def _save_metrics_and_queried_sample(
database_class: DataBase,
current_loop: int, output_metric_file_name: str,
output_queried_file_name: str, batch: int, epoch: int,
is_save_full_query: bool):
"""
Saves metrics and queried sample data
Parameters
----------
database_class
An instance of DataBase class
current_loop
Number of learning loops finished at this stage.
output_metric_file_name
Full path to file to store metrics results.
output_queried_file_name
batch
Number of queries in each loop.
epoch
Days since the beginning of the survey.
is_save_full_query
If true, write down a complete queried sample stored in
property 'queried_sample'. Otherwise append 1 line per loop to
'queried_sample_file'. Default is False.
"""
database_class.save_metrics(
loop=current_loop, output_metrics_file=output_metric_file_name,
batch=batch, epoch=epoch)
if is_save_full_query:
output_queried_file_name = (output_queried_file_name[:-4] +
'_' + str(current_loop) + '.dat')
database_class.save_queried_sample(
output_queried_file_name, loop=current_loop,
full_sample=is_save_full_query, epoch=epoch)
def _save_metrics_and_queried_samples(database_class: DataBase,
metrics_file_name: str,
queried_file_name: str,
iteration_step: int,
batch: int,
full_sample: bool,
file_name_suffix: str = None):
"""
Save metrics and queried samples details
Parameters
----------
database_class
An instance of DataBase class
metrics_file_name
Full path to file to store metrics results.
queried_file_name
Complete path to output file.
iteration_step
active learning iteration number
batch
Number of queries in each loop.
full_sample
If true, write down a complete queried sample stored in
property 'queried_sample'. Otherwise append 1 line per loop to
'queried_sample_file'. Default is False.
file_name_suffix
suffix string for save file name with file extension
"""
if file_name_suffix is not None:
metrics_file_name = metrics_file_name.replace('.dat', file_name_suffix)
queried_file_name = queried_file_name.replace('.dat', file_name_suffix)
database_class.save_metrics(loop=iteration_step,
output_metrics_file=metrics_file_name,
batch=batch,
epoch=iteration_step)
database_class.save_queried_sample(queried_file_name,
loop=iteration_step,
full_sample=full_sample,
epoch=iteration_step,
batch=batch)
def learn_loop(nloops: int, strategy: str, path_to_features: str,
output_diag_file: str, output_queried_file: str,
features_method='Bazin', classifier='RandomForest',
training='original', batch=1, screen=True, survey='DES',
perc=0.1, nclass=2):
"""Perform the active learning loop. All results are saved to file.
Parameters
----------
nloops: int
Number of active learning loops to run.
strategy: str
Query strategy. Options are 'UncSampling' and 'RandomSampling'.
path_to_features: str or dict
Complete path to input features file.
if dict, keywords should be 'train' and 'test',
and values must contain the path for separate train
and test sample files.
output_diag_file: str
Full path to output file to store diagnostics of each loop.
output_queried_file: str
Full path to output file to store the queried sample.
features_method: str (optional)
Feature extraction method. Currently only 'Bazin' is implemented.
classifier: str (optional)
Machine Learning algorithm.
Currently only 'RandomForest' is implemented.
training: str or int (optional)
Choice of initial training sample.
If 'original': begin from the train sample flagged in the file
If int: choose the required number of samples at random,
ensuring that at least half are SN Ia
Default is 'original'.
batch: int (optional)
Size of batch to be queried in each loop. Default is 1.
screen: bool (optional)
If True, print on screen number of light curves processed.
survey: str (optional)
'DES' or 'LSST'. Default is 'DES'.
Name of the survey which characterizes filter set.
perc: float in [0,1] (optioal)
Percentile chosen to identify the new query.
Only used for PercentileSampling.
Default is 0.1.
nclass: int (optional)
Number of classes to consider in the classification
Currently only nclass == 2 is implemented.
"""
## This module will need to be expanded for RESSPECT
# initiate object
data = DataBase()
# load features
if isinstance(path_to_features, str):
data.load_features(path_to_features, method=features_method,
screen=screen, survey=survey)
# separate training and test samples
data.build_samples(initial_training=training, nclass=nclass)
else:
data.load_features(path_to_features['train'], method=features_method,
screen=screen, survey=survey, sample='train')
data.load_features(path_to_features['test'], method=features_method,
screen=screen, survey=survey, sample='test')
data.build_samples(initial_training=training, nclass=nclass,
screen=screen, sep_files=True)
for loop in range(nloops):
if screen:
print('Processing... ', loop)
# classify
data.classify(method=classifier)
# calculate metrics
data.evaluate_classification()
# choose object to query
indx = data.make_query(strategy=strategy, batch=batch, perc=perc)
# update training and test samples
data.update_samples(indx, loop=loop)
# save diagnostics for current state
data.save_metrics(loop=loop, output_metrics_file=output_diag_file,
batch=batch, epoch=loop)
# save query sample to file
data.save_queried_sample(output_queried_file, loop=loop,
full_sample=False)
def time_domain_loop(days: list, output_diag_file: str,
output_queried_file: str,
path_to_features_dir: str, strategy: str,
batch=1, canonical = False, classifier='RandomForest',
features_method='Bazin', path_to_canonical="",
path_to_full_lc_features="",
screen=True, training='original'):
"""Perform the active learning loop. All results are saved to file.
