def train(file_path, P, frac):
target, df = create_dataset(file_path)
i = 0
rolling_test = []
train_size = int(frac * df.shape[0])
starts = [pd.Timestamp(df.index[0]) for _ in range(len(target))]
delay = 0
grouper_train = MultivariateGrouper(max_target_dim=df.shape[0])
grouper_test = MultivariateGrouper(max_target_dim=df.shape[0])
train_ds = ListDataset([{
FieldName.TARGET: targets,
FieldName.START: start
} for (targets, start) in zip(target[:, 0:train_size - P], starts)],
freq='1B')
train_ds = grouper_train(train_ds)
while train_size + delay < df.shape[0]:
delay = int(P) * i
test_ds = ListDataset([{
FieldName.TARGET: targets,
FieldName.START: start
} for (targets, start) in zip(target[:, 0:train_size + delay], starts)
],
freq='1B')
test_ds = grouper_test(test_ds)
rolling_test.append(test_ds)
i += 1
estimator = GPVAREstimator(prediction_length=pred_len,
context_length=6,
freq='1B',
target_dim=df.shape[1],
trainer=Trainer(ctx="cpu", epochs=200))
return train_ds, rolling_test, estimator, train_size
def load_multivariate_constant_dataset():
metadata, train_ds, test_ds = constant_dataset()
grouper_train = MultivariateGrouper(max_target_dim=NUM_SERIES)
grouper_test = MultivariateGrouper(max_target_dim=NUM_SERIES)
return TrainDatasets(
metadata=metadata,
train=grouper_train(train_ds),
test=grouper_test(test_ds),
)
def load_multivariate_datasets(path: Path) -> TrainDatasets:
ds = load_datasets(path / "metadata", path / "train", path / "test")
target_dim = ds.metadata.feat_static_cat[0].cardinality
grouper_train = MultivariateGrouper(max_target_dim=target_dim)
grouper_test = MultivariateGrouper(max_target_dim=target_dim)
return TrainDatasets(
metadata=ds.metadata,
train=grouper_train(ds.train),
test=grouper_test(ds.test),
)
def load_dataset(dataset_name: str, path: Path) -> TrainDatasets:
dataset = get_dataset(dataset_name, path, regenerate=False)
target_dim = dataset.metadata.feat_static_cat[0].cardinality
grouper_train = MultivariateGrouper(max_target_dim=target_dim)
grouper_test = MultivariateGrouper(max_target_dim=target_dim)
return TrainDatasets(
metadata=dataset.metadata,
train=grouper_train(dataset.train),
test=grouper_test(dataset.test),
)
def load_multivariate_constant_dataset():
dataset_info, train_ds, test_ds = constant_dataset()
grouper_train = MultivariateGrouper(max_target_dim=10)
grouper_test = MultivariateGrouper(num_test_dates=1, max_target_dim=10)
metadata = dataset_info.metadata
metadata.prediction_length = dataset_info.prediction_length
return TrainDatasets(
metadata=dataset_info.metadata,
train=grouper_train(train_ds),
test=grouper_test(test_ds),
)
def test_multivariate_grouper_train(univariate_ts, multivariate_ts,
train_fill_rule) -> None:
univariate_ds = ListDataset(univariate_ts, freq="1D")
multivariate_ds = ListDataset(multivariate_ts,
freq="1D",
one_dim_target=False)
grouper = MultivariateGrouper(train_fill_rule=train_fill_rule)
assert (list(grouper(univariate_ds))[0]["target"] == list(multivariate_ds)
[0]["target"]).all()
assert (list(grouper(univariate_ds))[0]["start"] == list(multivariate_ds)
[0]["start"])
def test_multivariate_grouper_test(univariate_ts, multivariate_ts,
test_fill_rule, max_target_dim) -> None:
univariate_ds = ListDataset(univariate_ts, freq="1D")
multivariate_ds = ListDataset(multivariate_ts,
freq="1D",
one_dim_target=False)
grouper = MultivariateGrouper(
test_fill_rule=test_fill_rule,
num_test_dates=2,
max_target_dim=max_target_dim,
)
for grouped_data, multivariate_data in zip(grouper(univariate_ds),
multivariate_ds):
assert (grouped_data["target"] == multivariate_data["target"]).all()
assert grouped_data["start"] == multivariate_data["start"]
def train(args):
freq = args.freq.replace('"', '')
prediction_length = args.prediction_length
context_length = args.context_length
use_feat_dynamic_real = args.use_feat_dynamic_real
use_past_feat_dynamic_real = args.use_past_feat_dynamic_real
use_feat_static_cat = args.use_feat_static_cat
use_log1p = args.use_log1p
print('freq:', freq)
print('prediction_length:', prediction_length)
print('context_length:', context_length)
print('use_feat_dynamic_real:', use_feat_dynamic_real)
print('use_past_feat_dynamic_real:', use_past_feat_dynamic_real)
print('use_feat_static_cat:', use_feat_static_cat)
print('use_log1p:', use_log1p)
batch_size = args.batch_size
print('batch_size:', batch_size)
train = load_json(os.path.join(args.train, 'train_'+freq+'.json'))
