def train(config):
with open(config.pkl_file,"rb") as fp:
data=pickle.load(fp)
fp.close()
X=data["array"]
y=data["cluster"]
print("Encoder cluster -> label ")
encoder=LabelEncoder()
encoder.fit(y)
label=encoder.transform(y)
num_classes=len(np.unique(label))
weight=compute_class_weight("balanced",np.unique(label),label)
X_train, X_test, y_train, y_test = train_test_split(X,label,test_size=config.test_ratio,shuffle=True)
print("X train : ({},{})".format(X_train.shape[0],X_train.shape[1]))
print("X test : ({},{})".format(X_test.shape[0],X_test.shape[1]))
print("Build Model")
model=AutoNetClassification(log_level='info',
cuda=False,
dataset_name="VKH_10X",
shuffle=True,
num_iterations=config.num_iter,
budget_type='epochs',
min_budget=100,
max_budget=10000,
result_logger_dir="./logger",
cross_validator="k_fold",
cross_validator_args={"n_splits": 5})
print("Training")
model.fit(X_train,y_train,validation_split=0.2)
return
def train_autopytorch(X_train,X_test,y_train,y_test,mtype,common_name_model,problemtype,classes,default_featurenames,transform_model,settings,model_session):
# name model
model_name=common_name_model+'.pickle'
files=list()
if mtype=='c':
from autoPyTorch import AutoNetClassification
autonet = AutoNetClassification(log_level='debug', max_runtime=900, min_budget=50, max_budget=150)
autonet.fit(X_train, y_train, validation_split=0.30)
print(autonet.predict(X_test).flatten())
if mtype=='r':
from autoPyTorch import AutoNetRegression
autonet = AutoNetRegression(log_level='debug', max_runtime=900, min_budget=50, max_budget=150)
autonet.fit(X_train, y_train)
print(autonet.predict(X_test).flatten())
print('saving model -->')
torch.save(autonet, model_name)
# get model directory
files.append(model_name)
files.append('configs.json')
files.append('results.json')
model_dir=os.getcwd()
return model_name, model_dir, files
def create_model(max_batch):
search_space_updates = HyperparameterSearchSpaceUpdates()
#TODO: this still runs out of memory and wastes resources
search_space_updates.append(node_name="CreateImageDataLoader", hyperparameter="batch_size", log=False, \
value_range=[2, max_batch])
shutil.rmtree(save_output_to)
autonet = AutoNetClassification(
preset, \
# hyperparameter_search_space_updates=search_space_updates, \
min_workers=2, \
# dataloader_worker=4, \
# global_results_dir="results", \
# keep_only_incumbent_checkpoints=False, \
log_level="info", \
budget_type="time", \
# save_checkpoints=True, \
result_logger_dir=save_output_to, \
min_budget=200, \
max_budget=600, \
num_iterations=1, \
# images_shape=[channels, input_size[0], input_size[1]], \
optimizer = ["adam", "adamw", "sgd", "rmsprop"], \
algorithm="hyperband", \
optimize_metric="balanced_accuracy", \
additional_metrics=["pac_metric"], \
lr_scheduler=["cosine_annealing", "cyclic", "step", "adapt", "plateau", "alternating_cosine", "exponential"], \
networks=['mlpnet', 'shapedmlpnet', 'resnet', 'shapedresnet'], #, 'densenet_flexible', 'resnet', 'resnet152', 'darts'], \
use_tensorboard_logger=True, \
cuda=True \
)
return autonet
def fit(self, data_manager):
if (data_manager.problem_type == ProblemType.FeatureRegression):
autonet = AutoNetRegression()
elif (data_manager.problem_type == ProblemType.FeatureMultilabel):
autonet = AutoNetMultilabel()
elif (data_manager.problem_type == ProblemType.FeatureClassification):
autonet = AutoNetClassification()
else:
raise ValueError('Problem type ' + str(data_manager.problem_type) +
' is not defined')
