def test_cfg_to_algorithm_pass_2(write_yaml):
""" Test ``yatsm.classifiers.cfg_to_algorithm`` with an empty config
"""
cfg = {
'algorithm': 'RandomForest',
'RandomForest': {'init': {}, 'fit': {}}
}
classifiers.cfg_to_algorithm(write_yaml(cfg))
def test_cfg_to_algorithm_fail_2(write_yaml):
""" Fail because algorithm parameters don't exist
"""
cfg = {
'algorithm': 'RandomForest',
'RandomForest': {'init': {'not_a_param': 42}, 'fit': {}}
}
with pytest.raises(TypeError):
classifiers.cfg_to_algorithm(write_yaml(cfg))
def test_cfg_to_algorithm_pass_2(write_yaml):
""" Test ``yatsm.classifiers.cfg_to_algorithm`` with an empty config
"""
cfg = {
'algorithm': 'RandomForest',
'RandomForest': {
'init': {},
'fit': {}
}
}
classifiers.cfg_to_algorithm(write_yaml(cfg))
def test_cfg_to_algorithm_fail_2(write_yaml):
""" Fail because algorithm parameters don't exist
"""
cfg = {
'algorithm': 'RandomForest',
'RandomForest': {
'init': {
'not_a_param': 42
},
'fit': {}
}
}
with pytest.raises(TypeError):
classifiers.cfg_to_algorithm(write_yaml(cfg))
def train(ctx, config, classifier_config, model, n_fold, seed, plot, diagnostics, overwrite):
"""
Train a classifier from `scikit-learn` on YATSM output and save result to
file <model>. Dataset configuration is specified by <yatsm_config> and
classifier and classifier parameters are specified by <classifier_config>.
"""
# Setup
if not model.endswith(".pkl"):
model += ".pkl"
if os.path.isfile(model) and not overwrite:
logger.error("<model> exists and --overwrite was not specified")
raise click.Abort()
if seed:
np.random.seed(seed)
# Parse config & algorithm config
cfg = parse_config_file(config)
algo, algo_cfg = classifiers.cfg_to_algorithm(classifier_config)
training_image = cfg["classification"]["training_image"]
if not training_image or not os.path.isfile(training_image):
logger.error("Training data image %s does not exist" % training_image)
raise click.Abort()
# Find information from results -- e.g., design info
attrs = find_result_attributes(cfg)
cfg["YATSM"].update(attrs)
# Cache file for training data
has_cache = False
training_cache = cfg["classification"]["cache_training"]
if training_cache:
# If doesn't exist, retrieve it
if not os.path.isfile(training_cache):
logger.info("Could not retrieve cache file for Xy")
logger.info(" file: %s" % training_cache)
else:
logger.info("Restoring X/y from cache file")
has_cache = True
training_image = cfg["classification"]["training_image"]
# Check if we need to regenerate the cache file because training data is
# newer than the cache
regenerate_cache = is_cache_old(training_cache, training_image)
if regenerate_cache:
logger.warning("Existing cache file older than training data ROI")
logger.warning("Regenerating cache file")
if not has_cache or regenerate_cache:
logger.debug("Reading in X/y")
X, y, row, col, labels = get_training_inputs(cfg)
logger.debug("Done reading in X/y")
else:
logger.debug("Reading in X/y from cache file %s" % training_cache)
with np.load(training_cache) as f:
X = f["X"]
y = f["y"]
row = f["row"]
col = f["col"]
labels = f["labels"]
logger.debug("Read in X/y from cache file %s" % training_cache)
# If cache didn't exist but is specified, create it for first time
if not has_cache and training_cache:
logger.info("Saving X/y to cache file %s" % training_cache)
try:
np.savez(training_cache, X=X, y=y, row=row, col=col, labels=labels)
except:
logger.error("Could not save X/y to cache file")
raise
# Do modeling
logger.info("Training classifier")
algo.fit(X, y, **algo_cfg.get("fit", {}))
# Serialize algorithm to file
logger.info("Pickling classifier with sklearn.externals.joblib")
joblib.dump(algo, model, compress=3)
# Diagnostics
if diagnostics:
algo_diagnostics(cfg, X, y, row, col, algo, n_fold, plot)
def test_cfg_to_algorithm_fail_3(tmpdir):
""" Fail because we don't use a YAML file
"""
f = tmpdir.mkdir('clf').join('test').strpath
with pytest.raises(IOError):
classifiers.cfg_to_algorithm(f)
def test_cfg_to_algorithm_fail_1(write_yaml):
""" Fail because algorithm in config doesn't exist
"""
cfg = {'algorithm': 'hopefully_not_an_algo'}
with pytest.raises(AlgorithmNotFoundException):
classifiers.cfg_to_algorithm(write_yaml(cfg))
def test_cfg_to_algorithm_fail_3(tmpdir):
""" Fail because we don't use a YAML file
"""
f = tmpdir.mkdir('clf').join('test').strpath
with pytest.raises(IOError):
classifiers.cfg_to_algorithm(f)
def test_cfg_to_algorithm_fail_1(write_yaml):
""" Fail because algorithm in config doesn't exist
"""
cfg = {'algorithm': 'hopefully_not_an_algo'}
with pytest.raises(AlgorithmNotFoundException):
classifiers.cfg_to_algorithm(write_yaml(cfg))