test = load_json(os.path.join(args.test, 'test_'+freq+'.json'))
num_timeseries = len(train)
print('num_timeseries:', num_timeseries)
train_ds = ListDataset(parse_data(train, use_log1p=use_log1p), freq=freq)
test_ds = ListDataset(parse_data(test, use_log1p=use_log1p), freq=freq)
predictor = None
trainer= Trainer(ctx="cpu",
epochs=args.epochs,
num_batches_per_epoch=args.num_batches_per_epoch,
learning_rate=args.learning_rate,
learning_rate_decay_factor=args.learning_rate_decay_factor,
patience=args.patience,
minimum_learning_rate=args.minimum_learning_rate,
clip_gradient=args.clip_gradient,
weight_decay=args.weight_decay,
init=args.init.replace('"', ''),
hybridize=args.hybridize)
print('trainer:', trainer)
cardinality = None
if args.cardinality != '':
cardinality = args.cardinality.replace('"', '').replace(' ', '').replace('[', '').replace(']', '').split(',')
for i in range(len(cardinality)):
cardinality[i] = int(cardinality[i])
print('cardinality:', cardinality)
embedding_dimension = [min(50, (cat+1)//2) for cat in cardinality] if cardinality is not None else None
print('embedding_dimension:', embedding_dimension)
algo_name = args.algo_name.replace('"', '')
print('algo_name:', algo_name)
if algo_name == 'CanonicalRNN':
estimator = CanonicalRNNEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
num_layers=5,
num_cells=50,
cell_type='lstm',
num_parallel_samples=100,
cardinality=cardinality,
embedding_dimension=10,
)
elif algo_name == 'DeepFactor':
estimator = DeepFactorEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
cardinality=cardinality,
embedding_dimension=10,
)
elif algo_name == 'DeepAR':
estimator = DeepAREstimator(
freq = freq, # – Frequency of the data to train on and predict
prediction_length = prediction_length, # – Length of the prediction horizon
trainer = trainer, # – Trainer object to be used (default: Trainer())
context_length = context_length, # – Number of steps to unroll the RNN for before computing predictions (default: None, in which case context_length = prediction_length)
num_layers = 2, # – Number of RNN layers (default: 2)
num_cells = 40, # – Number of RNN cells for each layer (default: 40)
cell_type = 'lstm', # – Type of recurrent cells to use (available: ‘lstm’ or ‘gru’; default: ‘lstm’)
dropoutcell_type = 'ZoneoutCell', # – Type of dropout cells to use (available: ‘ZoneoutCell’, ‘RNNZoneoutCell’, ‘VariationalDropoutCell’ or ‘VariationalZoneoutCell’; default: ‘ZoneoutCell’)
dropout_rate = 0.1, # – Dropout regularization parameter (default: 0.1)
use_feat_dynamic_real = use_feat_dynamic_real, # – Whether to use the feat_dynamic_real field from the data (default: False)
use_feat_static_cat = use_feat_static_cat, # – Whether to use the feat_static_cat field from the data (default: False)
use_feat_static_real = False, # – Whether to use the feat_static_real field from the data (default: False)
cardinality = cardinality, # – Number of values of each categorical feature. This must be set if use_feat_static_cat == True (default: None)
embedding_dimension = embedding_dimension, # – Dimension of the embeddings for categorical features (default: [min(50, (cat+1)//2) for cat in cardinality])
# distr_output = StudentTOutput(), # – Distribution to use to evaluate observations and sample predictions (default: StudentTOutput())
# scaling = True, # – Whether to automatically scale the target values (default: true)
# lags_seq = None, # – Indices of the lagged target values to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq)
# time_features = None, # – Time features to use as inputs of the RNN (default: None, in which case these are automatically determined based on freq)
# num_parallel_samples = 100, # – Number of evaluation samples per time series to increase parallelism during inference. This is a model optimization that does not affect the accuracy (default: 100)
# imputation_method = None, # – One of the methods from ImputationStrategy
# train_sampler = None, # – Controls the sampling of windows during training.
# validation_sampler = None, # – Controls the sampling of windows during validation.
# alpha = None, # – The scaling coefficient of the activation regularization
# beta = None, # – The scaling coefficient of the temporal activation regularization
batch_size = batch_size, # – The size of the batches to be used training and prediction.
