diff --git a/examples/m5.ipynb b/examples/m5.ipynb new file mode 100644 index 0000000..dca854e --- /dev/null +++ b/examples/m5.ipynb @@ -0,0 +1,398 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from pts.dataset.repository import get_dataset\n", + "from pts.dataset.utils import to_pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = get_dataset(\"m5\", regenerate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "entry = next(iter(dataset.train))\n", + "train_series = to_pandas(entry)\n", + "train_series.plot()\n", + "plt.grid(which=\"both\")\n", + "plt.legend([\"train series\"], loc=\"upper left\")\n", + "plt.title(entry['item_id'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "entry = next(iter(dataset.test))\n", + "test_series = to_pandas(entry)\n", + "test_series.plot()\n", + "plt.axvline(train_series.index[-1], color='r') # end of train dataset\n", + "plt.grid(which=\"both\")\n", + "plt.legend([\"test series\", \"end of train series\"], loc=\"upper left\")\n", + "plt.title(entry['item_id'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from pts.model.deepar import DeepAREstimator\n", + "from pts.modules import ZeroInflatedNegativeBinomialOutput\n", + "from pts import Trainer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "estimator = DeepAREstimator(\n", + " distr_output=ZeroInflatedNegativeBinomialOutput(),\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=50,\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": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "119it [00:29, 4.09it/s, avg_epoch_loss=1.17, epoch=0]\n", + "119it [00:31, 3.81it/s, avg_epoch_loss=1.14, epoch=1]\n", + "119it [00:27, 4.29it/s, avg_epoch_loss=1.12, epoch=2]\n", + "119it [00:29, 4.06it/s, avg_epoch_loss=1.11, epoch=3]\n", + "119it [00:28, 4.17it/s, avg_epoch_loss=1.1, epoch=4]\n", + "119it [00:28, 4.14it/s, avg_epoch_loss=1.1, epoch=5]\n", + "119it [00:30, 3.93it/s, avg_epoch_loss=1.1, epoch=6] \n", + "119it [00:27, 4.27it/s, avg_epoch_loss=1.11, epoch=7]\n", + "119it [00:28, 4.16it/s, avg_epoch_loss=1.09, epoch=8]\n", + "119it [00:29, 4.09it/s, avg_epoch_loss=1.11, epoch=9]\n", + "119it [00:27, 4.40it/s, avg_epoch_loss=1.1, epoch=10]\n", + "119it [00:28, 4.23it/s, avg_epoch_loss=1.1, epoch=11]\n", + "119it [00:28, 4.12it/s, avg_epoch_loss=1.1, epoch=12] \n", + "119it [00:29, 4.06it/s, avg_epoch_loss=1.1, epoch=13]\n", + "119it [00:29, 4.10it/s, avg_epoch_loss=1.11, epoch=14]\n", + "119it [00:28, 4.24it/s, avg_epoch_loss=1.1, epoch=15] \n", + "119it [00:30, 3.95it/s, avg_epoch_loss=1.1, epoch=16]\n", + "119it [00:27, 4.28it/s, avg_epoch_loss=1.09, epoch=17]\n", + "119it [00:27, 4.26it/s, avg_epoch_loss=1.1, epoch=18]\n", + "119it [00:29, 4.07it/s, avg_epoch_loss=1.1, epoch=19] \n", + "119it [00:29, 3.98it/s, avg_epoch_loss=1.09, epoch=20]\n", + "119it [00:27, 4.33it/s, avg_epoch_loss=1.1, epoch=21]\n", + "119it [00:29, 4.08it/s, avg_epoch_loss=1.09, epoch=22]\n", + "119it [00:29, 4.09it/s, avg_epoch_loss=1.09, epoch=23]\n", + "119it [00:28, 4.22it/s, avg_epoch_loss=1.09, epoch=24]\n", + "119it [00:27, 4.26it/s, avg_epoch_loss=1.09, epoch=25]\n", + "119it [00:31, 