def test_experiment_restoration(nested_dict_config, tmpdir):
experiments_dir = tmpdir.join("experiments").strpath
# create an experiment
experiment = Experiment(nested_dict_config,
experiments_dir=experiments_dir)
experiment.register_directory("temp")
with pytest.raises(ValueError):
# since the experiment with the same identifier has been
# already created, experiment raises an error
experiment = Experiment(nested_dict_config,
experiments_dir=experiments_dir)
# test restoration from identifier
experiment = Experiment(resume_from=experiment.config.identifier,
experiments_dir=experiments_dir)
assert experiment.config.to_dict() == nested_dict_config
# test that `temp` is registered after restoration
assert os.path.isdir(experiment.temp)
# test restoration from directory
experiment = Experiment(resume_from=os.path.join(
experiments_dir, experiment.config.identifier))
assert experiment.config.to_dict() == nested_dict_config
# test that `temp` is registered after restoration
assert os.path.isdir(experiment.temp)
def test_experiment_register_directory(nested_dict_config, tmpdir):
experiments_dir = tmpdir.join("experiments").strpath
experiment = Experiment(nested_dict_config,
experiments_dir=experiments_dir)
experiment.register_directory("temp")
target = os.path.join(experiment.experiment_dir, "temp")
assert os.path.isdir(target)
assert experiment.temp == target
def do_exp(model_name, params, _dir, preproc_name, padding):
"""Perform an experiment using the specified parameters.
Args:
params (dict): specific hyperparameter set to use.
Returns:
(dict): score found using specified hyperparameters.
"""
model = get_model(model_name)
preproc = get_filter(preproc_name)
X_train, y_train = get_data(dataset='train',
preprocessor=preproc,
TIMIT_root='../../TIMIT/TIMIT',
padding=padding)
X_test, y_test = get_data(dataset='val',
preprocessor=preproc,
TIMIT_root='../../TIMIT/TIMIT',
padding=padding)
try:
with Experiment(config=params, experiments_dir=_dir) as experiment:
score = run_model(model, X_train, y_train, X_test, y_test, params)
experiment.register_result('score', score)
except ValueError:
# if something breaks, return the worst score possible
return 0
return score
def test_experiment_commit_hash_saving(nested_dict_config, tmpdir):
experiments_dir = tmpdir.join("experiments").strpath
experiment = Experiment(nested_dict_config,
experiments_dir=experiments_dir)
assert os.path.isfile(
os.path.join(experiment.experiment_dir, "commit_hash"))
def test_experiment_initialization(nested_dict_config, tmpdir):
experiments_dir = tmpdir.join("experiments").strpath
experiment = Experiment(nested_dict_config,
experiments_dir=experiments_dir)
config = Config.from_json(
os.path.join(experiments_dir, experiment.config.identifier,
"config.json"))
assert config.to_dict() == nested_dict_config
def test_experiment_logging(nested_dict_config, tmpdir):
experiments_dir = tmpdir.join("experiments").strpath
with Experiment(nested_dict_config,
experiments_dir=experiments_dir) as experiment:
print("test")
with open(experiment.log_file, "r") as f:
assert f.readlines()[-1].strip() == "test"
print("test2")
# check that nothing is logged when print is called
# outside with block
with open(experiment.log_file, "r") as f:
assert f.readlines()[-1].strip() == "test"
def single_experiment(model, data, params):
"""Apply the model to the data and store the results using mag.
Args:
model (str): name of callable model constructor in the current namespace.
data (str): specify the TIMIT data sets to use. If specified, must be one of {'full', 'toy'}.
params (dict): dictionary with parameters for model.