autonet.pipeline[
autonet_nodes.LogFunctionsSelector.get_name()].add_log_function(
'test_result',
test_result(autonet, data_manager.X_test, data_manager.Y_test))
metrics = autonet.pipeline[autonet_nodes.MetricSelector.get_name()]
metrics.add_metric('pac_metric', autonet_metrics.pac_metric)
metrics.add_metric('balanced_accuracy',
autonet_metrics.balanced_accuracy)
metrics.add_metric('mean_distance', autonet_metrics.mean_distance)
metrics.add_metric('multilabel_accuracy',
autonet_metrics.multilabel_accuracy)
metrics.add_metric('auc_metric', autonet_metrics.auc_metric)
metrics.add_metric('accuracy', autonet_metrics.accuracy)
return {'autonet': autonet}
additional_metrices = []
""" TEST CASE 4: Openml, missing values """
if TEST_CASE == 4:
dm.read_data("openml:188", is_classification=True)
metric = "accuracy"
additional_metrices = []
""" TEST CASE 5: MNIST """
if TEST_CASE == 5:
dm.read_data(os.path.join(dataset_dir, "classification/phpnBqZGZ.csv"), is_classification=True)
metric = "accuracy"
additional_metrices = []
# Generate autonet
autonet = AutoNetClassification() if TEST_CASE != 3 else AutoNetMultilabel()
# add metrics and test_result to pipeline
autonet.pipeline[autonet_nodes.LogFunctionsSelector.get_name()].add_log_function('test_result', test_result(autonet, dm.X_test, dm.Y_test))
metrics = autonet.pipeline[autonet_nodes.MetricSelector.get_name()]
metrics.add_metric('pac_metric', autonet_metrics.pac_metric)
metrics.add_metric('auc_metric', autonet_metrics.auc_metric)
metrics.add_metric('accuracy', autonet_metrics.accuracy)
# Fit autonet using train data
res = autonet.fit(min_budget=300,
max_budget=900, max_runtime=1800, budget_type='time',
normalization_strategies=['maxabs'],
train_metric=metric,
additional_metrics=additional_metrices,
cv_splits=3,
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
#Init custom search space
# search_space_updates = HyperparameterSearchSpaceUpdates()
# search_space_updates.append(node_name="CreateDataLoader",
# hyperparameter="batch_size",
# value_range=[32],
# log=False)
#Init autonet
# autoPyTorch = AutoNetClassification(hyperparameter_search_space_updates=search_space_updates, # config
autoPyTorch = AutoNetClassification(
# "full_cs", # config
networks=["resnet"],
# torch_num_threads=2,
log_level='info',
budget_type='epochs',
min_budget=5,
max_budget=20,
num_iterations=100,
cuda=True,
use_pynisher=False)
#fit
autoPyTorch.fit(X_train=X_train,
Y_train=y_train,
X_valid=X_test,
Y_valid=y_test,
optimize_metric="auc_metric",
loss_modules=["cross_entropy", "cross_entropy_weighted"])
#predict
y_pred = autoPyTorch.predict(X_test)
#check
dm = DataManager(verbose=1)
dataset_dir = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..', 'datasets'))
dm.read_data(os.path.join(dataset_dir, "classification/dataset_28_optdigits.csv"), is_classification=True)
# 5620 samples, 10 classes, 65 features ---> 98% validation accuracy
mem_logger = MemoryLogger()
mem_logger.start()
try:
autonet = AutoNetClassification(early_stopping_patience=15, budget_type='epochs', min_budget=1, max_budget=9, num_iterations=1, log_level='error')
res = autonet.fit(X_train=dm.X,
Y_train=dm.Y,
X_valid=dm.X_train,
Y_valid=dm.Y_train,
categorical_features=dm.categorical_features)
print(res)
finally:
mem_logger.stop()
__author__ = "Max Dippel, Michael Burkart and Matthias Urban"
__version__ = "0.0.1"
__license__ = "BSD"
import os, sys
sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..", "..")))
