change_params[
'feature_old'] = self._lookup_previous_measurement_feature(
self._var)
log.debug('change_params: %s' % self._change_params)
if build_raw_only:
self._build_raw_feature_matrix()
return
else:
self._build_raw_feature_matrix()
self._build_processed_feature_matrix()
self._train_and_analyze_predictors()
def __init__(self, lab_panel, microcultures, num_episodes, use_cache=None, random_state=None):
SupervisedLearningPipeline.__init__(self, lab_panel, num_episodes, use_cache, random_state)
self.panel = microcultures
self._build_raw_feature_matrix()
self._build_processed_feature_matrix()
self._train_and_analyze_predictors()
def __init__(self, lab_panel, num_episodes, use_cache=None, random_state=None, isLabPanel=True,
notUsePatIds=[], pat_batch_ind=None):
self.notUsePatIds = notUsePatIds
self.pat_batch_ind = pat_batch_ind
self.usedPatIds = []
SupervisedLearningPipeline.__init__(self, lab_panel, num_episodes, use_cache, random_state, isLabPanel)
self._factory = FeatureMatrixFactory()
self._build_raw_feature_matrix()
def __init__(self,
lab_panel,
num_episodes,
use_cache=None,
random_state=None,
isLabPanel=True,
timeLimit=None,
notUsePatIds=None,
holdOut=False,
pat_batch_ind=None):
self.notUsePatIds = notUsePatIds
self.pat_batch_ind = pat_batch_ind
self.usedPatIds = []
SupervisedLearningPipeline.__init__(
self,
lab_panel,
num_episodes,
use_cache,
random_state,
isLabPanel,
timeLimit,
holdOut,
isLabNormalityPredictionPipeline=True)
# TODO: naming of lab_panel
self._factory = FeatureMatrixFactory()
self._build_raw_feature_matrix()
data_lab_folder = self._fetch_data_dir_path(
inspect.getfile(inspect.currentframe()))
feat2imputed_dict_path = data_lab_folder + '/feat2imputed_dict.pkl'
if holdOut:
'''
For holdOut evaluation data, produce the raw matrix, pick
features according to the saved feat2imputed_dict.
'''
self.feat2imputed_dict = pickle.load(
open(feat2imputed_dict_path, 'r'))
self._build_processed_feature_matrix_holdout()
self._analyze_predictors_on_holdout()
else:
'''
For training/validation data, record the pat_ids,
selected features and their imputed value correspondingly.
'''
pickle.dump(self.usedPatIds,
open('data/used_patient_set_%s.pkl' % self._var, 'w'),
pickle.HIGHEST_PROTOCOL)
self._build_processed_feature_matrix()
self._build_baseline_results() # TODO: prototype in SLPP
return
# TODO: find better place to put the dict.pkl
pickle.dump(self.feat2imputed_dict,
open(feat2imputed_dict_path, 'w'),
pickle.HIGHEST_PROTOCOL)
self._train_and_analyze_predictors()
def __init__(self, lab_panel, num_episodes, use_cache=None, random_state=None,
timeLimit=None, notUsePatIds=None, holdOut=False, pat_batch_ind=None, includeLastNormality=True):
# self.notUsePatIds = notUsePatIds
self.pat_batch_ind = pat_batch_ind
self._holdOut = holdOut
self.usedPatIds = []
SupervisedLearningPipeline.__init__(self, lab_panel, num_episodes, use_cache, random_state,
timeLimit, notUsePatIds)
# TODO: naming of lab_panel
self._factory = FeatureMatrixFactory()
self._build_raw_feature_matrix()
if LAB_TYPE == 'panel':
self.ylabel = 'all_components_normal'
else:
self.ylabel = 'component_normal'
self.includeLastNormality = includeLastNormality
if self.includeLastNormality:
fm_io = FeatureMatrixIO()
df = fm_io.read_file_to_data_frame('data/'+lab_panel+'/%s-normality-matrix-raw.tab'%lab_panel)
df = df.sort_values(['pat_id', 'order_time']).reset_index(drop=True)
df['last_normality'] = df['order_proc_id'].apply(lambda x:float('nan'))
for i in range(1,df.shape[0]):
if df.ix[i, 'pat_id'] == df.ix[i-1, 'pat_id']:
df.ix[i, 'last_normality'] = df.ix[i-1, self.ylabel]
df.to_csv('data/'+lab_panel+'/%s-normality-matrix-raw.tab'%lab_panel, index=False, sep='\t')
data_lab_folder = self._fetch_data_dir_path(inspect.getfile(inspect.currentframe()))
feat2imputed_dict_path = data_lab_folder + '/feat2imputed_dict.pkl'
if holdOut:
'''
For holdOut evaluation data, produce the raw matrix, pick
features according to the saved feat2imputed_dict.
'''
self.feat2imputed_dict = pickle.load(open(feat2imputed_dict_path, 'r'))
self._build_processed_feature_matrix_holdout()
self._analyze_predictors_on_holdout()
else:
'''
For training/validation data, record the pat_ids,
selected features and their imputed value correspondingly.
'''
pickle.dump(self.usedPatIds, open('data/used_patient_set_%s.pkl'%self._var, 'w'), pickle.HIGHEST_PROTOCOL)
self._build_processed_feature_matrix()
self._build_baseline_results() # TODO: prototype in SLPP
# return
# TODO: find better place to put the dict.pkl
pickle.dump(self.feat2imputed_dict, open(feat2imputed_dict_path, 'w'), pickle.HIGHEST_PROTOCOL)
self._train_and_analyze_predictors()
