def __init__(self, verbose=False, path=None, resume=False, searcher_args=None,
search_type=BayesianSearcher):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
searcher_args: A dictionary containing the parameters for the searcher's __init__ function.
search_type: A constant denoting the type of hyperparameter search algorithm that must be used.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = rand_temp_folder_generator()
self.path = path
ensure_dir(path)
if resume:
classifier = pickle_from_file(os.path.join(self.path, 'classifier'))
self.__dict__ = classifier.__dict__
self.cnn = pickle_from_file(os.path.join(self.path, 'module'))
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.cnn = CnnModule(self.loss, self.metric, searcher_args, path, verbose, search_type)
def __init__(self, verbose=False, path=None, resume=False, searcher_args=None,
search_type=BayesianSearcher):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
searcher_args: A dictionary containing the parameters for the searcher's __init__ function.
search_type: A constant denoting the type of hyperparameter search algorithm that must be used.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = rand_temp_folder_generator()
self.path = path
ensure_dir(path)
if resume:
classifier = pickle_from_file(os.path.join(self.path, 'classifier'))
self.__dict__ = classifier.__dict__
self.cnn = pickle_from_file(os.path.join(self.path, 'module'))
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.cnn = CnnModule(self.loss, self.metric, searcher_args, path, verbose, search_type)
def __init__(self, verbose=False, path=None):
"""Initialize the instance.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
"""
super().__init__(verbose)
if path is None:
path = rand_temp_folder_generator()
self.path = path
def __init__(self, loss, metric, searcher_args=None, path=None, verbose=False, search_type=BayesianSearcher):
self.searcher_args = searcher_args if searcher_args is not None else {}
self.searcher = None
self.path = path if path is not None else rand_temp_folder_generator()
ensure_dir(self.path)
if verbose:
print('Saving Directory:', self.path)
self.verbose = verbose
self.loss = loss
self.metric = metric
self.generators = []
self.search_type = search_type
def __init__(self, loss, metric, searcher_args=None, path=None, verbose=False, search_type=BayesianSearcher):
self.searcher_args = searcher_args if searcher_args is not None else {}
self.searcher = None
self.path = path if path is not None else rand_temp_folder_generator()
ensure_dir(self.path)
if verbose:
print('Saving Directory:', self.path)
self.verbose = verbose
self.loss = loss
self.metric = metric
self.generators = []
self.search_type = search_type
def __init__(self, verbose=False, path=None):
"""Initialize the instance of the SingleModelSupervised class.
Args:
verbose: A boolean of whether the search process will be printed to stdout. (optional, default = False)
path: A string. The path to a directory, where the intermediate results are saved. (optional, default = None)
"""
super().__init__(verbose)
if path is None:
path = rand_temp_folder_generator()
self.path = path
self.graph = None
self.data_transformer = None
def __init__(self, graph, y_encoder, data_transformer, verbose=False, path=None):
"""Initialize the instance.
Args:
graph: The graph form of the learned model
"""
super(PortableDeepSupervised, self).__init__(graph, verbose)
self.y_encoder = y_encoder
self.data_transformer = data_transformer
if path is None:
path = rand_temp_folder_generator()
self.path = path
def __init__(self, y_encoder=OneHotEncoder, data_transformer_class=ImageDataTransformer,
verbose=False,
path=None):
self.graph = None
self.generator = None
self.loss = classification_loss
self.metric = Accuracy
self.y_encoder = y_encoder()
self.data_transformer_class = data_transformer_class
self.data_transformer = None
self.verbose = verbose
if path is None:
path = rand_temp_folder_generator()
self.path = path
def __init__(self, path=None, **kwargs):
"""
Initialization function for tabular supervised learner.
"""
super().__init__(**kwargs)
self.is_trained = False
self.clf = None
self.objective = None
self.tabular_preprocessor = None
self.path = path if path is not None else rand_temp_folder_generator()
ensure_dir(self.path)
if self.verbose:
print('Path:', path)
self.save_filename = os.path.join(self.path, 'lgbm.txt')
self.time_limit = None
self.lgbm = None
def __init__(self, path=None, **kwargs):
"""
Initialization function for tabular supervised learner.
"""
super().__init__(**kwargs)
self.is_trained = False
self.clf = None
self.objective = None
self.tabular_preprocessor = None
self.path = path if path is not None else rand_temp_folder_generator()
ensure_dir(self.path)
if self.verbose:
print('Path:', path)
self.save_filename = os.path.join(self.path, 'lgbm.txt')
self.time_limit = None
self.lgbm = None
def __init__(self, graph, y_encoder, data_transformer, verbose=False, path=None):
"""Initialize the instance.
Args:
graph: The graph form of the learned model.
y_encoder: The encoder of the label. See example as OneHotEncoder
data_transformer: A transformer class to process the data. See example as ImageDataTransformer.
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
"""
super(PortableDeepSupervised, self).__init__(graph, verbose)
self.y_encoder = y_encoder
self.data_transformer = data_transformer
if path is None:
path = rand_temp_folder_generator()
self.path = path
def __init__(self,
y_encoder=OneHotEncoder,
data_transformer_class=ImageDataTransformer,
verbose=False,
path=None):
self.graph = None
self.generator = None
self.loss = classification_loss
self.metric = Accuracy
self.y_encoder = y_encoder()
self.data_transformer_class = data_transformer_class
self.data_transformer = None
self.verbose = verbose
if path is None:
path = rand_temp_folder_generator()
self.path = path
def __init__(self, graph, y_encoder, data_transformer, verbose=False, path=None):
"""Initialize the instance.
Args:
graph: The graph form of the learned model.
y_encoder: The encoder of the label. See example as OneHotEncoder
data_transformer: A transformer class to process the data. See example as ImageDataTransformer.
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
"""
super(PortableDeepSupervised, self).__init__(graph, verbose)
self.y_encoder = y_encoder
self.data_transformer = data_transformer
if path is None:
path = rand_temp_folder_generator()
self.path = path
def __init__(self, path=None):
"""
This constructor is supposed to initialize data members.
Use triple quotes for function documentation.
"""
super().__init__()
self.is_trained = False
self.clf = None
self.save_filename = None
self.objective = 'multiclass'
self.tabular_preprocessor = None
if path is None:
path = rand_temp_folder_generator()
print('Path:', path)
self.path = path
self.time_limit = None
self.datainfo = None
def test_rand_temp_folder_generator(_):
path = rand_temp_folder_generator()
assert path.find("tests/resources/temp/autokeras_") != -1
clean_dir(path)
def test_rand_temp_folder_generator(_):
path = rand_temp_folder_generator()
assert path.find("tests/resources/temp/autokeras_") != -1
clean_dir(path)
