def main():
set_seed(0)
run_id = str(int(time.time()))
print("Starting run={}, model={} ".format(run_id, MODEL_TYPE.BENCHMARK.value))
BASE_DIR = Path("data/raw/")
LOG_DIR = Path("logs/" + MODEL_TYPE.BENCHMARK.value)
FIGURE_PATH = Path("figures-temp/" + MODEL_TYPE.BENCHMARK.value)
print("Loading config")
config = get_config("Monthly")
print("Frequency:{}".format(config["variable"]))
print("loading data")
info = pd.read_csv(str(BASE_DIR / "M4info.csv"))
train_path = str(BASE_DIR / "train/%s-train.csv") % (config["variable"])
test_path = str(BASE_DIR / "test/%s-test.csv") % (config["variable"])
sample = config["sample"]
sample_ids = config["sample_ids"] if "sample_ids" in config else []
train, ts_labels, _, test, test_idx = create_datasets(train_path, test_path, config["output_size"],
create_val_dataset=False,
sample_ids=sample_ids, sample=sample,
sampling_size=4)
generate_timeseries_length_stats(train)
print("#.train:{}, #.test ts:{}".format(len(train), len(test)))
reload = config["reload"]
add_run_id = config["add_run_id"]
criterion = PinballLoss(config["training_tau"], config["output_size"] * config["batch_size"], config["device"])
trainer = Trainer(MODEL_TYPE.BENCHMARK.value, None, None, criterion, run_id, add_run_id, config,
csv_path=LOG_DIR, figure_path=FIGURE_PATH,
sampling=sample, reload=reload)
trainer.train_epochs()
def main():
set_seed(0)
run_id = str(int(time.time()))
print("Starting run={}, model={} ".format(run_id, MODEL_TYPE.NBEATS.value))
BASE_DIR = Path("data/raw/")
LOG_DIR = Path("logs/" + MODEL_TYPE.NBEATS.value)
FIGURE_PATH = Path("figures-temp/" + MODEL_TYPE.NBEATS.value)
print("Loading config")
config = get_config("Quarterly")
print("Frequency:{}".format(config["variable"]))
forecast_length = config["output_size"]
backcast_length = 1 * forecast_length
print("loading data")
info = pd.read_csv(str(BASE_DIR / "M4info.csv"))
train_path = str(BASE_DIR / "train/%s-train.csv") % (config["variable"])
test_path = str(BASE_DIR / "test/%s-test.csv") % (config["variable"])
sample = config["sample"]
sample_ids = config["sample_ids"] if "sample_ids" in config else []
train, ts_labels, val, test, test_idx = create_datasets(
train_path,
test_path,
config["output_size"],
sample_ids=sample_ids,
sample=sample,
sampling_size=4)
generate_timeseries_length_stats(train)
print("#.Train before chopping:{}".format(train.shape[0]))
train_before_chopping_count = train.shape[0]
chop_val = determine_chop_value(train, backcast_length, forecast_length)
print("Chop value:{:6.3f}".format(chop_val))
train, val, test, data_infocat_ohe, data_infocat_headers, data_info_cat = \
filter_timeseries(info, config["variable"], sample, ts_labels, train, chop_val, val, test)
print("#.Train after chopping:{}, lost:{:5.2f}%".format(
len(train), (train_before_chopping_count - len(train)) /
train_before_chopping_count * 100.))