3.81it/s, avg_epoch_loss=1.1, epoch=26]\n", + "119it [00:31, 3.73it/s, avg_epoch_loss=1.09, epoch=27]\n", + "119it [00:27, 4.32it/s, avg_epoch_loss=1.08, epoch=28]\n", + "119it [00:28, 4.14it/s, avg_epoch_loss=1.09, epoch=29]\n", + "119it [00:30, 3.87it/s, avg_epoch_loss=1.08, epoch=30]\n", + "119it [00:28, 4.19it/s, avg_epoch_loss=1.09, epoch=31]\n", + "119it [00:28, 4.17it/s, avg_epoch_loss=1.08, epoch=32]\n", + "119it [00:29, 4.09it/s, avg_epoch_loss=1.1, epoch=33] \n", + "119it [00:27, 4.39it/s, avg_epoch_loss=1.09, epoch=34]\n", + "119it [00:28, 4.21it/s, avg_epoch_loss=1.09, epoch=35]\n", + "119it [00:28, 4.16it/s, avg_epoch_loss=1.09, epoch=36]\n", + "119it [00:27, 4.31it/s, avg_epoch_loss=1.08, epoch=37]\n", + "119it [00:29, 4.07it/s, avg_epoch_loss=1.09, epoch=38]\n", + "119it [00:28, 4.19it/s, avg_epoch_loss=1.09, epoch=39]\n", + "119it [00:29, 4.06it/s, avg_epoch_loss=1.09, epoch=40]\n", + "119it [00:28, 4.14it/s, avg_epoch_loss=1.08, epoch=41]\n", + "119it [00:28, 4.16it/s, avg_epoch_loss=1.09, epoch=42]\n", + "119it [00:27, 4.25it/s, avg_epoch_loss=1.09, epoch=43]\n", + "119it [00:27, 4.26it/s, avg_epoch_loss=1.1, epoch=44]\n", + "119it [00:26, 4.41it/s, avg_epoch_loss=1.09, epoch=45]\n", + "119it [00:27, 4.25it/s, avg_epoch_loss=1.08, epoch=46]\n", + "119it [00:28, 4.20it/s, avg_epoch_loss=1.09, epoch=47]\n", + "119it [00:30, 3.92it/s, avg_epoch_loss=1.09, epoch=48]\n", + "119it [00:30, 3.96it/s, avg_epoch_loss=1.09, epoch=49]\n" + ] + } + ], + "source": [ + "predictor = estimator.train(dataset.train)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from pts.evaluation import make_evaluation_predictions, Evaluator" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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": 14, + "metadata": {}, + "outputs": [], + "source": [ + "forecasts = list(forecast_it)\n", + "tss = list(ts_it)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running evaluation: 100%|██████████| 30490/30490 [00:01<00:00, 23152.64it/s]\n" + ] + } + ], + "source": [ + "evaluator = Evaluator()\n", + "agg_metrics, item_metrics = evaluator(iter(tss), iter(forecasts), num_series=len(dataset.test))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"MSE\": 4.439620313754265,\n", + " \"abs_error\": 807089.0,\n", + " \"abs_target_sum\": 1231764.0,\n", + " \"abs_target_mean\": 1.4428196598416343,\n", + " \"seasonal_error\": 1.1272178349378457,\n", + " \"MASE\": 0.8789472000957106,\n", + " \"MAPE\": 0.30587227335898637,\n", + " \"sMAPE\": 0.6816909686747539,\n", + " \"OWA\": NaN,\n", + " \"MSIS\": 7.28829495943688,\n", + " \"QuantileLoss[0.1]\": 228315.8,\n", + " \"Coverage[0.1]\": 0.0042929766199690765,\n", + " \"QuantileLoss[0.2]\": 422650.8,\n", + " \"Coverage[0.2]\": 0.01732535257461463,\n", + " \"QuantileLoss[0.3]\": 586642.4,\n", + " \"Coverage[0.3]\": 0.042479970013587595,\n", + " \"QuantileLoss[0.4]\": 716891.6,\n", + " \"Coverage[0.4]\": 0.08317012603663966,\n", + " \"QuantileLoss[0.5]\": 807089.0,\n", + " \"Coverage[0.5]\": 0.14288174108607035,\n", + " \"QuantileLoss[0.6]\": 854345.2,\n", + " \"Coverage[0.6]\": 0.2176439582064377,\n", + " \"QuantileLoss[0.7]\": 