Pytorch 预测：加载自定义数据集

Question

我正在尝试通过修改此 Github 存储库中给出的示例将自定义数据集加载到 PyTorch Forecasting。然而，我坚持实例化

TimeSeriesDataSet

。相关部分代码如下：

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
)

以及错误消息和回溯：

/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

我尝试调整初始化参数但没有成功。文件

timeseries.py

可以在同一个Github存储库中找到，这里。

Answer 1

据我所知，我猜这可能会发生，因为并非所有时间序列都有最小长度（

min_prediction_length

+

min_encoder_length

）。

就您而言，每个时间序列的长度至少应为 70。

Answer 2

我也遇到同样的问题，请问你解决了吗？