print("#.train:{}, #.validation ts:{}, #.test ts:{}".format(
len(train), len(val), len(test)))
dataset = SeriesDataset(data_infocat_ohe, data_infocat_headers,
data_info_cat, ts_labels, train, val, test,
config["device"])
# dataloader = DataLoader(dataset, batch_size=config["batch_size"], collate_fn=collate_lines, shuffle=True)
dataloader = DataLoader(dataset,
batch_size=config["batch_size"],
shuffle=False)
model = NBeatsNet(stack_types=config["stack_types"],
forecast_length=forecast_length,
thetas_dims=config["thetas_dims"],
nb_blocks_per_stack=config["nb_blocks_per_stack"],
backcast_length=backcast_length,
hidden_layer_units=config["hidden_layer_units"],
share_weights_in_stack=config["share_weights_in_stack"],
dropout=config["dropout"],
device=config["device"])
reload = config["reload"]
add_run_id = config["add_run_id"]
optimizer = torch.optim.Adam(model.parameters(),
lr=config["learning_rate"])
criterion = PinballLoss(config["training_tau"],
config["output_size"] * config["batch_size"],
config["device"])
trainer = Trainer(MODEL_TYPE.NBEATS.value,
model,
optimizer,
criterion,
dataloader,
run_id,
add_run_id,
config,
forecast_length,
backcast_length,
ohe_headers=dataset.data_info_cat_headers,
csv_path=LOG_DIR,
figure_path=FIGURE_PATH,
sampling=sample,
reload=reload)
trainer.train_epochs()
def main():
set_seed(0)
run_id = str(int(time.time()))
print("Starting run={}, model={} ".format(run_id,
MODEL_TYPE.BENCHMARK.value))
BASE_DIR = Path("data/raw/")
LOG_DIR = Path("logs/" + MODEL_TYPE.BENCHMARK.value)
FIGURE_PATH = Path("figures-temp/" + MODEL_TYPE.BENCHMARK.value)
print("Loading config")
config = get_config("Monthly")
print("Frequency:{}".format(config["variable"]))
print("loading data")
info = pd.read_csv(str(BASE_DIR / "M4info.csv"))
train_path = str(BASE_DIR / "train/%s-train.csv") % (config["variable"])
test_path = str(BASE_DIR / "test/%s-test.csv") % (config["variable"])
sample = config["sample"]
sample_ids = config["sample_ids"] if "sample_ids" in config else []
train, ts_labels, val, test, test_idx = create_datasets(
train_path,
test_path,
config["output_size"],
create_val_dataset=True,
sample_ids=sample_ids,
sample=sample,
sampling_size=4)
generate_timeseries_length_stats(train)
train_before_chopping_count = train.shape[0]
print("#.Train before chopping:{}".format(train.shape[0]))
chop_val = config["chop_val"]
print("Chop value:{:6.3f}".format(chop_val))
train, val, test, data_infocat_ohe, data_infocat_headers, data_info_cat = \
filter_timeseries(info, config["variable"], sample, ts_labels, train, chop_val, val, test)
train, val, test, data_infocat_ohe, data_infocat_headers, data_info_cat = \
filter_timeseries(info, config["variable"], sample, ts_labels, train, chop_val, val, test)
print("#.Train after chopping:{}, lost:{:5.2f}%".format(
len(train), (train_before_chopping_count - len(train)) /
train_before_chopping_count * 100.))
print("#.train:{}, #.validation ts:{}, #.test ts:{}".format(
len(train), len(val), len(test)))
dataset = SeriesDataset(data_infocat_ohe, data_infocat_headers,
data_info_cat, ts_labels, train, val, test,
config["device"])
# dataloader = DataLoader(dataset, batch_size=config["batch_size"], collate_fn=collate_lines, shuffle=True)
dataloader = DataLoader(dataset,
batch_size=config["batch_size"],
shuffle=False)
add_run_id = config["add_run_id"]
trainer = Trainer(MODEL_TYPE.BENCHMARK.value,
dataloader,
run_id,
add_run_id,
config,
csv_path=LOG_DIR,
figure_path=FIGURE_PATH)
trainer.train()
import time
from pathlib import Path
import pandas as pd
import torch
from torch.nn import SmoothL1Loss
from torch.utils.data import DataLoader
from ts.es_rnn.config import get_config
from ts.es_rnn.model import ESRNN
from ts.es_rnn.trainer import ESRNNTrainer
from ts.utils.data_loading import SeriesDataset
from ts.utils.helper_funcs import MODEL_TYPE, set_seed, create_datasets, generate_timeseries_length_stats, \
filter_timeseries
set_seed(0)
run_id = str(int(time.time()))
print("Starting run={}, model={} ".format(run_id, MODEL_TYPE.ESRNN.value))
try:
user_paths = os.environ["PYTHONPATH"].split(os.pathsep)
print(user_paths)
except KeyError:
user_paths = []
BASE_DIR = Path("data/raw/")
LOG_DIR = Path("logs/" + MODEL_TYPE.ESRNN.value)
FIGURE_PATH = Path("figures-temp/" + MODEL_TYPE.ESRNN.value)
print("loading config")