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
# 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
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_pred))
print("Confusion matrix",
sklearn.metrics.confusion_matrix(y_test, y_pred, labels=le.classes_))
print(autoPyTorch)
pytorch_model = autoPyTorch.get_pytorch_model()
print(pytorch_model)
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)
print("Model prediction:", pred[0:10])
print("Accuracy score", score)
pytorch_model = autonet.get_pytorch_model()
print(pytorch_model)
# Load fit results as json
with open("logs/results_fit.json") as file:
results_fit = json.load(file)
# Create an autonet
autonet_config = {
"result_logger_dir": "logs/",
"budget_type": "epochs",
"log_level": "info",
"use_tensorboard_logger": True,
"validation_split": 0.0
}
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"])