"""
# prepare the experiment directory
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d-%H-%M-%S')
_dir = "./results/" + model + "_" + st
if data is not None and data.lower() == 'toy':
_dir = "./results/TOY_" + model + "_" + st + '/'
else:
_dir = "./results/" + model + "_" + st + '/'
# select the model to be used
model = get_model(model)
# get the specified dataset
if data is None:
data = "full"
if data.lower() == "toy":
X_train, y_train = get_data(dataset='toy',
preprocessor=mel,
TIMIT_root='../TIMIT/TIMIT',
use_cache=True)
X_test, y_test = X_train, y_train
elif data == "full":
X_train, y_train = get_data(dataset='train',
preprocessor=mel,
TIMIT_root='../TIMIT/TIMIT',
use_cache=True)
X_test, y_test = get_data(dataset='val',
preprocessor=mel,
TIMIT_root='../TIMIT/TIMIT',
use_cache=True)
else:
raise ValueError("data must be one of {'toy', 'full'}")
with Experiment(config=params, experiments_dir=_dir) as experiment:
score = run_model(model, X_train, y_train, X_test, y_test, params)
experiment.register_result('score', score)
def test_experiment_register_result(simple_dict_config, tmpdir):
experiments_dir = tmpdir.join("experiments").strpath
experiment = Experiment(simple_dict_config,
experiments_dir=experiments_dir)
experiment.register_result("fold1.accuracy", 0.97)
experiment.register_result("fold2.accuracy", 0.99)
experiment.register_result("fold1.loss", 0.03)
experiment.register_result("fold2.loss", 0.01)
experiment.register_result("overall_accuracy", 0.98)
results = experiment.results.to_dict()
assert results["fold1"]["accuracy"] == 0.97
assert results["fold2"]["accuracy"] == 0.99
assert results["fold1"]["loss"] == 0.03
assert results["fold2"]["loss"] == 0.01
assert results["overall_accuracy"] == 0.98
def do_exp(params, _dir, X_train, y_train, X_test, y_test, result_dict):
"""Perform an experiment using the specified parameters.
Args:
params (dict): specific hyperparameter set to use.
Returns:
(float): score found using specified hyperparameters.
"""
try:
with Experiment(config=params, experiments_dir=_dir) as experiment:
score = run_model(model, X_train, y_train, X_test, y_test, params)
# save the params and score
for k in params.keys():
result_dict[k].append(params[k])
experiment.register_result('score', score)
except ValueError:
# if something breaks, return the worst score possible
return np.inf
return -score # pylint: disable=invalid-unary-operand-type
"--num_workers",
type=int,
default=4,
help="number of workers for data loader",
)
parser.add_argument(
"--label",
type=str,
default="finetuned_hierarchical_cnn_classifier",
help="optional label",
)
args = parser.parse_args()
class_map = load_json(args.classmap)
pretrained = Experiment(resume_from=args.pretrained_model)
with Experiment({
"network": {
"num_conv_blocks": pretrained.config.network.num_conv_blocks,
"start_deep_supervision_on":
pretrained.config.network.start_deep_supervision_on,
"conv_base_depth": pretrained.config.network.conv_base_depth,
"growth_rate": pretrained.config.network.growth_rate,
"dropout": args.dropout,
"output_dropout": args.output_dropout,
},
"data": {
"_n_folds": args.n_folds,
"_kfold_seed": args.kfold_seed,
"n_fft": pretrained.config.data.n_fft,
"bert_model": args.bert_model.replace("-", "_"),
"batch_accumulation": args.batch_accumulation,
"batch_size": args.batch_size,
"warmup": args.warmup,
"lr": args.lr,
"folds": args.folds,
"max_sequence_length": args.max_sequence_length,
"max_title_length": args.max_title_length,
"max_question_length": args.max_question_length,
"max_answer_length": args.max_answer_length,
"head_tail": args.head_tail,
"label": args.label,
"_pseudo_file": args.pseudo_file,
"model_type": args.model_type,
}
experiment = Experiment(config, implicit_resuming=args.use_folds is not None)
experiment.register_directory("checkpoints")
experiment.register_directory("predictions")
def seed_everything(seed: int):
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