# minimum_scale = None, # – The minimum scale that is returned by the MeanScaler
# default_scale = None, # – Default scale that is applied if the context length window is completely unobserved. If not set, the scale in this case will be the mean scale in the batch.
# impute_missing_values = None, # – Whether to impute the missing values during training by using the current model parameters. Recommended if the dataset contains many missing values. However, this is a lot slower than the default mode.
# num_imputation_samples = None, # – How many samples to use to impute values when impute_missing_values=True
)
elif algo_name == 'DeepState':
estimator = DeepStateEstimator(
freq=freq,
prediction_length=prediction_length,
trainer=trainer,
batch_size=batch_size,
use_feat_dynamic_real=use_feat_dynamic_real,
use_feat_static_cat=use_feat_static_cat,
cardinality=cardinality,
)
elif algo_name == 'DeepVAR':
estimator = DeepVAREstimator( # use multi
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
target_dim=96,
)
elif algo_name == 'GaussianProcess':
# # TODO
# estimator = GaussianProcessEstimator(
# freq=freq,
# prediction_length=prediction_length,
# context_length=context_length,
# trainer=trainer,
# batch_size=batch_size,
# cardinality=num_timeseries,
# )
pass
elif algo_name == 'GPVAR':
estimator = GPVAREstimator( # use multi
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
target_dim=96,
)
elif algo_name == 'LSTNet':
estimator = LSTNetEstimator( # use multi
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
num_series=96,
skip_size=4,
ar_window=4,
channels=72,
trainer=trainer,
batch_size=batch_size,
)
elif algo_name == 'NBEATS':
estimator = NBEATSEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
)
elif algo_name == 'DeepRenewalProcess':
estimator = DeepRenewalProcessEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
num_cells=40,
num_layers=2,
)
elif algo_name == 'Tree':
estimator = TreePredictor(
freq = freq,
prediction_length = prediction_length,
context_length = context_length,
n_ignore_last = 0,
lead_time = 0,
max_n_datapts = 1000000,
min_bin_size = 100, # Used only for "QRX" method.
use_feat_static_real = False,
use_feat_dynamic_cat = False,
use_feat_dynamic_real = use_feat_dynamic_real,
cardinality = cardinality,
one_hot_encode = False,
model_params = {'eta': 0.1, 'max_depth': 6, 'silent': 0, 'nthread': -1, 'n_jobs': -1, 'gamma': 1, 'subsample': 0.9, 'min_child_weight': 1, 'colsample_bytree': 0.9, 'lambda': 1, 'booster': 'gbtree'},
max_workers = 4, # default: None
method = "QRX", # "QRX", "QuantileRegression", "QRF"
quantiles=None, # Used only for "QuantileRegression" method.
model=None,
seed=None,
)
elif algo_name == 'SelfAttention':
# # TODO
# estimator = SelfAttentionEstimator(
# freq=freq,
# prediction_length=prediction_length,
# context_length=context_length,
# trainer=trainer,
# batch_size=batch_size,
# )
pass
elif algo_name == 'MQCNN':
estimator = MQCNNEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
use_past_feat_dynamic_real=use_past_feat_dynamic_real,
use_feat_dynamic_real=use_feat_dynamic_real,
use_feat_static_cat=use_feat_static_cat,
cardinality=cardinality,
embedding_dimension=embedding_dimension,
add_time_feature=True,
add_age_feature=False,
enable_encoder_dynamic_feature=True,
enable_decoder_dynamic_feature=True,
seed=None,
decoder_mlp_dim_seq=None,
channels_seq=None,
dilation_seq=None,
kernel_size_seq=None,
use_residual=True,
quantiles=None,
distr_output=None,
scaling=None,
scaling_decoder_dynamic_feature=False,
num_forking=None,
max_ts_len=None,
)
elif algo_name == 'MQRNN':
estimator = MQRNNEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
batch_size=batch_size,
)
elif algo_name == 'Seq2Seq':
# # TODO
# estimator = Seq2SeqEstimator(
# freq=freq,
# prediction_length=prediction_length,
# context_length=context_length,
# trainer=trainer,
# cardinality=cardinality,
# embedding_dimension=4,
# encoder=Seq2SeqEncoder(),
# decoder_mlp_layer=[4],
# decoder_mlp_static_dim=4
# )
pass
