PEMS04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "from st_nca.common import resume, get_device, checkpoint_all\n",
    "from st_nca.datasets.PEMS import PEMS03, PEMS04, PEMS08, get_config as pems_get_config\n",
    "from st_nca.cellmodel import CellModel, load_config, get_config, save_as, setup\n",
    "from st_nca.pretrain import training_loop\n",
    "from st_nca.gca import GraphCellularAutomata, timestamp_generator\n",
    "from st_nca.finetune import FineTunningDataset, finetune_loop\n",
    "from st_nca.evaluate import evaluate, diff_states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET = 4\n",
    "DEVICE = get_device()\n",
    "DTYPE = torch.float32\n",
    "#DEFAULT_PATH = 'C:\\\\Users\\\\petro\\\\Dropbox\\\\Projetos\\\\futurelab\\\\posdoc\\\\st_nca\\\\st_nca\\\\st_nca\\\\'\n",
    "DEFAULT_PATH = 'D:\\\\Dropbox\\\\Projetos\\\\futurelab\\\\posdoc\\\\st_nca\\\\st_nca\\\\st_nca\\\\'\n",
    "DATA_PATH = DEFAULT_PATH + 'data\\\\PEMS0{}\\\\'.format(DATASET)\n",
    "MODELS_PATH = DEFAULT_PATH + 'weights\\\\PEMS0{}\\\\'.format(DATASET)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Step Ahead (5 Minutes / 12 NGCA iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CM_FILE = MODELS_PATH + 'cm3x16x1024_3x1024_h1_smape20.pt'\n",
    "\n",
    "STEPS_AHEAD = 1\n",
    "\n",
    "\n",
    "pems = PEMS08(edges_file = DATA_PATH + 'edges.csv', data_file = DATA_PATH + 'data.csv',\n",
    "    device = DEVICE, dtype = DTYPE, steps_ahead = STEPS_AHEAD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\n",
    "    'num_tokens': pems.max_length,\n",
    "    'dim_token': pems.token_dim,\n",
    "    'num_transformers': 3,\n",
    "    'num_heads': 16,\n",
    "    'transformer_feed_forward': 1024,\n",
    "    'transformer_activation': nn.GELU(approximate='none'),\n",
    "    'normalization': torch.nn.modules.normalization.LayerNorm,\n",
    "    'pre_norm': False,\n",
    "    'feed_forward': 3,\n",
    "    'feed_forward_dim': 1024,\n",
    "    'feed_forward_activation': nn.GELU(approximate='none'),\n",
    "    'device': DEVICE,\n",
    "    'dtype': DTYPE,\n",
    "    'steps_ahead': STEPS_AHEAD\n",
    " }\n",
    "\n",
    "FLAUTIM_FILE = MODELS_PATH + 'OlwLedGbWh2XBP3yn_8iG_weights.h5'\n",
    "\n",
    "\n",
    "save_as(FLAUTIM_FILE, CM_FILE, pems, config = config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model, pems = setup(CM_FILE, pems, get_device())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup NGCA and Finetune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gca = GraphCellularAutomata(device=model.device, dtype=model.dtype, graph = pems.G,\n",
    "                            max_length = pems.max_length, token_size=pems.token_dim,\n",
    "                            tokenizer=pems.tokenizer, cell_model = model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "finetune_ds = FineTunningDataset(pems, increment_type='hours', increment=1, steps_ahead=1, step=250)\n",
    "\n",
    "finetune_loop(get_device(), finetune_ds, gca, \n",
    "              #iterations = 1, increment_type='minutes', increment=5,\n",
    "              iterations = 1, increment_type='hours', increment=1,\n",
    "              epochs = 150, batch = 1, lr = 0.00001,\n",
    "              checkpoint_file =  MODELS_PATH + 'gca3x16x1024_h12.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = evaluate(finetune_ds.test(), gca, 1, increment_type='hour', increment=1)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12 Steps Ahead (1 hour / 1 NGCA iteration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "CM_FILE = MODELS_PATH + 'cm3x16x1024_3x1024_h12_smape29.pt'\n",
    "\n",