logging.getLogger("transformers").setLevel(logging.ERROR)
seed_everything(args.seed)
"_bert_model": args.bert_model,
"batch_accumulation": args.batch_accumulation,
"batch_size": args.batch_size,
"warmup": args.warmup,
"lr": args.lr,
"folds": args.folds,
"max_sequence_length": args.max_sequence_length,
"max_title_length": args.max_title_length,
"max_question_length": args.max_question_length,
"max_answer_length": args.max_answer_length,
"head_tail": args.head_tail,
"label": args.label,
"split_pseudo": args.split_pseudo,
"_pseudo_file": args.pseudo_file,
}
experiment = Experiment(config)
experiment.register_directory("checkpoints")
experiment.register_directory("predictions")
def seed_everything(seed: int):
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
logging.getLogger("transformers").setLevel(logging.ERROR)
seed_everything(args.seed)
with Experiment(
{
"network": {
"num_conv_blocks": args.num_conv_blocks,
"start_deep_supervision_on": args.start_deep_supervision_on,
"conv_base_depth": args.conv_base_depth,
"growth_rate": args.growth_rate,
"output_dropout": args.output_dropout,
"aggregation_type": args.aggregation_type
},
"data": {
"features": args.features,
"_n_folds": args.n_folds,
"_kfold_seed": args.kfold_seed,
"_input_dim": audio_transform.n_features,
"p_mixup": args.p_mixup,
"p_aug": args.p_aug,
"max_audio_length": args.max_audio_length,
"_train_df": args.train_df,
"_train_data_dir": args.train_data_dir
},
"train": {
"accumulation_steps": args.accumulation_steps,
"batch_size": args.batch_size,
"learning_rate": args.lr,
"scheduler": args.scheduler,
"optimizer": args.optimizer,
"epochs": args.epochs,
"_save_every": args.save_every,
"weight_decay": args.weight_decay,
"switch_off_augmentations_on": args.switch_off_augmentations_on
},
"label": args.label
},
implicit_resuming=args.resume) as experiment:
help="How frequenlty to plot samples from current distribution."
)
parser.add_argument(
"--plot_points", type=int, default=1000,
help="How many to points to generate for one plot."
)
args = parser.parse_args()
torch.manual_seed(42)
with Experiment({
"batch_size": 40,
"iterations": 10000,
"initial_lr": 0.01,
"lr_decay": 0.999,
"flow_length": 16,
"name": "planar"
}) as experiment:
config = experiment.config
experiment.register_directory("samples")
experiment.register_directory("distributions")
flow = NormalizingFlow(dim=2, flow_length=config.flow_length)
bound = FreeEnergyBound(density=p_z)
optimizer = optim.RMSprop(flow.parameters(), lr=config.initial_lr)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, config.lr_decay)
plot_density(p_z, directory=experiment.distributions)
help="whether to train on cuda or cpu",
choices=("cuda", "cpu"))
parser.add_argument(
"--num_workers",
type=int,
default=4,
help="number of workers for data loader",
)
args = parser.parse_args()
class_map = load_json(args.classmap)
train_df = pd.read_csv(args.train_df)
with Experiment(resume_from=args.experiment) as experiment:
config = experiment.config
audio_transform = AudioFeatures(config.data.features)
splits = list(
train_validation_data_stratified(train_df.fname, train_df.labels,
class_map, config.data._n_folds,
config.data._kfold_seed))
all_labels = np.zeros(shape=(len(train_df), len(class_map)),
dtype=np.float32)
all_predictions = np.zeros(shape=(len(train_df), len(class_map)),
dtype=np.float32)
audio_transform = AudioFeatures(args.features)
with Experiment({
"network": {
"prediction_steps": args.prediction_steps,
"rnn_size": args.rnn_size,
"rnn_layers": args.rnn_layers
},
"data": {
"features": args.features,
"_n_folds": args.n_folds,
"_kfold_seed": args.kfold_seed,
"_input_dim": audio_transform.n_features,
"p_aug": args.p_aug,
"max_audio_length": args.max_audio_length
},
"train": {
"_proj_interval": args.proj_interval,
"accumulation_steps": args.accumulation_steps,
"batch_size": args.batch_size,
"learning_rate": args.lr,
"scheduler": args.scheduler,
"optimizer": args.optimizer,
"epochs": args.epochs,
"_save_every": args.save_every,
"weight_decay": args.weight_decay,
"switch_off_augmentations_on": args.switch_off_augmentations_on
},
"label": args.label
}) as experiment:
config = experiment.config
from torch.utils.data import DataLoader
from transformers import BertTokenizer, RobertaTokenizer
mag.use_custom_separator("-")
parser = argparse.ArgumentParser()
parser.add_argument("--experiment", type=str, required=True)
parser.add_argument("--checkpoint", type=str, required=True)
parser.add_argument("--bert_model", type=str, required=True)
parser.add_argument("--dataframe", type=str, required=True)
parser.add_argument("--output_dir", type=str, required=True)
args = parser.parse_args()
experiment = Experiment(resume_from=args.experiment)
config = experiment.config
logging.getLogger("transformers").setLevel(logging.ERROR)
test_df = pd.read_csv(args.dataframe)
original_args = argparse.Namespace(
folds=config.folds,
lr=config.lr,
batch_size=config.batch_size,
seed=config._seed,
bert_model=args.bert_model,
num_classes=30,
target_columns=target_columns,
input_columns=input_columns,
args = parser.parse_args()
svm_config = {
"model": {
"C": args.C,
"gamma": args.gamma
},
"crossval": {
"n_folds": args.cv,
"_random_seed": args.cv_random_seed
}
}
iris = load_iris()
with Experiment(config=svm_config) as experiment:
config = experiment.config
model = SVC(C=config.model.C, gamma=config.model.gamma)
score = cross_val_score(
model, X=iris.data, y=iris.target, scoring="accuracy",
cv=StratifiedKFold(
config.crossval.n_folds,
shuffle=True,
random_state=config.crossval._random_seed),
).mean()
print("Accuracy is", round(score, 4))
experiment.register_result("accuracy", score)
default=0.9,
help="Momentum value used in optimizer.")
args = parser.parse_args()
with Experiment({
"_n_classes": 10,
"network": {
"n_layers": args.n_layers,
"hidden_units": args.hidden_units,
"activation": args.activation
},
"train":
{
"batch_size": args.batch_size,
"n_epochs": args.n_epochs,
# to exclude the parameter from the identifier,
# start its name from the underscore
"_buffer_size": 128,
"learning_rate": args.lr,
"momentum": args.momentum
},
"validation": {
"_batch_size": 128
}
}) as experiment:
classifier = MnistClassifier(mnist, experiment)
classifier.fit()
print("Finished!") # will be logged to a file
with Experiment({
"data": {
"_input_dim": 64,
"_kfold_seed": 42,
"_n_folds": 5,
"_train_data_dir": "data/Training_Data/",
"_train_df": "data/train_df.csv",
"features": "mel_1024_512_64",
"max_audio_length": 3,
"p_aug": 0.3,
"p_mixup": 0.0
},
"label": "2d_cnn",
"network": {
"aggregation_type": "max",
"conv_base_depth": 32,
"growth_rate": 1.3,
"num_conv_blocks": 5,
"output_dropout": 0.5,
"start_deep_supervision_on": 2
},
"train": {
"_save_every": 5,
"accumulation_steps": 1,
"batch_size": 50,
"epochs": 7,
"learning_rate": 0.001,
"optimizer": "adam",
"scheduler": "1cycle_0.0001_0.005",
"switch_off_augmentations_on": 6,
"weight_decay": 0.0
}
}) as experiment:
with Experiment(
{
"network": {
"backbone": args.backbone,
"output_dropout": args.output_dropout,
},
"data": {
"features": args.features,
"_n_folds": args.n_folds,
"_kfold_seed": args.kfold_seed,
"_input_dim": audio_transform.n_features,
"_n_classes": len(class_map),
"_holdout_size": args.holdout_size,
"p_mixup": args.p_mixup,
"p_aug": args.p_aug,
"max_audio_length": args.max_audio_length,
"noisy": args.noisy_train_df is not None,
"_train_df": args.train_df,
"_train_data_dir": args.train_data_dir,
"_noisy_train_df": args.noisy_train_df,
"_noisy_train_data_dir": args.noisy_train_data_dir,
"_share_noisy": args.share_noisy
},
"train": {
"accumulation_steps": args.accumulation_steps,
"batch_size": args.batch_size,
"learning_rate": args.lr,
"scheduler": args.scheduler,
"optimizer": args.optimizer,
"epochs": args.epochs,
"_save_every": args.save_every,
"weight_decay": args.weight_decay,
"switch_off_augmentations_on": args.switch_off_augmentations_on
},
"label": args.label
},
implicit_resuming=args.resume) as experiment:
