def _run_classification_and_evaluation(
database_class: DataBase, classifier: str,
is_classifier_bootstrap: bool, **kwargs: dict) -> DataBase:
"""
Runs active learning classification and evaluation methods
Parameters
----------
database_class
An instance of DataBase class
classifier
Machine Learning algorithm.
Currently 'RandomForest', 'GradientBoostedTrees',
'KNN', 'MLP', 'SVM' and 'NB' are implemented.
Default is 'RandomForest'.
is_classifier_bootstrap
if tp apply a machine learning classifier by bootstrapping
kwargs
All keywords required by the classifier function.
"""
if is_classifier_bootstrap:
database_class.classify_bootstrap(method=classifier, **kwargs)
else:
database_class.classify(method=classifier, **kwargs)
database_class.evaluate_classification()
return database_class
def run_classification(database_class: DataBase, classifier: str,
is_classifier_bootstrap: bool, prediction_dir: str,
is_save_prediction: bool, iteration_step: int,
**kwargs: dict) -> DataBase:
"""
Run active learning classification model
Parameters
----------
database_class
An instance of DataBase class
classifier
Machine Learning algorithm.
Currently implemented options are 'RandomForest', 'GradientBoostedTrees',
'KNNclassifier','MLPclassifier','SVMclassifier' and 'NBclassifier'.
Default is 'RandomForest'.
is_classifier_bootstrap
if tp apply a machine learning classifier by bootstrapping
prediction_dir
Output directory to store prediction file for each loop.
Only used if `save_predictions==True
is_save_prediction
if predictions should be saved
iteration_step
active learning iteration number
kwargs
All keywords required by the classifier function.
-------
"""
if is_classifier_bootstrap:
database_class.classify_bootstrap(method=classifier,
loop=iteration_step,
pred_dir=prediction_dir,
save_predictions=is_save_prediction,
**kwargs)
else:
database_class.classify(method=classifier,
pred_dir=prediction_dir,
loop=iteration_step,
save_predictions=is_save_prediction,
**kwargs)
return database_class
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])