wehub-resource-sync
204 lines
5.4 KiB
Plaintext
204 lines
5.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Base Model"
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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 timesfm import TimesFm, freq_map, data_loader\n",
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"from adapter.utils import load_adapter_checkpoint\n",
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"from tqdm import tqdm\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"\n",
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"tfm = TimesFm(\n",
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" context_len=512,\n",
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" horizon_len=128,\n",
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" input_patch_len=32,\n",
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" output_patch_len=128,\n",
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" num_layers=20,\n",
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" model_dims=1280,\n",
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" backend=\"cpu\",\n",
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")\n",
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"tfm.load_from_checkpoint(repo_id=\"google/timesfm-1.0-200m\")"
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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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"DATA_DICT = {\n",
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" \"ettm2\": {\n",
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" \"boundaries\": [34560, 46080, 57600],\n",
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" \"data_path\": \"../datasets/ETT-small/ETTm2.csv\",\n",
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" \"freq\": \"15min\",\n",
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" },\n",
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" \"ettm1\": {\n",
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" \"boundaries\": [34560, 46080, 57600],\n",
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" \"data_path\": \"../datasets/ETT-small/ETTm1.csv\",\n",
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" \"freq\": \"15min\",\n",
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" },\n",
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" \"etth2\": {\n",
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" \"boundaries\": [8640, 11520, 14400],\n",
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" \"data_path\": \"../datasets/ETT-small/ETTh2.csv\",\n",
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" \"freq\": \"H\",\n",
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" },\n",
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" \"etth1\": {\n",
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" \"boundaries\": [8640, 11520, 14400],\n",
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" \"data_path\": \"../datasets/ETT-small/ETTh1.csv\",\n",
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" \"freq\": \"H\",\n",
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" },\n",
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" \"elec\": {\n",
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" \"boundaries\": [18413, 21044, 26304],\n",
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" \"data_path\": \"../datasets/electricity/electricity.csv\",\n",
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" \"freq\": \"H\",\n",
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" },\n",
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" \"traffic\": {\n",
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" \"boundaries\": [12280, 14036, 17544],\n",
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" \"data_path\": \"../datasets/traffic/traffic.csv\",\n",
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" \"freq\": \"H\",\n",
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" },\n",
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" \"weather\": {\n",
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" \"boundaries\": [36887, 42157, 52696],\n",
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" \"data_path\": \"../datasets/weather/weather.csv\",\n",
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" \"freq\": \"10min\",\n",
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" },\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Adapter Checkpoint\n",
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"\n",
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"Specify the adapter checkpoint path, rank and the modules used to train the adapters and whether dora was employed or not."
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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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"load_adapter_checkpoint(\n",
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" model=tfm,\n",
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" adapter_checkpoint_path=\"./checkpoints/run_20240716_163900_lyo4psz3\",\n",
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" lora_rank=1,\n",
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" lora_target_modules=\"all\",\n",
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" use_dora=True,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Test Performance"
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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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"dataset = \"ettm1\"\n",
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"data_path = DATA_DICT[dataset][\"data_path\"]\n",
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"freq = DATA_DICT[dataset][\"freq\"]\n",
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"int_freq = freq_map(freq)\n",
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"boundaries = DATA_DICT[dataset][\"boundaries\"]\n",
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"\n",
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"data_df = pd.read_csv(open(data_path, \"r\"))\n",
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"\n",
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"ts_cols = [col for col in data_df.columns if col != \"date\"]\n",
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"num_cov_cols = None\n",
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"cat_cov_cols = None\n",
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"\n",
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"context_len = 512\n",
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"pred_len = 96\n",
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"\n",
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"num_ts = len(ts_cols)\n",
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"batch_size = 16\n",
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"\n",
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"dtl = data_loader.TimeSeriesdata(\n",
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" data_path=data_path,\n",
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" datetime_col=\"date\",\n",
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" num_cov_cols=num_cov_cols,\n",
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" cat_cov_cols=cat_cov_cols,\n",
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" ts_cols=np.array(ts_cols),\n",
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" train_range=[0, boundaries[0]],\n",
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" val_range=[boundaries[0], boundaries[1]],\n",
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" test_range=[boundaries[1], boundaries[2]],\n",
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" hist_len=context_len,\n",
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" pred_len=pred_len,\n",
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" batch_size=num_ts,\n",
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" freq=\"15min\",\n",
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" normalize=True,\n",
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" epoch_len=None,\n",
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" holiday=False,\n",
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" permute=True,\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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"test_batches = dtl.tf_dataset(mode=\"test\", shift=pred_len)"
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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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"mae_losses = []\n",
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"for batch in tqdm(test_batches.as_numpy_iterator()):\n",
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" past = batch[0]\n",
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" actuals = batch[3]\n",
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" _, forecasts = tfm.forecast(list(past), [0] * past.shape[0])\n",
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" forecasts = forecasts[:, 0 : actuals.shape[1], 5]\n",
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" mae_losses.append(np.abs(forecasts - actuals).mean())\n",
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"\n",
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"print(f\"MAE: {np.mean(mae_losses)}\")"
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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": "tanmay_tfm_env",
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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": "3.10.14"
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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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