Я пытаюсь заставить работать временной термоядерный преобразователь PyTorch Forecasting, следуя примеру здесь:
https://pytorch-forecasting.readthedocs. io/en/stable/tutorials/stallion.html
Я продолжаю получать сообщение «KeyError: 0», хотя в моих данных вообще нет нулей . Впервые я получил эту ошибку с моим фактическим набором данных, поэтому я переключился на минимальный набор данных (ниже) и упрощенный код, но ошибка все еще возникает. Это происходит, когда я использую GroupNormalizer при определении TimeSeriesDataSet.
Я запускаю приведенный ниже код в ячейке блокнота PaperSpace Jupyter с контейнером Python 3.9.13:
!pip install numpy==1.24.4
!pip install pandas
!pip install --ignore-installed PyYAML==5.4.1
!pip install torch
!pip install pytorch-lightning
!pip install pytorch-forecasting
!pip install statsmodels
!pip install scipy
!pip install matplotlib
import pandas as pd
from pytorch_forecasting import TimeSeriesDataSet
from pytorch_forecasting.data.encoders import NaNLabelEncoder, GroupNormalizer
# Create a small sample dataframe
sample_data = pd.DataFrame({
'symbol': ['AAPL', 'AAPL', 'AAPL', 'GOOG', 'GOOG', 'GOOG'],
'period': [1, 2, 3, 1, 2, 3],
'close': [150.0, 152.0, 151.0, 2800.0, 2820.0, 2810.0],
'day_of_epoch': [19844, 19844, 19844, 19845, 19845, 19845]
})
# Ensure 'period' column is treated as integer index
sample_data['period'] = sample_data['period'].astype(int)
# Print the dataframe to verify
print(sample_data)
# Define the maximum prediction length
max_prediction_length = 1
max_encoder_length = 2
# Define the training dataset with minimal configuration
training = TimeSeriesDataSet(
sample_data,
time_idx="period",
target="close",
group_ids=["symbol", "day_of_epoch"],
min_encoder_length=1,
max_encoder_length=max_encoder_length,
min_prediction_length=1,
max_prediction_length=max_prediction_length,
static_categoricals=["symbol"],
time_varying_known_categoricals=[],
time_varying_known_reals=["period", "day_of_epoch"],
time_varying_unknown_categoricals=[],
time_varying_unknown_reals=["close"],
target_normalizer=GroupNormalizer(
groups=["symbol", "day_of_epoch"], transformation="softplus"
),
add_relative_time_idx=True,
add_target_scales=True,
add_encoder_length=True,
categorical_encoders={
'symbol': NaNLabelEncoder(add_nan=True)
},
)
Он выдает следующий результат (после завершения установки):
symbol period close day_of_epoch
0 AAPL 1 150.0 19844
1 AAPL 2 152.0 19844
2 AAPL 3 151.0 19844
3 GOOG 1 2800.0 19845
4 GOOG 2 2820.0 19845
5 GOOG 3 2810.0 19845
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File /usr/local/lib/python3.9/dist-packages/pytorch_forecasting/data/encoders.py:331, in NaNLabelEncoder.transform(self, y, return_norm, target_scale, ignore_na)
330 try:
--> 331 encoded = [self.classes_[v] for v in y]
332 except KeyError as e:
File /usr/local/lib/python3.9/dist-packages/pytorch_forecasting/data/encoders.py:331, in (.0)
330 try:
--> 331 encoded = [self.classes_[v] for v in y]
332 except KeyError as e:
KeyError: 0
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
Input In [1], in ()
31 max_encoder_length = 2
33 # Define the training dataset with minimal configuration
---> 34 training = TimeSeriesDataSet(
35 sample_data,
36 time_idx="period",
37 target="close",
38 group_ids=["symbol", "day_of_epoch"],
39 min_encoder_length=1,
40 max_encoder_length=max_encoder_length,
41 min_prediction_length=1,
42 max_prediction_length=max_prediction_length,
43 static_categoricals=["symbol"],
44 time_varying_known_categoricals=[],
45 time_varying_known_reals=["period", "day_of_epoch"],
46 time_varying_unknown_categoricals=[],
47 time_varying_unknown_reals=["close"],
48 target_normalizer=GroupNormalizer(
49 groups=["symbol", "day_of_epoch"], transformation="softplus"
50 ),
51 add_relative_time_idx=True,
52 add_target_scales=True,
53 add_encoder_length=True,
54 categorical_encoders={
55 'symbol': NaNLabelEncoder(add_nan=True)
56 },
57 )
File /usr/local/lib/python3.9/dist-packages/pytorch_forecasting/data/timeseries.py:476, in TimeSeriesDataSet.__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)
473 data = data.sort_values(self.group_ids + [self.time_idx])
475 # preprocess data
--> 476 data = self._preprocess_data(data)
477 for target in self.target_names:
478 assert target not in self.scalers, "Target normalizer is separate and not in scalers."
File /usr/local/lib/python3.9/dist-packages/pytorch_forecasting/data/timeseries.py:837, in TimeSeriesDataSet._preprocess_data(self, data)
831 transformer = self.get_transformer(name)
832 if (
833 name not in self.target_names
834 and transformer is not None
835 and not isinstance(transformer, EncoderNormalizer)
836 ):
--> 837 data[name] = self.transform_values(name, data[name], data=data, inverse=False)
839 # encode lagged categorical targets
840 for name in self.lagged_targets:
841 # normalizer only now available
File /usr/local/lib/python3.9/dist-packages/pytorch_forecasting/data/timeseries.py:935, in TimeSeriesDataSet.transform_values(self, name, values, data, inverse, group_id, **kwargs)
933 # remaining categories
934 if name in self.flat_categoricals + self.group_ids + self._group_ids:
--> 935 return transform(values, **kwargs)
937 # reals
938 elif name in self.reals:
File /usr/local/lib/python3.9/dist-packages/sklearn/utils/_set_output.py:313, in _wrap_method_output..wrapped(self, X, *args, **kwargs)
311 @wraps(f)
312 def wrapped(self, X, *args, **kwargs):
--> 313 data_to_wrap = f(self, X, *args, **kwargs)
314 if isinstance(data_to_wrap, tuple):
315 # only wrap the first output for cross decomposition
316 return_tuple = (
317 _wrap_data_with_container(method, data_to_wrap[0], X, self),
318 *data_to_wrap[1:],
319 )
File /usr/local/lib/python3.9/dist-packages/pytorch_forecasting/data/encoders.py:333, in NaNLabelEncoder.transform(self, y, return_norm, target_scale, ignore_na)
331 encoded = [self.classes_[v] for v in y]
332 except KeyError as e:
--> 333 raise KeyError(
334 f"Unknown category '{e.args[0]}' encountered. Set `add_nan=True` to allow unknown categories"
335 )
337 if isinstance(y, torch.Tensor):
338 encoded = torch.tensor(encoded, dtype=torch.long, device=y.device)
KeyError: "Unknown category '0' encountered. Set `add_nan=True` to allow unknown categories"
Подробнее здесь: https://stackoverflow.com/questions/786 ... ng-pytorch
