{ "cells": [ { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import json\n", "\n", "from pts.dataset.repository import get_dataset\n", "from pts.dataset.utils import to_pandas\n", "\n", "from pts.model.deepar import DeepAREstimator\n", "from pts.modules.distribution_output import ImplicitQuantileOutput\n", "from pts import Trainer\n", "from pts.evaluation import make_evaluation_predictions, Evaluator" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "import torch" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "dataset = get_dataset(\"m5\", regenerate=False)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Recommended prediction horizon: 28\n", "Frequency of the time series: D\n" ] } ], "source": [ "print(f\"Recommended prediction horizon: {dataset.metadata.prediction_length}\")\n", "print(f\"Frequency of the time series: {dataset.metadata.freq}\")" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "estimator = DeepAREstimator(\n", " distr_output=ImplicitQuantileOutput(output_domain=\"Positive\"),\n", " cell_type='GRU',\n", " input_size=72,\n", " num_cells=64,\n", " num_layers=3,\n", " dropout_rate=0.2,\n", " use_feat_dynamic_real=True,\n", " use_feat_static_cat=True,\n", " cardinality=[int(cat_feat_info.cardinality) for cat_feat_info in dataset.metadata.feat_static_cat],\n", " embedding_dimension = [4, 4, 4, 4, 16],\n", " prediction_length=dataset.metadata.prediction_length,\n", " context_length=dataset.metadata.prediction_length*2,\n", " freq=dataset.metadata.freq,\n", " scaling=True,\n", " trainer=Trainer(device=device,\n", " epochs=20,\n", " learning_rate=1e-3,\n", " num_batches_per_epoch=120,\n", " batch_size=256,\n", " num_workers=8,\n", " pin_memory=True,\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "119it [01:01, 1.94it/s, avg_epoch_loss=0.435, epoch=0]\n", "119it [00:56, 2.10it/s, avg_epoch_loss=0.366, epoch=1]\n", "119it [00:59, 2.01it/s, avg_epoch_loss=0.344, epoch=2]\n", "119it [00:57, 2.09it/s, avg_epoch_loss=0.339, epoch=3]\n", "119it [00:57, 2.08it/s, avg_epoch_loss=0.323, epoch=4]\n", "119it [00:56, 2.09it/s, avg_epoch_loss=0.339, epoch=5]\n", "119it [00:58, 2.04it/s, avg_epoch_loss=0.327, epoch=6]\n", "119it [00:56, 2.10it/s, avg_epoch_loss=0.327, epoch=7]\n", "119it [00:57, 2.06it/s, avg_epoch_loss=0.328, epoch=8]\n", "119it [00:58, 2.04it/s, avg_epoch_loss=0.331, epoch=9]\n", "119it [00:57, 2.08it/s, avg_epoch_loss=0.322, epoch=10]\n", "119it [00:59, 2.01it/s, avg_epoch_loss=0.319, epoch=11]\n", "119it [00:58, 2.05it/s, avg_epoch_loss=0.321, epoch=12]\n", "119it [00:58, 2.05it/s, avg_epoch_loss=0.319, epoch=13]\n", "119it [00:58, 2.02it/s, avg_epoch_loss=0.321, epoch=14]\n", "119it [00:55, 2.13it/s, avg_epoch_loss=0.326, epoch=15]\n", "119it [00:57, 2.09it/s, avg_epoch_loss=0.326, epoch=16]\n", "119it [00:59, 2.02it/s, avg_epoch_loss=0.324, epoch=17]\n", "119it [00:57, 2.07it/s, avg_epoch_loss=0.317, epoch=18]\n", "119it [00:58, 2.02it/s, avg_epoch_loss=0.32, epoch=19] \n" ] } ], "source": [ "predictor = estimator.train(dataset.train)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "forecast_it, ts_it = make_evaluation_predictions(\n", " dataset=dataset.test, # test dataset\n", " predictor=predictor, # predictor\n", " num_samples=100, # number of sample paths we want for evaluation\n", ")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "forecasts = list(forecast_it)\n", "tss = list(ts_it)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Running evaluation: 100%|██████████| 30490/30490 [00:01<00:00, 15362.84it/s]\n" ] } ], "source": [ "evaluator = Evaluator()\n", "agg_metrics, item_metrics = evaluator(iter(tss), iter(forecasts), num_series=len(dataset.test))\n", "\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"MSE\": 4.67881831019069,\n", " \"abs_error\": 829715.0852242557,\n", " \"abs_target_sum\": 1231764.0,\n", " \"abs_target_mean\": 1.4428196598416543,\n", " \"seasonal_error\": 1.12721783493784,\n", " \"MASE\": 0.8981459708461484,\n", " \"MAPE\": 0.32066903474970854,\n", " \"sMAPE\": 1.607201008190784,\n", " \"OWA\": NaN,\n", " \"MSIS\": 8.020464013332395,\n", " \"QuantileLoss[0.1]\": 228597.93391284882,\n", " \"Coverage[0.1]\": 0.5465105655249968,\n", " \"QuantileLoss[0.2]\": 427520.68589925097,\n", " \"Coverage[0.2]\": 0.5526788642646276,\n", " \"QuantileLoss[0.3]\": 596678.5831146002,\n", " \"Coverage[0.3]\": 0.5641638944853101,\n", " \"QuantileLoss[0.4]\": 733503.7435174085,\n", " \"Coverage[0.4]\": 0.5829768542379278,\n", " \"QuantileLoss[0.5]\": 829715.0852156124,\n", " \"Coverage[0.5]\": 0.6140994705524139,\n", " \"QuantileLoss[0.6]\": 881578.0619610748,\n", " \"Coverage[0.6]\": 0.6517195333364716,\n", " \"QuantileLoss[0.7]\": 872760.4227906723,\n", " \"Coverage[0.7]\": 0.7082111230848716,\n", " \"QuantileLoss[0.8]\": 783768.9510111441,\n", " \"Coverage[0.8]\": 0.7846483624607741,\n", " \"QuantileLoss[0.9]\": 573582.3997516176,\n", " \"Coverage[0.9]\": 0.873871995502109,\n", " \"RMSE\": 2.1630576298819895,\n", " \"NRMSE\": 1.4991877987851783,\n", " \"ND\": 0.6735990702961409,\n", " \"wQuantileLoss[0.1]\": 0.18558582156391062,\n", " \"wQuantileLoss[0.2]\": 0.3470800298590079,\n", " \"wQuantileLoss[0.3]\": 0.48440982453992826,\n", " \"wQuantileLoss[0.4]\": 0.5954904864222437,\n", " \"wQuantileLoss[0.5]\": 0.6735990702891239,\n", " \"wQuantileLoss[0.6]\": 0.7157037078215266,\n", " \"wQuantileLoss[0.7]\": 0.7085451618903233,\n", " \"wQuantileLoss[0.8]\": 0.6362979848502993,\n", " \"wQuantileLoss[0.9]\": 0.4656593306441961,\n", " \"mean_wQuantileLoss\": 0.5347079353200622,\n", " \"MAE_Coverage\": 0.16242666083597068\n", "}\n" ] } ], "source": [ "print(json.dumps(agg_metrics, indent=4))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "item_metrics.plot(x='MSIS', y='MASE', kind='scatter')\n", "plt.grid(which=\"both\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "pytorchts-experiment", "language": "python", "name": "pytorchts-experiment" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }