def test_batch_dataset(_):
Constant.MAX_ITER_NUM = 1
Constant.MAX_MODEL_NUM = 4
Constant.SEARCH_MAX_ITER = 1
Constant.T_MIN = 0.8
data_path = 'tests/resources'
clean_dir(TEST_TEMP_DIR)
csv_file_path = os.path.join(data_path, "images_test/images_name.csv")
image_path = os.path.join(data_path, "images_test/Color_images")
train_dataset = BatchDataset(csv_file_path, image_path, has_target=True)
test_dataset = BatchDataset(csv_file_path, image_path, has_target=True)
train_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=False)
test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False)
cnn = CnnModule(classification_loss, Accuracy, {}, TEST_TEMP_DIR, True)
cnn.fit(2, (4, 250, 250, 3), train_dataloader, test_dataloader,
12 * 60 * 60)
clean_dir(TEST_TEMP_DIR)
class ImageSupervised(Supervised):
"""The image classifier class.
It is used for image classification. It searches convolutional neural network architectures
for the best configuration for the dataset.
Attributes:
path: A path to the directory to save the classifier.
y_encoder: An instance of OneHotEncoder for `y_train` (array of categorical labels).
verbose: A boolean value indicating the verbosity mode.
searcher: An instance of BayesianSearcher. It searches different
neural architecture to find the best model.
searcher_args: A dictionary containing the parameters for the searcher's __init__ function.
augment: A boolean value indicating whether the data needs augmentation. If not define, then it
will use the value of Constant.DATA_AUGMENTATION which is True by default.
"""
def __init__(self, verbose=False, path=None, resume=False, searcher_args=None, augment=None):
"""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.
augment: A boolean value indicating whether the data needs augmentation. If not define, then it
will use the value of Constant.DATA_AUGMENTATION which is True by default.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = temp_folder_generator()
self.cnn = CnnModule(self.loss, self.metric, searcher_args, path, verbose)
if augment is None:
augment = Constant.DATA_AUGMENTATION
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle_from_file(os.path.join(path, 'classifier'))
self.__dict__ = classifier.__dict__
self.path = path
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.searcher = False
self.path = path
self.searcher_args = searcher_args
self.augment = augment
ensure_dir(path)
@property
@abstractmethod
def metric(self):
pass
@property
@abstractmethod
def loss(self):
pass
def fit(self, x_train=None, y_train=None, time_limit=None):
"""Find the best neural architecture and train it.
Based on the given dataset, the function will find the best neural architecture for it.
The dataset is in numpy.ndarray format.
So they training data should be passed through `x_train`, `y_train`.
Args:
x_train: A numpy.ndarray instance containing the training data.
y_train: A numpy.ndarray instance containing the label of the training data.
time_limit: The time limit for the search in seconds.
"""
if y_train is None:
y_train = []
if x_train is None:
x_train = []
x_train = np.array(x_train)
y_train = np.array(y_train).flatten()
_validate(x_train, y_train)
y_train = self.transform_y(y_train)
# Transform x_train
if self.data_transformer is None:
self.data_transformer = DataTransformer(x_train, augment=self.augment)
# Divide training data into training and testing data.
x_train, x_test, y_train, y_test = train_test_split(x_train, y_train,
test_size=min(Constant.VALIDATION_SET_SIZE,
int(len(y_train) * 0.2)),
random_state=42)
# Wrap the data into DataLoaders
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
# Save the classifier
pickle.dump(self, open(os.path.join(self.path, 'classifier'), 'wb'))
pickle_to_file(self, os.path.join(self.path, 'classifier'))
if time_limit is None:
time_limit = 24 * 60 * 60
self.cnn.fit(self.get_n_output_node(), x_train.shape, train_data, test_data, time_limit)
@abstractmethod
def get_n_output_node(self):
pass
def transform_y(self, y_train):
return y_train
def predict(self, x_test):
"""Return predict results for the testing data.
Args:
x_test: An instance of numpy.ndarray containing the testing data.
Returns:
A numpy.ndarray containing the results.
"""
if Constant.LIMIT_MEMORY:
pass
test_loader = self.data_transformer.transform_test(x_test)
model = self.cnn.best_model
model.eval()
outputs = []
with torch.no_grad():
for index, inputs in enumerate(test_loader):
outputs.append(model(inputs).numpy())
output = reduce(lambda x, y: np.concatenate((x, y)), outputs)
return self.inverse_transform_y(output)
def inverse_transform_y(self, output):
return output
def evaluate(self, x_test, y_test):
"""Return the accuracy score between predict value and `y_test`."""
y_predict = self.predict(x_test)
return self.metric().evaluate(y_test, y_predict)
def save_searcher(self, searcher):
pickle.dump(searcher, open(os.path.join(self.path, 'searcher'), 'wb'))
def load_searcher(self):
return pickle_from_file(os.path.join(self.path, 'searcher'))
def final_fit(self, x_train, y_train, x_test, y_test, trainer_args=None, retrain=False):
"""Final training after found the best architecture.
Args:
x_train: A numpy.ndarray of training data.
y_train: A numpy.ndarray of training targets.
x_test: A numpy.ndarray of testing data.
y_test: A numpy.ndarray of testing targets.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
retrain: A boolean of whether reinitialize the weights of the model.
"""
if trainer_args is None:
trainer_args = {'max_no_improvement_num': 30}
y_train = self.transform_y(y_train)
y_test = self.transform_y(y_test)
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
self.cnn.final_fit(train_data, test_data, trainer_args, retrain)
def get_best_model_id(self):
""" Return an integer indicating the id of the best model."""
return self.load_searcher().get_best_model_id()
def export_keras_model(self, model_file_name):
""" Exports the best Keras model to the given filename. """
self.load_searcher().load_best_model().produce_keras_model().save(model_file_name)
def export_autokeras_model(self, model_file_name):
""" Creates and Exports the AutoKeras model to the given filename. """
portable_model = PortableImageSupervised(graph=self.load_searcher().load_best_model(),
y_encoder=self.y_encoder,
data_transformer=self.data_transformer,
metric=self.metric,
inverse_transform_y_method=self.inverse_transform_y)
pickle_to_file(portable_model, model_file_name)
class TextClassifier(Supervised):
def __init__(self, verbose=False, path=None, resume=False, searcher_args=None):
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = temp_folder_generator()
self.cnn = CnnModule(self.loss, self.metric, searcher_args, path, verbose)
self.path = path
if has_file(os.path.join(self.path, 'text_classifier')) and resume:
classifier = pickle_from_file(os.path.join(self.path, 'text_classifier'))
self.__dict__ = classifier.__dict__
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
def fit(self, x, y, x_test=None, y_test=None, batch_size=None, time_limit=None):
"""Find the best neural architecture and train it.
Based on the given dataset, the function will find the best neural architecture for it.
The dataset is in numpy.ndarray format.
So they training data should be passed through `x_train`, `y_train`.
Args:
x: A numpy.ndarray instance containing the training data.
y: A numpy.ndarray instance containing the label of the training data.
time_limit: The time limit for the search in seconds.
y_test:
x_test:
"""
x = text_preprocess(x, path=self.path)
x = np.array(x)
y = np.array(y)
validate_xy(x, y)
y = self.transform_y(y)
if batch_size is None:
batch_size = Constant.MAX_BATCH_SIZE
# Divide training data into training and testing data.
if x_test is None or y_test is None:
x_train, x_test, y_train, y_test = train_test_split(x, y,
test_size=min(Constant.VALIDATION_SET_SIZE,
int(len(y) * 0.2)),
random_state=42)
else:
x_train = x
y_train = y
# Wrap the data into DataLoaders
if self.data_transformer is None:
self.data_transformer = TextDataTransformer()
train_data = self.data_transformer.transform_train(x_train, y_train, batch_size=batch_size)
test_data = self.data_transformer.transform_test(x_test, y_test)
# Save the classifier
pickle.dump(self, open(os.path.join(self.path, 'text_classifier'), 'wb'))
pickle_to_file(self, os.path.join(self.path, 'text_classifier'))
if time_limit is None:
time_limit = 24 * 60 * 60
self.cnn.fit(self.get_n_output_node(), x_train.shape, train_data, test_data, time_limit)
def final_fit(self, x_train=None, y_train=None, x_test=None, y_test=None, trainer_args=None, retrain=False):
"""Final training after found the best architecture.
Args:
x_train: A numpy.ndarray of training data.
y_train: A numpy.ndarray of training targets.
x_test: A numpy.ndarray of testing data.
y_test: A numpy.ndarray of testing targets.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
retrain: A boolean of whether reinitialize the weights of the model.
"""
if trainer_args is None:
trainer_args = {'max_no_improvement_num': 30}
if x_test is None:
x_train, x_test, y_train, y_test = train_test_split(x_train, y_train,
test_size=min(Constant.VALIDATION_SET_SIZE,
int(len(y_train) * 0.2)),
random_state=42)
x_train = text_preprocess(x_train, path=self.path)
x_test = text_preprocess(x_test, path=self.path)
y_train = self.transform_y(y_train)
y_test = self.transform_y(y_test)
train_data = self.data_transformer.transform_train(x_train, y_train, batch_size=Constant.MAX_BATCH_SIZE)
test_data = self.data_transformer.transform_test(x_test, y_test, batch_size=Constant.MAX_BATCH_SIZE)
self.cnn.final_fit(train_data, test_data, trainer_args, retrain)
def predict(self, x_test):
"""Return predict results for the testing data.
Args:
x_test: An instance of numpy.ndarray containing the testing data.
Returns:
A numpy.ndarray containing the results.
"""
if Constant.LIMIT_MEMORY:
pass
test_loader = self.data_transformer.transform_test(x_test)
model = self.cnn.best_model.produce_model()
model.eval()
outputs = []
with torch.no_grad():
for index, inputs in enumerate(test_loader):
outputs.append(model(inputs).numpy())
output = reduce(lambda x, y: np.concatenate((x, y)), outputs)
return self.inverse_transform_y(output)
def evaluate(self, x_test, y_test):
x_test = text_preprocess(x_test, path=self.path)
"""Return the accuracy score between predict value and `y_test`."""
y_predict = self.predict(x_test)
return self.metric().evaluate(y_test, y_predict)
@property
def metric(self):
return Accuracy
@property
def loss(self):
return classification_loss
def transform_y(self, y_train):
# Transform y_train.
if self.y_encoder is None:
self.y_encoder = OneHotEncoder()
self.y_encoder.fit(y_train)
y_train = self.y_encoder.transform(y_train)
return y_train
def inverse_transform_y(self, output):
return self.y_encoder.inverse_transform(output)
def load_searcher(self):
return pickle_from_file(os.path.join(self.path, 'searcher'))
def get_n_output_node(self):
return self.y_encoder.n_classes