264 lines
9.3 KiB
Plaintext
264 lines
9.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pickle\n",
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"import pycuda.driver as cuda\n",
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"import sys\n",
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"import os\n",
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"from datetime import datetime\n",
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"from stable_baselines3 import PPO\n",
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"\n",
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"current_dir = os.path.dirname(os.path.abspath(\"__file__\"))\n",
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"parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))\n",
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"output_dir = os.path.join(parent_dir, \"output\")\n",
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"os.makedirs(output_dir, exist_ok=True)\n",
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"sys.path.append(parent_dir)\n",
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"\n",
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"cuda.init()\n",
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"context2 = cuda.Device(2).make_context()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(os.path.join(output_dir, \"d1a3o12_re100.pkl\"), 'rb') as f:\n",
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" data = pickle.load(f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from gym_env import CustomEnv\n",
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"context2.push()\n",
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"vec_env = CustomEnv(device_id=2)\n",
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"context2.pop()\n",
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"model = PPO.load(os.path.join(parent_dir, \"models\", \"d1a3o12_re100.zip\"), env=vec_env, device=\"cuda:2\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"context2.push()\n",
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"model_env = model.get_env()\n",
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"# obs = model_env.reset()\n",
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"obs_init = np.array(model_env.envs[0].save_states.copy())\n",
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"context2.pop()\n",
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"\n",
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"obs_init[:, 6:12] = obs_init[:, 6:12]/model_env.envs[0].force_norm_fact.copy()\n",
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"sens_deviation = model_env.envs[0].sens_deviation.copy()\n",
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"sens_norm_fact = model_env.envs[0].sens_norm_fact.copy()\n",
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"for i in range(6):\n",
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" obs_init[:, i] = (obs_init[:, i] - sens_deviation[i]) / sens_norm_fact[i]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from darts import TimeSeries\n",
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"from darts.models import RNNModel # 或者 NBEATSModel、TFTModel 等\n",
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"from darts.models import TFTModel\n",
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"from darts.dataprocessing.transformers import Scaler\n",
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"from typing import List"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"set_obs_train = []\n",
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"set_act_train = []\n",
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"index = pd.RangeIndex(start=0, stop=480, step=1)\n",
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"for i in range(len(data)):\n",
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" obs_episolde = np.array(data[i]['observations'])[:, 0]\n",
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" if np.any(np.all(obs_episolde == 0, axis=1)):\n",
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" # print(f\"Episode {i} contains a row with all zeros in observations.\")\n",
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" continue\n",
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" else:\n",
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" obs_full = np.concatenate((obs_init, obs_episolde), axis=0)\n",
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" act_init = np.zeros((len(obs_init), 3))\n",
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" act_episolde = np.array(data[i]['actions'])\n",
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" act_full = np.concatenate((act_init, act_episolde), axis=0)\n",
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"\n",
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" obs_ts = TimeSeries.from_times_and_values(\n",
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" times=index,\n",
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" values=obs_full.astype(np.float32), \n",
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" columns=[f\"obs{dim+1}\" for dim in range(12)]\n",
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" )\n",
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" act_ts = TimeSeries.from_times_and_values(\n",
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" times=index,\n",
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" values=act_full.astype(np.float32), \n",
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" columns=[f\"act{dim+1}\" for dim in range(3)]\n",
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" )\n",
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"\n",
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" set_obs_train.append(obs_ts)\n",
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" set_act_train.append(act_ts)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Trainer will use only 1 of 4 GPUs because it is running inside an interactive / notebook environment. You may try to set `Trainer(devices=4)` but please note that multi-GPU inside interactive / notebook environments is considered experimental and unstable. Your mileage may vary.\n",
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"GPU available: True (cuda), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n",
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"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n",
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" | Name | Type | Params | Mode \n",
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"------------------------------------------------------------------------------------------------\n",
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"0 | train_metrics | MetricCollection | 0 | train\n",
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"1 | val_metrics | MetricCollection | 0 | train\n",
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"2 | input_embeddings | _MultiEmbedding | 0 | train\n",
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"3 | static_covariates_vsn | _VariableSelectionNetwork | 0 | train\n",
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"4 | encoder_vsn | _VariableSelectionNetwork | 17.8 K | train\n",
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"5 | decoder_vsn | _VariableSelectionNetwork | 896 | train\n",
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"6 | static_context_grn | _GatedResidualNetwork | 4.3 K | train\n",
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"7 | static_context_hidden_encoder_grn | _GatedResidualNetwork | 4.3 K | train\n",
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"8 | static_context_cell_encoder_grn | _GatedResidualNetwork | 4.3 K | train\n",
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"9 | static_context_enrichment | _GatedResidualNetwork | 4.3 K | train\n",
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"10 | lstm_encoder | LSTM | 8.4 K | train\n",
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"11 | lstm_decoder | LSTM | 8.4 K | train\n",
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"12 | post_lstm_gan | _GateAddNorm | 2.2 K | train\n",
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"13 | static_enrichment_grn | _GatedResidualNetwork | 5.3 K | train\n",
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"14 | multihead_attn | _InterpretableMultiHeadAttention | 2.6 K | train\n",
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"15 | post_attn_gan | _GateAddNorm | 2.2 K | train\n",
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"16 | feed_forward_block | _GatedResidualNetwork | 4.3 K | train\n",
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"17 | pre_output_gan | _GateAddNorm | 2.2 K | train\n",
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"18 | output_layer | Linear | 6.7 K | train\n",
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"------------------------------------------------------------------------------------------------\n",
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"78.2 K Trainable params\n",
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"0 Non-trainable params\n",
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"78.2 K Total params\n",
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"0.313 Total estimated model params size (MB)\n",
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"382 Modules in train mode\n",
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"0 Modules in eval mode\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 49: 100%|██████████| 12/12 [00:00<00:00, 21.99it/s, train_loss=0.133]"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_epochs=50` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 49: 100%|██████████| 12/12 [00:00<00:00, 21.93it/s, train_loss=0.133]\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"TFTModel(output_chunk_shift=0, hidden_size=32, lstm_layers=1, num_attention_heads=4, full_attention=False, feed_forward=GatedResidualNetwork, dropout=0.1, hidden_continuous_size=8, categorical_embedding_sizes=None, add_relative_index=True, loss_fn=None, likelihood=None, norm_type=LayerNorm, use_static_covariates=True, input_chunk_length=120, output_chunk_length=1, batch_size=32, n_epochs=50, random_state=42)"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model_tft = TFTModel(\n",
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" input_chunk_length=120,\n",
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" output_chunk_length=1, # 预测1步\n",
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" hidden_size=32,\n",
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" lstm_layers=1,\n",
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" dropout=0.1,\n",
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" batch_size=32,\n",
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" n_epochs=50,\n",
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" add_relative_index=True,\n",
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" random_state=42,\n",
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" # 注意：TFT支持past_covariates与future_covariates，也可设置transformer头等更多超参\n",
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")\n",
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"\n",
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"model_tft.fit(\n",
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" series=obs_ts,\n",
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" past_covariates=act_ts\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in range(360):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_data = obs_full[:, 11]\n",
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"import matplotlib.pyplot as plt\n",
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"plt.plot(plot_data)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "pycuda_3_10",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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