from autoPyTorch import AutoNetClassification
from autoPyTorch.data_management.data_manager import DataManager
# Note: You can write your own datamanager! Call fit with respective train, valid data (numpy matrices)
dm = DataManager()
dm.generate_classification(num_classes=3, num_features=21, num_samples=1500)
# Note: every parameter has a default value, you do not have to specify anything. The given parameter allow a fast test.
autonet = AutoNetClassification(budget_type='epochs',
min_budget=1,
max_budget=9,
num_iterations=1,
log_level='info')
res = autonet.fit(X_train=dm.X,
Y_train=dm.Y,
X_valid=dm.X_train,
Y_valid=dm.Y_train)
print(res)
print("Score:", autonet.score(X_test=dm.X_train, Y_test=dm.Y_train))
__author__ = "Max Dippel, Michael Burkart and Matthias Urban"
__version__ = "0.0.1"
__license__ = "BSD"
import os, sys
sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..", "..")))
from autoPyTorch import AutoNetClassification
from autoPyTorch.data_management.data_manager import DataManager
# Note: You can write your own datamanager! Call fit with respective train, valid data (numpy matrices)
dm = DataManager()
dm.generate_classification(num_classes=3, num_features=21, num_samples=1500)
# Note: every parameter has a default value, you do not have to specify anything. The given parameter allow a fast test.
autonet = AutoNetClassification("tiny_cs", budget_type='epochs', min_budget=1, max_budget=9, num_iterations=1, log_level='debug', use_pynisher=False)
res = autonet.fit(X_train=dm.X, Y_train=dm.Y, cross_validator="k_fold", cross_validator_args={"n_splits": 3})
print(res)
print("Score:", autonet.score(X_test=dm.X_train, Y_test=dm.Y_train))
is_classification=True)
metric = "pac_metric"
additional_metrices = []
""" TEST CASE 4: Openml, missing values """
if TEST_CASE == 4:
dm.read_data("openml:188", is_classification=True)
metric = "accuracy"
additional_metrices = []
""" TEST CASE 5: MNIST """
if TEST_CASE == 5:
dm.read_data("openml:40996", is_classification=True)
metric = "accuracy"
additional_metrices = []
# Generate autonet
autonet = AutoNetClassification() if TEST_CASE != 3 else AutoNetMultilabel()
# add metrics and test_result to pipeline
autonet.pipeline[
autonet_nodes.LogFunctionsSelector.get_name()].add_log_function(
'test_result', test_result(autonet, dm.X_test, dm.Y_test))
# Fit autonet using train data
res = autonet.fit(min_budget=300,
max_budget=900,
max_runtime=1800,
budget_type='time',
normalization_strategies=['maxabs'],
train_metric=metric,
additional_metrics=additional_metrices,
cross_validator='stratified_k_fold',
from autoPyTorch import AutoNetClassification
# Other imports for later usage
import openml
import json
autonet = AutoNetClassification(config_preset="tiny_cs",
result_logger_dir="logs/")
# Get the current configuration as dict
current_configuration = autonet.get_current_autonet_config()
hyperparameter_search_space = autonet.get_hyperparameter_search_space()
task = openml.tasks.get_task(task_id=31)
X, y = task.get_X_and_y()
ind_train, ind_test = task.get_train_test_split_indices()
X_train, Y_train = X[ind_train], y[ind_train]
X_test, Y_test = X[ind_test], y[ind_test]
autonet = AutoNetClassification(config_preset="tiny_cs",
result_logger_dir="logs/")
# Fit (note that the settings are for demonstration, you might need larger budgets)
results_fit = autonet.fit(X_train=X_train,
Y_train=Y_train,
validation_split=0.3,
max_runtime=300,
min_budget=60,
max_budget=100,
refit=True)
# Save fit results as json
with open("logs/results_fit.json", "w") as file:
json.dump(results_fit, file)
# See how the random configuration performs (often it just predicts 0)
score = autonet.score(X_test=X_test, Y_test=Y_test)
pred = autonet.predict(X=X_test)
# Test logging
autonet_config["additional_logs"] = [
test_predictions_for_ensemble.__name__, test_result_ens.__name__
]
# Initialize (ensemble)
if args.ensemble_setting == "ensemble":
print("Using ensembles!")