    "STEPS_AHEAD = 12\n",
    "\n",
    "pems = PEMS04(edges_file = DATA_PATH + 'edges.csv', data_file = DATA_PATH + 'data.csv',\n",
    "    device = DEVICE, dtype = DTYPE, steps_ahead = STEPS_AHEAD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Cell Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\n",
    "    'num_tokens': pems.max_length,\n",
    "    'dim_token': pems.token_dim,\n",
    "    'num_transformers': 3,\n",
    "    'num_heads': 16,\n",
    "    'transformer_feed_forward': 1024,\n",
    "    'transformer_activation': nn.GELU(approximate='none'),\n",
    "    'normalization': torch.nn.modules.normalization.LayerNorm,\n",
    "    'pre_norm': False,\n",
    "    'feed_forward': 3,\n",
    "    'feed_forward_dim': 1024,\n",
    "    'feed_forward_activation': nn.GELU(approximate='none'),\n",
    "    'device': DEVICE,\n",
    "    'dtype': DTYPE,\n",
    "    'steps_ahead': STEPS_AHEAD\n",
    " }\n",
    "\n",
    "FLAUTIM_FILE = MODELS_PATH + 'yHngAjL-c3Iv-PpyG7iEk.h5'\n",
    "\n",
    "\n",
    "save_as(FLAUTIM_FILE, CM_FILE, pems, config = config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\petro\\Dropbox\\Projetos\\futurelab\\posdoc\\st_nca\\st_nca\\st_nca\\cellmodel.py:77: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  saved_config = torch.load(file)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'num_heads': 16, 'normalization': <class 'torch.nn.modules.normalization.LayerNorm'>, 'pre_norm': False, 'transformer_feed_forward': 1024, 'transformer_activation': GELU(approximate='none'), 'num_tokens': 10, 'dim_token': 7, 'num_transformers': 3, 'feed_forward': 3, 'feed_forward_dim': 1024, 'feed_forward_activation': GELU(approximate='none'), 'device': 'cuda', 'dtype': torch.float32, 'steps_ahead': 12, 'config': {'normalization': <class 'torch.nn.modules.normalization.LayerNorm'>, 'pre_norm': False, 'steps_ahead': 12}}\n"
     ]
    }
   ],
   "source": [
    "model, pems = setup(CM_FILE, pems, get_device())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup NGCA and Finetune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gca = GraphCellularAutomata(device=model.device, dtype=model.dtype, graph = pems.G,\n",
    "                            max_length = pems.max_length, token_size=pems.token_dim,\n",
    "                            tokenizer=pems.tokenizer, cell_model = model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR!: Initial date: 2016-07-07 02:25:00   Error date: 2016-07-07 03:25:00\n"
     ]
    }
   ],
   "source": [
    "finetune_ds = FineTunningDataset(pems, increment_type='hours', increment=1, steps_ahead=1, step=50)\n",
    "\n",
    "finetune_loop(get_device(), finetune_ds, gca, \n",
    "              #iterations = 1, increment_type='minutes', increment=5,\n",
    "              iterations = 1, increment_type='hours', increment=1,\n",
    "              epochs = 150, batch = 1, lr = 0.00001,\n",
    "              checkpoint_file =  MODELS_PATH + 'gca3x16x1024_h12_3.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'evaluate' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate\u001b[49m(finetune_ds\u001b[38;5;241m.\u001b[39mtest(), gca, \u001b[38;5;241m1\u001b[39m, increment_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhour\u001b[39m\u001b[38;5;124m'\u001b[39m, increment\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      2\u001b[0m df\u001b[38;5;241m.\u001b[39mdescribe()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'evaluate' is not defined"
     ]
    }
   ],
   "source": [
    "df = evaluate(finetune_ds.test(), gca, 1, increment_type='hour', increment=1)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}