elif algo_name == 'SimpleFeedForward':
estimator = SimpleFeedForwardEstimator(
num_hidden_dimensions=[40, 40],
prediction_length=prediction_length,
context_length=context_length,
freq=freq,
trainer=trainer,
batch_size=batch_size,
)
elif algo_name == 'TemporalFusionTransformer':
estimator = TemporalFusionTransformerEstimator(
prediction_length=prediction_length,
context_length=context_length,
freq=freq,
trainer=trainer,
batch_size=batch_size,
hidden_dim = 32,
variable_dim = None,
num_heads = 4,
num_outputs = 3,
num_instance_per_series = 100,
dropout_rate = 0.1,
# time_features = [],
# static_cardinalities = {},
# dynamic_cardinalities = {},
# static_feature_dims = {},
# dynamic_feature_dims = {},
# past_dynamic_features = []
)
elif algo_name == 'DeepTPP':
# # TODO
# estimator = DeepTPPEstimator(
# prediction_interval_length=prediction_length,
# context_interval_length=context_length,
# freq=freq,
# trainer=trainer,
# batch_size=batch_size,
# num_marks=len(cardinality) if cardinality is not None else 0,
# )
pass
elif algo_name == 'Transformer':
estimator = TransformerEstimator(
freq=freq,
prediction_length=prediction_length,
trainer=trainer,
batch_size=batch_size,
cardinality=cardinality,
)
elif algo_name == 'WaveNet':
estimator = WaveNetEstimator(
freq=freq,
prediction_length=prediction_length,
trainer=trainer,
batch_size=batch_size,
cardinality=cardinality,
)
elif algo_name == 'Naive2':
# TODO Multiplicative seasonality is not appropriate for zero and negative values
predictor = Naive2Predictor(freq=freq, prediction_length=prediction_length, season_length=context_length)
elif algo_name == 'NPTS':
predictor = NPTSPredictor(freq=freq, prediction_length=prediction_length, context_length=context_length)
elif algo_name == 'Prophet':
def configure_model(model):
model.add_seasonality(
name='weekly', period=7, fourier_order=3, prior_scale=0.1
)
return model
predictor = ProphetPredictor(freq=freq, prediction_length=prediction_length, init_model=configure_model)
elif algo_name == 'ARIMA':
predictor = RForecastPredictor(freq=freq,
prediction_length=prediction_length,
method_name='arima',
period=context_length,
trunc_length=len(train[0]['target']))
elif algo_name == 'ETS':
predictor = RForecastPredictor(freq=freq,
prediction_length=prediction_length,
method_name='ets',
period=context_length,
trunc_length=len(train[0]['target']))
elif algo_name == 'TBATS':
predictor = RForecastPredictor(freq=freq,
prediction_length=prediction_length,
method_name='tbats',
period=context_length,
trunc_length=len(train[0]['target']))
elif algo_name == 'CROSTON':
predictor = RForecastPredictor(freq=freq,
prediction_length=prediction_length,
method_name='croston',
period=context_length,
trunc_length=len(train[0]['target']))
elif algo_name == 'MLP':
predictor = RForecastPredictor(freq=freq,
prediction_length=prediction_length,
method_name='mlp',
period=context_length,
trunc_length=len(train[0]['target']))
elif algo_name == 'SeasonalNaive':
predictor = SeasonalNaivePredictor(freq=freq, prediction_length=prediction_length)
else:
print('[ERROR]:', algo_name, 'not supported')
return
if predictor is None:
try:
predictor = estimator.train(train_ds, test_ds)
except Exception as e:
print(e)
try:
grouper_train = MultivariateGrouper(max_target_dim=num_timeseries)
train_ds_multi = grouper_train(train_ds)
test_ds_multi = grouper_train(test_ds)
predictor = estimator.train(train_ds_multi, test_ds_multi)
except Exception as e:
print(e)
forecast_it, ts_it = make_evaluation_predictions(
dataset=test_ds, # test dataset
predictor=predictor, # predictor
num_samples=100, # number of sample paths we want for evaluation
)
forecasts = list(forecast_it)
tss = list(ts_it)
# print(len(forecasts), len(tss))
evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])
agg_metrics, item_metrics = evaluator(iter(tss), iter(forecasts), num_series=len(test_ds))
print(json.dumps(agg_metrics, indent=4))
model_dir = os.path.join(args.model_dir, algo_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
predictor.serialize(Path(model_dir))
def train(bucket, seq, algo, freq, prediction_length, epochs, learning_rate,
hybridize, num_batches_per_epoch):
#create train dataset
df = pd.read_csv(filepath_or_buffer=os.environ['SM_CHANNEL_TRAIN'] +
"/train.csv",
header=0,
index_col=0)
training_data = ListDataset([{
"start": df.index[0],
"target": df.usage[:],
"item_id": df.client[:]
}],
freq=freq)
#create test dataset
df = pd.read_csv(filepath_or_buffer=os.environ['SM_CHANNEL_TEST'] +
"/test.csv",
header=0,
index_col=0)
test_data = ListDataset([{
"start": df.index[0],
"target": df.usage[:],
"item_id": 'client_12'
}],
freq=freq)
hook = Hook.create_from_json_file()
#determine estimators##################################
if algo == "DeepAR":
estimator = DeepAREstimator(
freq=freq,
prediction_length=prediction_length,
context_length=1,
trainer=Trainer(ctx="cpu",
epochs=epochs,
learning_rate=learning_rate,
hybridize=hybridize,
num_batches_per_epoch=num_batches_per_epoch))
#train the model
predictor = estimator.train(training_data=training_data)
print("DeepAR training is complete SUCCESS")
elif algo == "SFeedFwd":
estimator = SimpleFeedForwardEstimator(
freq=freq,
prediction_length=prediction_length,
trainer=Trainer(ctx="cpu",
epochs=epochs,
learning_rate=learning_rate,
hybridize=hybridize,
num_batches_per_epoch=num_batches_per_epoch))
#train the model
predictor = estimator.train(training_data=training_data)
print("training is complete SUCCESS")
elif algo == "lstnet":
# Needed for LSTNet ONLY
grouper = MultivariateGrouper(max_target_dim=6)
training_data = grouper(training_data)
test_data = grouper(test_data)
context_length = prediction_length
num_series = 1
skip_size = 1
ar_window = 1
channels = 4
estimator = LSTNetEstimator(
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
num_series=num_series,
skip_size=skip_size,
ar_window=ar_window,
channels=channels,
trainer=Trainer(ctx="cpu",
epochs=epochs,
learning_rate=learning_rate,
hybridize=hybridize,
num_batches_per_epoch=num_batches_per_epoch))
#train the model
predictor = estimator.train(training_data=training_data)
print("training is complete SUCCESS")
elif algo == "seq2seq":
estimator = MQCNNEstimator(
freq=freq,
prediction_length=prediction_length,
trainer=Trainer(ctx="cpu",
epochs=epochs,
learning_rate=learning_rate,
hybridize=hybridize,
num_batches_per_epoch=num_batches_per_epoch))
#train the model
predictor = estimator.train(training_data=training_data)
print("training is complete SUCCESS")
else:
estimator = TransformerEstimator(
freq=freq,
prediction_length=prediction_length,
trainer=Trainer(ctx="cpu",
epochs=epochs,
learning_rate=learning_rate,
hybridize=hybridize,
num_batches_per_epoch=num_batches_per_epoch))
#train the model
predictor = estimator.train(training_data=training_data)
print("training is complete SUCCESS")
###################################################
#evaluate trained model on test data
forecast_it, ts_it = make_evaluation_predictions(test_data,
predictor,
num_samples=100)
print("EVALUATION is complete SUCCESS")
forecasts = list(forecast_it)
tss = list(ts_it)
evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])
agg_metrics, item_metrics = evaluator(iter(tss),
iter(forecasts),
num_series=len(test_data))
print("METRICS retrieved SUCCESS")
#bucket = "bwp-sandbox"
mainpref = "gluonts/blog-models/"
prefix = mainpref + str(seq) + "/"
agg_df = pd.DataFrame(agg_metrics, index=[0])
file = "metrics" + str(seq) + ".csv"
os.system('mkdir metrics')
cspath = os.path.join('metrics', file)
agg_df.to_csv(cspath)
s3.upload_file(cspath, bucket, mainpref + "metrics/" + file)
hook.save_scalar("MAPE", agg_metrics["MAPE"], sm_metric=True)
hook.save_scalar("RMSE", agg_metrics["RMSE"], sm_metric=True)
hook.save_scalar("MASE", agg_metrics["MASE"], sm_metric=True)
hook.save_scalar("MSE", agg_metrics["MSE"], sm_metric=True)
print("MAPE:", agg_metrics["MAPE"])
#save the model
predictor.serialize(pathlib.Path(os.environ['SM_MODEL_DIR']))
uploadDirectory(os.environ['SM_MODEL_DIR'], prefix, bucket)
return predictor