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# FeatureExtractorでの実施事項
# - 時系列データをdataframe, numpy array, torch tensorの状態からtorch tensor化
# - input validation
from typing import List, Optional, Union
from pandas import DataFrame
import numpy as np
import torch
import tensorflow as tf
import jax.numpy as jnp
from transformers import FeatureExtractionMixin
from transformers import TensorType
from transformers import BatchFeature
from transformers.utils import logging
logger = logging.get_logger(__name__)
class MomentFeatureExtractor(FeatureExtractionMixin):
# TODO: 本来はMoment側のTokenizerもts_tokenizerとして入れたかったが、モデルに組み込まれてしまっている。
# refers: https://github.com/moment-timeseries-foundation-model/moment/blob/088b253a1138ac7e48a7efc9bf902336c9eec8d9/momentfm/models/moment.py#L105
model_input_names = ["time_series_values", "input_mask"]
def __init__(self, **kwargs):
super().__init__(**kwargs)
def __call__(
self,
time_series: Union[DataFrame, np.ndarray, torch.Tensor, List[DataFrame], List[np.ndarray], List[torch.Tensor]] = None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
torch_dtype: Optional[Union[str, torch.dtype]] = torch.float,
) -> BatchFeature:
if time_series is not None:
time_series_values = self._convert_time_series(time_series, return_tensors, torch_dtype)
else:
time_series_values = None
return BatchFeature(data={"time_series_values": time_series_values})
def _convert_time_series(self, time_series, return_tensors, torch_dtype):
# DataFrame, np.ndarray, または torch.Tensor を torch.Tensor に変換
if isinstance(time_series, list):
# リスト内の各要素を torch.Tensor に変換し、最終的には1つのTensorに結合
time_series_tensor = torch.stack([self._convert_to_tensor(ts, torch_dtype) for ts in time_series])
else:
time_series_tensor = self._convert_to_tensor(time_series, torch_dtype)
# 次元数の確認
if time_series_tensor.dim() > 3:
raise ValueError("time_series_tensor must not have more than 3 dimensions")
elif time_series_tensor.dim() == 2:
time_series_tensor = time_series_tensor.unsqueeze(0)
elif time_series_tensor.dim() == 1:
time_series_tensor = time_series_tensor.unsqueeze(0).unsqueeze(0)
# 形式の出力
batch_size, n_channels, d_model = time_series_tensor.shape
logger.info(f"Batch size: {batch_size}, Number of channels: {n_channels}, Dimension of model: {d_model}")
# seq_lenを最大値512までに絞り込み
if time_series_tensor.shape[2] > 512:
time_series_tensor = time_series_tensor[:, :, :512]
logger.info("Sequence length has been truncated to 512.")
# return_tensorsの指定に応じてデータ形式を変換
if return_tensors == 'pt' or return_tensors == TensorType.PYTORCH:
return time_series_tensor
elif return_tensors == 'np' or return_tensors == TensorType.NUMPY:
return time_series_tensor.numpy()
elif return_tensors == 'tf' or return_tensors == TensorType.TENSORFLOW:
return tf.convert_to_tensor(time_series_tensor.numpy())
elif return_tensors == 'jax' or return_tensors == TensorType.JAX:
return jnp.array(time_series_tensor.numpy())
else:
raise ValueError("Unsupported return_tensors type")
def _convert_to_tensor(self, time_series, torch_dtype):
if isinstance(time_series, DataFrame):
time_series_tensor = torch.tensor(time_series.values, dtype=torch_dtype).t()
elif isinstance(time_series, np.ndarray):
time_series_tensor = torch.tensor(time_series, dtype=torch_dtype)
elif isinstance(time_series, torch.Tensor):
time_series_tensor = time_series.to(torch_dtype)
return time_series_tensor
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