ensemble_config = get_ensemble_config()
autonet_config = {**autonet_config, **ensemble_config}
autonet = AutoNetEnsemble(AutoNetClassification,
config_preset="full_cs",
**autonet_config)
elif args.ensemble_setting == "normal":
autonet = AutoNetClassification(config_preset="full_cs",
**autonet_config)
# Test logging cont.
autonet.pipeline[LogFunctionsSelector.get_name()].add_log_function(
name=test_predictions_for_ensemble.__name__,
log_function=test_predictions_for_ensemble(autonet, X_test, y_test),
loss_transform=False)
autonet.pipeline[LogFunctionsSelector.get_name()].add_log_function(
name=test_result_ens.__name__,
log_function=test_result_ens(autonet, X_test, y_test))
autonet.pipeline[BaselineTrainer.get_name()].add_test_data(X_test)
print(autonet.get_current_autonet_config())
fit_results = autonet.fit(X_train, y_train,
# Tập dữ liệu hoa Iris của sklearn
# https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris
import sklearn.model_selection
import sklearn.datasets
X, y = sklearn.datasets.load_iris(return_X_y=True)
x_train, x_test, y_train, y_test = \
sklearn.model_selection.train_test_split(X, y, random_state=1)
# Sử dụng máy học tự động
from autoPyTorch import AutoNetClassification
# khởi tạo autopytorch
autoPyTorch = AutoNetClassification("tiny_cs",
log_level='info',
max_runtime=900,
min_budget=30,
max_budget=90,
cuda=True,
use_pynisher=False)
# Huấn luyện với tập dữ liệu
autoPyTorch.fit(x_train, y_train, validation_split=0.3)
# Kiểm tra độ chính xác với dữ liệu kiểm thử
y_pred = autoPyTorch.predict(x_test)
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_pred))
import openml
from pprint import pprint
from autoPyTorch import AutoNetClassification
from sklearn.metrics import accuracy_score
# get OpenML task by its ID
task = openml.tasks.get_task(task_id=32)
X, y = task.get_X_and_y()
ind_train, ind_test = task.get_train_test_split_indices()
# run Auto-PyTorch
autoPyTorch = AutoNetClassification(
"tiny_cs", # config preset
log_level='info',
max_runtime=300,
min_budget=30,
max_budget=90)
autoPyTorch.fit(X[ind_train], y[ind_train], validation_split=0.3)
# predict
y_pred = autoPyTorch.predict(X[ind_test])
print("Accuracy score", accuracy_score(y[ind_test], y_pred))
# print network configuration
pprint(autoPyTorch.fit_result["optimized_hyperparameter_config"])
is_classification=True)
# 4601 samples, 2 classes, 58 features
if TEST_CASE == 8:
dm.read_data(os.path.join(dataset_dir,
"classification/dataset_32_pendigits.csv"),
is_classification=True)
if TEST_CASE == 9:
dm.read_data(os.path.join(dataset_dir, "classification/php4fATLZ.csv"),
is_classification=True)
if TEST_CASE == 10:
dm.read_data(os.path.join(dataset_dir, "classification/phpnBqZGZ.csv"),
is_classification=True)
autonet = AutoNetClassification(budget_type='epochs',
min_budget=1,
max_budget=9,
num_iterations=1,
log_level='info')
res = autonet.fit(
X_train=dm.X_train,
Y_train=dm.Y_train,
early_stopping_patience=3,
# validation_split=0.3,
categorical_features=dm.categorical_features)
print(res)