TorchNormalizer.transform() missing 1 required positional argument: 'X'

[Alec-Schneider]

• PyTorch-Forecasting version: 0.10.2
• PyTorch version: 1.13.1
• Python version: 3.10.9
• Operating System: Windows 11 - Anaconda

Expected behavior

I am attempting to run the stallion example as I am using PyTorch-Forecasting for the first time and trying to build the training dataloader. Expecting to have the training and validation sets created.

Actual behavior

However, I am receiving an error message: "TypeError: TorchNormalizer.transform() missing 1 required positional argument: 'X'". When I check the documentation for the TorchNormalizer I see no 'X' parameter required for the transform method of the class.

Code to reproduce the problem

import os
import warnings

warnings.filterwarnings("ignore")  # avoid printing out absolute paths

os.chdir("../../..")

import copy
from pathlib import Path
import warnings

import numpy as np
import pandas as pd
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
import torch

from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.data import GroupNormalizer
from pytorch_forecasting.metrics import SMAPE, PoissonLoss, QuantileLoss
from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters

from pytorch_forecasting.data.examples import get_stallion_data

data = get_stallion_data()

# add time index
data["time_idx"] = data["date"].dt.year * 12 + data["date"].dt.month
data["time_idx"] -= data["time_idx"].min()

# add additional features
data["month"] = data.date.dt.month.astype(str).astype("category")  # categories have be strings
data["log_volume"] = np.log(data.volume + 1e-8)
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")

# we want to encode special days as one variable and thus need to first reverse one-hot encoding
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.sample(10, random_state=521)

max_prediction_length = 6
max_encoder_length = 24
training_cutoff = data["time_idx"].max() - max_prediction_length

training = TimeSeriesDataSet(
    data[lambda x: x.time_idx <= training_cutoff],
    time_idx="time_idx",
    target="volume",
    group_ids=["agency", "sku"],
    min_encoder_length=max_encoder_length // 2,  # keep encoder length long (as it is in the validation set)
    max_encoder_length=max_encoder_length,
    min_prediction_length=1,
    max_prediction_length=max_prediction_length,
    static_categoricals=["agency", "sku"],
    static_reals=["avg_population_2017", "avg_yearly_household_income_2017"],
    time_varying_known_categoricals=["special_days", "month"],
    variable_groups={"special_days": special_days},  # group of categorical variables can be treated as one variable
    time_varying_known_reals=["time_idx", "price_regular", "discount_in_percent"],
    time_varying_unknown_categoricals=[],
    time_varying_unknown_reals=[
        "volume",
        "log_volume",
        "industry_volume",
        "soda_volume",
        "avg_max_temp",
        "avg_volume_by_agency",
        "avg_volume_by_sku",
    ],
    target_normalizer=GroupNormalizer(
        groups=["agency", "sku"], transformation="softplus"
    ),  # use softplus and normalize by group
    add_relative_time_idx=True,
    add_target_scales=True,
    add_encoder_length=True,
)

# create validation set (predict=True) which means to predict the last max_prediction_length points in time
# for each series
validation = TimeSeriesDataSet.from_dataset(training, data, predict=True, stop_randomization=True)

# create dataloaders for model
batch_size = 128  # set this between 32 to 128
train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=0)
val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size * 10, num_workers=0)

[BohdanBilonoh]

Hello! Got the same error. I fixed it by upgrading to scikit-learn version 1.1.3

[Alec-Schneider]

Hi @BohdanBilonoh, unfortunately that did not work. Now I am receiving the below error message when trying to import pytorch_forecasting ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject

[AhmedAbdel-Aal]

The error is due to using older version of pytorch-forecasting. The issue is solved in the recent version in this line return super().transform(y, return_norm=return_norm, target_scale=target_scale) the commit details: https://github.com/jdb78/pytorch-forecasting/commit/a5f56ca8b0b636fb9517459b56c931934736d42e

try to upgrade or install via pip