run_timesfm.py

# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Evaluation script for timesfm."""

import os
import sys
import time

from absl import flags
import numpy as np
import pandas as pd
from paxml import checkpoints
sys.path.append(os.getcwd())
from src import timesfm #TODO:路径
from experiments.extended_benchmarks.utils import ExperimentHandler
from jax.lib import xla_bridge

# sugon测试
dataset_names = [  # context_len=512
    "ett_small_15min",
    "traffic",
    "m3_quarterly",
    "m3_yearly",
    "tourism_yearly",
]

# dataset_names = [
#     "m1_monthly",
#     "m1_quarterly",
#     "m1_yearly",
#     "m3_monthly",
#     "m3_other",
#     "m3_quarterly",
#     "m3_yearly",
#     "m4_quarterly",
#     "m4_yearly",
#     "tourism_monthly",
#     "tourism_quarterly",
#     "tourism_yearly",
#     "nn5_daily_without_missing",
#     "m5",
#     "nn5_weekly",
#     "traffic",
#     "weather",
#     "australian_electricity_demand",
#     "car_parts_without_missing",
#     "cif_2016",
#     "covid_deaths",
#     "ercot",
#     "ett_small_15min",
#     "ett_small_1h",
#     "exchange_rate",
#     "fred_md",
#     "hospital",
# ]

context_dict = {
    "cif_2016": 32,
    "tourism_yearly": 64,
    "covid_deaths": 64,
    "tourism_quarterly": 64,
    "tourism_monthly": 64,
    "m1_monthly": 64,
    "m1_quarterly": 64,
    "m1_yearly": 64,
    "m3_monthly": 64,
    "m3_other": 64,
    "m3_quarterly": 64,
    "m3_yearly": 64,
    "m4_quarterly": 64,
    "m4_yearly": 64,
}

# TODO：模型位置
_MODEL_PATH = flags.DEFINE_string(   
    "model_path", "model/checkpoints", "Path to model"
)

 #TODO： 参数调整
_BATCH_SIZE = flags.DEFINE_integer("batch_size", 64, "Batch size")
_HORIZON = flags.DEFINE_integer("horizon", 128, "Horizon")
_BACKEND = flags.DEFINE_string("backend", "gpu", "Backend")
_NUM_JOBS = flags.DEFINE_integer("num_jobs", 1, "Number of jobs")  
_SAVE_DIR = flags.DEFINE_string("save_dir", "./results", "Save directory")


QUANTILES = list(np.arange(1, 10) / 10.0)


def main():
  results_list = []
  tfm = timesfm.TimesFm( 
      context_len=512,
      horizon_len=_HORIZON.value,
      input_patch_len=32,
      output_patch_len=128,
      num_layers=20,
      model_dims=1280,
      backend=_BACKEND.value,
      per_core_batch_size=_BATCH_SIZE.value,
      quantiles=QUANTILES,
  )
  tfm.load_from_checkpoint(   # 检查点,加载模型
      _MODEL_PATH.value,
      checkpoint_type=checkpoints.CheckpointType.FLAX,
  )
  run_id = np.random.randint(100000)
  model_name = "timesfm"
  for dataset in dataset_names:
    print(f"Evaluating model {model_name} on dataset {dataset}", flush=True)
    exp = ExperimentHandler(dataset, quantiles=QUANTILES)

    if dataset in context_dict:
      context_len = context_dict[dataset]
    else:
      context_len = 512
    train_df = exp.train_df
    freq = exp.freq
    init_time = time.time()
    fcsts_df = tfm.forecast_on_df(
        inputs=train_df,
        freq=freq,
        value_name="y",
        model_name=model_name,
        forecast_context_len=context_len,
        num_jobs=_NUM_JOBS.value,
    )
    total_time = time.time() - init_time
    time_df = pd.DataFrame({"time": [total_time], "model": model_name})
    results = exp.evaluate_from_predictions(
        models=[model_name], fcsts_df=fcsts_df, times_df=time_df
    )
    print(results, flush=True)
    results_list.append(results)
    results_full = pd.concat(results_list)
    save_path = os.path.join(_SAVE_DIR.value, str(run_id))
    print(f"Saving results to {save_path}", flush=True)
    os.makedirs(save_path, exist_ok=True)
    results_full.to_csv(f"{save_path}/results.csv")


if __name__ == "__main__":
  # # debug1-测试torch-gpu\jax-gpu\TensorFlow-gpu
  jax_test=xla_bridge.get_backend().platform
  print(jax_test)
  if not (jax_test=='gpu'):
      exit()
    
  FLAGS = flags.FLAGS
  FLAGS(sys.argv)
  main()