初めに
過去に 比較的小さい時系列基盤モデルを触りました。こちらはfine tuingはできなかったので、fine tuingができてより大きいモデルを触っていきます
ayousanz.hatenadiary.jp
環境
- L4 GPU
- ubuntu22.04
- jupter notebook
ライブラリのインストール
!pip install git+https://github.com/amazon-science/chronos-forecasting.git
データの取得と分析
以下でデータを取得・分析をしていきます
import pandas as pd
df = pd.read_csv("ETTh1.csv")
print(len(df))
df.head(2)
以下のようにデータの中が表示されます
推論に用いれる形に変換をします
import torch
context_length = 512
forecast_horizon = 96
df_train = df.iloc[-(context_length+forecast_horizon):-forecast_horizon]
df_test = df.iloc[-forecast_horizon:]
train_tensor = torch.tensor(df_train[["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"]].values, dtype=torch.float)
train_tensor = train_tensor.t()
test_tensor = torch.tensor(df_test[["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"]].values, dtype=torch.float)
test_tensor = test_tensor.t()
推論
モデルのロード
以下でモデルをロードします
import pandas as pd
import torch
from chronos import ChronosPipeline
pipeline = ChronosPipeline.from_pretrained(
"amazon/chronos-t5-large",
device_map="cuda",
torch_dtype=torch.bfloat16,
)
推論実行及びグラフにプロット
forecast = pipeline.predict(train_tensor, forecast_horizon, limit_prediction_length=False)
forecast_median_tensor, _ = torch.median(forecast, dim=1)
import matplotlib.pyplot as plt
channel_idx = 6
time_index = 0
history = train_tensor[channel_idx, :].detach().numpy()
true = test_tensor[channel_idx, :].detach().numpy()
pred = forecast_median_tensor[channel_idx, :].detach().numpy()
plt.figure(figsize=(12, 4))
plt.plot(range(len(history)), history, label='History (512 timesteps)', c='darkblue')
num_forecasts = len(true)
offset = len(history)
plt.plot(range(offset, offset + len(true)), true, label='Ground Truth (96 timesteps)', color='darkblue', linestyle='--', alpha=0.5)
plt.plot(range(offset, offset + len(pred)), pred, label='Forecast (96 timesteps)', color='red', linestyle='--')
plt.title(f"ETTh1 (Hourly) -- (idx={time_index}, channel={channel_idx})", fontsize=18)
plt.xlabel('Time', fontsize=14)
plt.ylabel('Value', fontsize=14)
plt.legend(fontsize=14)
plt.show()
以下のようにゼロショットで推論ができました。かなり一致しています
fine tuing
ライブラリのインストール
!pip install "chronos[training] @ git+https://github.com/amazon-science/chronos-forecasting.git"
!git clone https://github.com/amazon-science/chronos-forecasting.git
! git clone https://github.com/IBM/tsfm.git
%cd tsfm
! pip install ".[notebooks]"
学習用のデータの変換
from pathlib import Path
from typing import List, Optional, Union
import numpy as np
from gluonts.dataset.arrow import ArrowWriter
from pathlib import Path
from typing import List, Optional, Union
import numpy as np
from gluonts.dataset.arrow import ArrowWriter
def convert_to_arrow(
path: Union[str, Path],
time_series: Union[List[np.ndarray], np.ndarray],
start_times: Optional[Union[List[np.datetime64], np.ndarray]] = None,
compression: str = "lz4",
):
if start_times is None:
start_times = [np.datetime64("2000-01-01 00:00", "s")] * len(time_series)
assert len(time_series) == len(start_times)
dataset = [
{"start": start, "target": ts} for ts, start in zip(time_series, start_times)
]
ArrowWriter(compression=compression).write_to_file(
dataset,
path=path,
)
cols = ["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"]
convert_to_arrow(
path = "./etth1-train.arrow",
time_series=[np.array(df_train[col]) for col in cols],
start_times=[pd.to_datetime(df_train["date"]).values[0]] * len(cols),
)
以下のようなファイルが生成されます
参考サイト
hamaruki.com
note.com
fine tuing
以下で追加学習をします
import yaml
batch_size = 2
num_steps = train_size/batch_size
print("steps:" + str(num_steps))
config_data = {
'training_data_paths': ["./etth1-train.arrow"],
'probability': [1.0],
'output_dir': './output/',
'context_length': context_length,
'prediction_length': forecast_horizon,
'max_steps': num_steps,
'per_device_train_batch_size': batch_size,
'learning_rate': 0.001,
'model_id': 'amazon/chronos-t5-large',
'random_init': False,
'tf32': True,
'gradient_accumulation_steps':2,
}
config_file_path = './ft_config.yaml'
with open(config_file_path, 'w') as file:
