def data_with_covariates():
data = get_stallion_data()
data["month"] = data.date.dt.month.astype(str)
data["log_volume"] = np.log1p(data.volume)
data["weight"] = 1 + np.sqrt(data.volume)
data["time_idx"] = data["date"].dt.year * 12 + data["date"].dt.month
data["time_idx"] -= data["time_idx"].min()
# convert special days into strings
special_days = [
"easter_day",
"good_friday",
"new_year",
"christmas",
"labor_day",
"independence_day",
"revolution_day_memorial",
"regional_games",
"fifa_u_17_world_cup",
"football_gold_cup",
"beer_capital",
"music_fest",
]
data[special_days] = data[special_days].apply(lambda x: x.map({
0: "",
1: x.name
})).astype("category")
return data
def data_with_covariates():
data = get_stallion_data()
data["month"] = data.date.dt.month.astype(str)
data["log_volume"] = np.log1p(data.volume)
data["weight"] = 1 + np.sqrt(data.volume)
data["time_idx"] = data["date"].dt.year * 12 + data["date"].dt.month
data["time_idx"] -= data["time_idx"].min()
# convert special days into strings
special_days = [
"easter_day",
"good_friday",
"new_year",
"christmas",
"labor_day",
"independence_day",
"revolution_day_memorial",
"regional_games",
"fifa_u_17_world_cup",
"football_gold_cup",
"beer_capital",
"music_fest",
]
data[special_days] = data[special_days].apply(lambda x: x.map({
0: "",
1: x.name
})).astype("category")
data = data.astype(dict(industry_volume=float))
# select data subset
data = data[lambda x: x.sku.isin(data.sku.unique()[:2])][
lambda x: x.agency.isin(data.agency.unique()[:2])]
# default target
data["target"] = data["volume"].clip(1e-3, 1.0)
return data
from pandas.core.common import SettingWithCopyWarning
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
import torch
from pytorch_forecasting import GroupNormalizer, TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.data.examples import get_stallion_data
from pytorch_forecasting.metrics import MAE, RMSE, SMAPE, PoissonLoss, QuantileLoss
from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
from pytorch_forecasting.utils import profile
warnings.simplefilter("error", category=SettingWithCopyWarning)
data = get_stallion_data()
data["month"] = data.date.dt.month.astype("str").astype("category")
data["log_volume"] = np.log(data.volume + 1e-8)
data["time_idx"] = data["date"].dt.year * 12 + data["date"].dt.month
data["time_idx"] -= data["time_idx"].min()
data["avg_volume_by_sku"] = data.groupby(["time_idx", "sku"], observed=True).volume.transform("mean")
data["avg_volume_by_agency"] = data.groupby(["time_idx", "agency"], observed=True).volume.transform("mean")
# data = data[lambda x: (x.sku == data.iloc[0]["sku"]) & (x.agency == data.iloc[0]["agency"])]
special_days = [
"easter_day",
"good_friday",
"new_year",
"christmas",
"labor_day",