Parameters
----------
days: list
List of 2 elements. First and last day of observations since the
beginning of the survey.
output_diag_file: str
Full path to output file to store diagnostics of each loop.
output_queried_file: str
Full path to output file to store the queried sample.
path_to_features_dir: str
Complete path to directory holding features files for all days.
strategy: str
Query strategy. Options are 'UncSampling' and 'RandomSampling'.
batch: int (optional)
Size of batch to be queried in each loop. Default is 1.
canonical: bool (optional)
If True, restrict the search to the canonical sample.
classifier: str (optional)
Machine Learning algorithm.
Currently only 'RandomForest' is implemented.
features_method: str (optional)
Feature extraction method. Currently only 'Bazin' is implemented.
path_to_canonical: str (optional)
Path to canonical sample features files.
It is only used if "strategy==canonical".
path_to_full_lc_features: str (optional)
Path to full light curve features file.
Only used if training is a number.
screen: bool (optional)
If True, print on screen number of light curves processed.
training: str or int (optional)
Choice of initial training sample.
If 'original': begin from the train sample flagged in the file
If int: choose the required number of samples at random,
ensuring that at least half are SN Ia
Default is 'original'.
"""
## This will need to change for RESSPECT
# initiate object
data = DataBase()
# load features for the first day
path_to_features = path_to_features_dir + 'day_' + str(int(days[0])) + '.dat'
data.load_features(path_to_features, method=features_method,
screen=screen)
# change training
if training == 'original':
data.build_samples(initial_training='original')
full_lc_features = get_original_training(path_to_features=path_to_full_lc_features)
data.train_metadata = full_lc_features.train_metadata
data.train_labels = full_lc_features.train_labels
data.train_features = full_lc_features.train_features
else:
data.build_samples(initial_training=int(training))
# get list of canonical ids
if canonical:
canonical = DataBase()
canonical.load_features(path_to_file=path_to_canonical)
data.queryable_ids = canonical.queryable_ids
for night in range(int(days[0]), int(days[-1]) - 1):
if screen:
print('Processing night: ', night)
# cont loop
loop = night - int(days[0])
# classify
data.classify(method=classifier)
# calculate metrics
data.evaluate_classification()
# choose object to query
indx = data.make_query(strategy=strategy, batch=batch)
# update training and test samples
data.update_samples(indx, loop=loop)
# save diagnostics for current state
data.save_metrics(loop=loop, output_metrics_file=output_diag_file,
batch=batch, epoch=night)
# save query sample to file
data.save_queried_sample(output_queried_file, loop=loop,
full_sample=False)
# load features for next day
path_to_features2 = path_to_features_dir + 'day_' + str(night + 1) + '.dat'
data_tomorrow = DataBase()
data_tomorrow.load_features(path_to_features2, method=features_method,
screen=False)
# identify objects in the new day which must be in training
train_flag = np.array([item in data.train_metadata['id'].values
for item in data_tomorrow.metadata['id'].values])
# use new data
data.train_metadata = data_tomorrow.metadata[train_flag]
data.train_features = data_tomorrow.features.values[train_flag]
data.test_metadata = data_tomorrow.metadata[~train_flag]
data.test_features = data_tomorrow.features.values[~train_flag]
# new labels
data.train_labels = np.array([int(item == 'Ia') for item in
data.train_metadata['type'].values])
data.test_labels = np.array([int(item == 'Ia') for item in
data.test_metadata['type'].values])
if strategy == 'canonical':
data.queryable_ids = canonical.queryable_ids
if queryable:
queryable_flag = data_tomorrow.metadata['queryable'].values
queryable_test_flag = np.logical_and(~train_flag, queryable_flag)
data.queryable_ids = data_tomorrow.metadata['id'].values[queryable_test_flag]
else:
data.queryable_ids = data_tomorrow.metadata['id'].values[~train_flag]
if screen:
print('Training set size: ', data.train_metadata.shape[0])
print('Test set size: ', data.test_metadata.shape[0])
