Greetings, everyone!
I’m having trouble with loading custom datasets into PyTorch Forecasting. I already posted the question to Stack Overflow 6 but it seems that I might find the answer here here’s the message pasted for your convenience:
I’m trying to load a custom dataset to PyTorch Forecasting by modifying the example given in this Github repository 6. However I’m stuck at instantiating the TimeSeriesDataSet. The relevant parts of the code are as follows:
import numpy as np
import pandas as pd

df = pd.read_csv("data.csv")
print(df.shape) # (300, 8)

# Divide the timestamps so that they are incremented by one each row.
df["unix"] = df["unix"].apply(lambda n: int(n / 86400))

# Set "unix" as the index
#df = df.set_index("unix")

# Add *integer* indices.
df["index"] = np.arange(300)
df = df.set_index("index")

# Add group column.
df["group"] = np.repeat(np.arange(30), 10)

from pytorch_forecasting import TimeSeriesDataSet

target = ["foo", "bar", "baz"]

# Create the dataset from the pandas dataframe
dataset = TimeSeriesDataSet(
    df,
    group_ids                  = ["group"],
    target                     = target,
    time_idx                   = "unix",
    min_encoder_length         = 50,
    max_encoder_length         = 50,
    min_prediction_length      = 20,
    max_prediction_length      = 20,
    time_varying_unknown_reals = target,
    allow_missing_timesteps    = True
)

And the error message plus traceback:
/home/user/.virtualenvs/torch/lib/python3.9/site-packages/pytorch_forecasting/data/timeseries.py:1241: UserWarning: Min encoder length and/or min_prediction_idx and/or min prediction length and/or lags are too large for 30 series/groups which therefore are not present in the dataset index. This means no predictions can be made for those series. First 10 removed groups: [{'__group_id__group': 0}, {'__group_id__group': 1}, {'__group_id__group': 2}, {'__group_id__group': 3}, {'__group_id__group': 4}, {'__group_id__group': 5}, {'__group_id__group': 6}, {'__group_id__group': 7}, {'__group_id__group': 8}, {'__group_id__group': 9}]
  warnings.warn(

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
/tmp/ipykernel_822/3402560775.py in <module>
      4 
      5 # create the dataset from the pandas dataframe
----> 6 dataset = TimeSeriesDataSet(
      7     df,
      8     group_ids                  = ["group"],

~/.virtualenvs/torch/lib/python3.9/site-packages/pytorch_forecasting/data/timeseries.py in __init__(self, data, time_idx, target, group_ids, weight, max_encoder_length, min_encoder_length, min_prediction_idx, min_prediction_length, max_prediction_length, static_categoricals, static_reals, time_varying_known_categoricals, time_varying_known_reals, time_varying_unknown_categoricals, time_varying_unknown_reals, variable_groups, constant_fill_strategy, allow_missing_timesteps, lags, add_relative_time_idx, add_target_scales, add_encoder_length, target_normalizer, categorical_encoders, scalers, randomize_length, predict_mode)
    437 
    438         # create index
--> 439         self.index = self._construct_index(data, predict_mode=predict_mode)
    440 
    441         # convert to torch tensor for high performance data loading later

~/.virtualenvs/torch/lib/python3.9/site-packages/pytorch_forecasting/data/timeseries.py in _construct_index(self, data, predict_mode)
   1247                 UserWarning,
   1248             )
-> 1249         assert (
   1250             len(df_index) > 0
   1251         ), "filters should not remove entries all entries - check encoder/decoder lengths and lags"

AssertionError: filters should not remove entries all entries - check encoder/decoder lengths and lags

I have tried tweaking the initializer arguments without success. The file timeseries.py can be found in the same Github repository, at pytorch_forecasting/data/timeseries.py, line 1246. Cannot give a third link in a single post, unfortunately.