yaml.dump(config_data, file)
def fine_tune_chronos(train_file_path, config_file_path):
"""
chronos-t5モデルをFine Tuningする関数
Args:
train_file_path (str): 学習用データファイルのパス
config_file_path (str): Fine Tuning設定ファイルのパス
"""
!CUDA_VISIBLE_DEVICES=0 python chronos-forecasting/scripts/training/train.py --config {config_file_path}
fine_tune_chronos("./etth1-train.arrow", config_file_path)
ログは以下のようになりました
{'loss': 3.8517, 'grad_norm': 1.0407471656799316, 'learning_rate': 0.0009282433983926521, 'epoch': 0.07}
{'loss': 3.7261, 'grad_norm': 0.7981559038162231, 'learning_rate': 0.0008564867967853042, 'epoch': 0.14}
{'loss': 3.6565, 'grad_norm': 1.0834617614746094, 'learning_rate': 0.0007847301951779565, 'epoch': 0.22}
{'loss': 3.5609, 'grad_norm': 0.6380050182342529, 'learning_rate': 0.0007129735935706086, 'epoch': 0.29}
{'loss': 3.4786, 'grad_norm': 1.107723355293274, 'learning_rate': 0.0006412169919632607, 'epoch': 0.36}
{'loss': 3.3614, 'grad_norm': 0.8139169812202454, 'learning_rate': 0.0005694603903559128, 'epoch': 0.43}
{'loss': 3.2374, 'grad_norm': 0.9819308519363403, 'learning_rate': 0.0004977037887485649, 'epoch': 0.5}
{'loss': 3.1432, 'grad_norm': 1.3025404214859009, 'learning_rate': 0.000425947187141217, 'epoch': 0.57}
{'loss': 3.0098, 'grad_norm': 1.464908242225647, 'learning_rate': 0.0003541905855338691, 'epoch': 0.65}
{'loss': 2.8967, 'grad_norm': 1.4902337789535522, 'learning_rate': 0.00028243398392652127, 'epoch': 0.72}
{'loss': 2.7994, 'grad_norm': 1.2662851810455322, 'learning_rate': 0.00021067738231917335, 'epoch': 0.79}
{'loss': 2.7279, 'grad_norm': 1.741388201713562, 'learning_rate': 0.0001389207807118255, 'epoch': 0.86}
{'loss': 2.6553, 'grad_norm': 1.594014286994934, 'learning_rate': 6.716417910447761e-05, 'epoch': 0.93}
{'train_runtime': 7191.7126, 'train_samples_per_second': 3.876, 'train_steps_per_second': 0.969, 'train_loss': 3.195165220275949, 'epoch': 1.0}
100%|█████████████████████████████████████| 6968/6968 [1:59:51<00:00, 1.03s/it]
追加学習モデルを使った推論
from chronos import ChronosPipeline
import matplotlib.pyplot as plt
def predict_with_chronos(train_tensor, forecast_horizon, model_name="amazon/chronos-t5-large", device_map="cuda"):
"""
chronos-t5モデルで予測を行う関数
Args:
train_tensor (torch.Tensor): 学習用データテンソル
forecast_horizon (int): 予測する長さ
model_name (str): モデル名 (デフォルト: "amazon/chronos-t5-large")
device_map (str): デバイス ("cuda" or "cpu")
Returns:
forecast_median_tensor (torch.Tensor): 予測結果の中央値テンソル
"""
pipeline = ChronosPipeline.from_pretrained(
model_name,
device_map=device_map,
torch_dtype=torch.bfloat16,
)
forecast = pipeline.predict(train_tensor, forecast_horizon, limit_prediction_length=False)
forecast_median_tensor, _ = torch.median(forecast, dim=1)
return forecast_median_tensor, forecast
def visualize_prediction(train_tensor, test_tensor, forecast_median_tensor, channel_idx=6):
"""
予測結果を可視化する関数
Args:
train_tensor (torch.Tensor): 学習用データテンソル
test_tensor (torch.Tensor): テスト用データテンソル
forecast_median_tensor (torch.Tensor): 予測結果の中央値テンソル
channel_idx (int): 可視化するチャンネルのインデックス (デフォルト: 6, OT)
"""
history = train_tensor[channel_idx, :].detach().numpy()
true = test_tensor[channel_idx, :].detach().numpy()
pred = forecast_median_tensor[channel_idx, :].detach().numpy()
plt.figure(figsize=(12, 4))
plt.plot(range(len(history)), history, label='History (512 timesteps)', c='darkblue')
plt.plot(range(len(history), len(history) + len(true)), true, label='Ground Truth (96 timesteps)', color='darkblue', linestyle='--', alpha=0.5)
plt.plot(range(len(history), len(history) + len(pred)), pred, label='Forecast (96 timesteps)', color='red', linestyle='--')
plt.title(f"ETTh1 (Hourly) - Channel {channel_idx}", fontsize=18)
plt.xlabel('Time', fontsize=14)
plt.ylabel('Value', fontsize=14)
plt.legend(fontsize=14)
plt.show()
forecast_median_tensor_ft, forecast_ft = predict_with_chronos(train_tensor, forecast_horizon=forecast_horizon, model_name="./output/run-4/checkpoint-final/")
visualize_prediction(train_tensor, test_tensor, forecast_median_tensor_ft)