842037.0,\n", + " \"Coverage[0.7]\": 0.3306306517359322,\n", + " \"QuantileLoss[0.8]\": 755156.7999999999,\n", + " \"Coverage[0.8]\": 0.48669352949444783,\n", + " \"QuantileLoss[0.9]\": 547328.7999999999,\n", + " \"Coverage[0.9]\": 0.7026999484608537,\n", + " \"RMSE\": 2.1070406530853325,\n", + " \"NRMSE\": 1.4603631429007586,\n", + " \"ND\": 0.655230222672525,\n", + " \"wQuantileLoss[0.1]\": 0.185356772888313,\n", + " \"wQuantileLoss[0.2]\": 0.34312644305240286,\n", + " \"wQuantileLoss[0.3]\": 0.47626201122942385,\n", + " \"wQuantileLoss[0.4]\": 0.5820040202506324,\n", + " \"wQuantileLoss[0.5]\": 0.655230222672525,\n", + " \"wQuantileLoss[0.6]\": 0.6935948769407126,\n", + " \"wQuantileLoss[0.7]\": 0.6836025407464417,\n", + " \"wQuantileLoss[0.8]\": 0.6130693866682253,\n", + " \"wQuantileLoss[0.9]\": 0.44434550774336634,\n", + " \"mean_wQuantileLoss\": 0.5196213091324493,\n", + " \"MAE_Coverage\": 0.2746868606412719\n", + "}\n" + ] + } + ], + "source": [ + "print(json.dumps(agg_metrics, indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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EQIU/UnS7RREBDdIWORqnR0RU+MdprPe5rYT9jXYwNxGZmFT4x6HUXSPVFVNEikFt/AUq9S0MdctEESkWFf4ClbprpLpiikixqPAXqNRdI9UVU0SKRYW/QKXuGqmumCJSLDq5Ow6l7hqprpgiUgwq/ONU6q6R6opZHqXutisSJhV+kTzUjVYmGrXxi4xA3WhlIlLhFxmButHKRKTCX2K6+1V1UTdamYhU+EtoY+cBlt71BH/8jW0svesJNnUeKHckyaMau9Hq4ELy0cndUcjVo2OsvTx096vqVU3daHUiWkZDhT+PXH9IDmP64+pK9tDxy0PUxAbfiXKgrbiSC4mkVUM3Wh1cyGiFVvjN7D7gKuCQuy8Klp0OPAjMBfYCN7p7d1gZxivXH9In1+8AjJ6+0f1xbew8wOr1O4hhHOsLp61YfcwFTp2IHvi9BB1cSG5htvH/A3BF1rLbgC3ufjawJZivWLl6dJgZ8WGO3LN1JXv4xEOd9PT5oKLfWB8vuK04u/12Ipw3UJt0cehEtIxWaEf87v4vZjY3a/E1wLJgeh2wFVgTVobxmj2tgRP9/YOWHe9NDSn8uf64upI9fH/Hq2Qd5APwyfeey9WLZ4656Gc3O91+1QI+t/m5qv7XXm3SxTNwInp11vtZLb8LUjrm7uG9eLrwb85o6jns7lODaQO6B+ZzbLsKWAWQSCRa2tvbC8qQTCZpamoqaNsjx3p5pfsYw71HMUt/AMye1sCUhtpB2w38B5DKse286Y001Q/9zB0pa3/K+eVrbwx6PTPDsvYRN2PeGY001Mbzf4PjMJ73dUCu7ylmxtvfMnnIh2uhipGzVIqVtT/lnOhPURePFe19zBbF9zVsYeRsa2vb7u6t2cvLdnLX3d3Mhv3Ucfe1wFqA1tZWX7ZsWUH72bp1K4Vs25XsYeldT3C8d/gCWhc3Hvuz9zA/MXnU29XGjSc/9Z6cR2EjZd3xymG+9uNtvNHTd3JZY32c3r4UJ/pPvY2TamP8dMUloR/lFfq+Zsr1PU2ur+Hb77mAxXOmji9goBg5S0VZw1EtWUuZs9T9+A+a2QyA4OuhEu9/1HK172err4lz9MTgpqDhtquviVFfE+OL/2nxoC6ho23bztV+259yPnP1wqrqY55JbdIi5VHqI/5NwErg88HXjSXe/6g11sXp6c/RQJ8hV5FqrIvT0zf4w6C+xvj6B1tYOHPKyaKcq237TSPsa7j22xVLZnHFordUZa8etUmLlEeY3TkfIH0id7qZ7Qc+Q7rgP2RmtwD7gBvD2v94DHTB9BHqfn3N0KPrgWIeixn0O/Vxw2LG3defz6XnvPnk84brb/2Vtkkj5hq4kGj3q0cAY+HM9EdFNfQxH041XRwlMlGE2avn5mFWLQ9rn8Ww5+Ab/Hl7JyMd65vBHVcvONn7pCvZw+5Xj7B6/Q56+k61t7sZj956yaBzAAC7Xz1C9im32liME3n+wwD4yZ7XJ1wvmGr+4BKpRrpyN8P9T+7j9o27Riz6AO5w5/ef44qFbzlZiGNmg4o+QH08NuQcwMbOA3z8wU76s05r96bSvTBGoiszRaQYIl34B654bayLs+EX+/nqj18e9bb9KWf3q0dOFuJcTvSnOHKsl65kD81N9XQle1i9fseQog9w+5ULiB//9Yj73P3qkZNdSAdkXpmpK3hFZDQiW/gH2uOBYQv3SPpSzne27cNy9NM/rTZObypFfyrFh+//xckmmTObG4lbDBj8X0BDbZxFs6bQ/dLIebObkuDUCWZdCCUioxXJYZkHNZkUUPQH/HD3IY5lN+/UxLj7hvOIGfSlGHTXpsa6OP05zhin3EfswjiQd0hTUnCCGSjaXaI0fILIxBfJwj+aPvqFuuPqBcw5vZG6+OALuOIxo/OVw1yTdRQeM/jrG0buwpgr72m1cb7+wVZWLJlVtLtEjWfcH31giFSPSDb1zJ7WwO9P9OV/4hg11sVZNHMKjXVxft87+PWP9vRz+yPPDvkPoTYeY+n86cO+ZleyhyPHTgwZMyiFs3Dmm4Zdn32NQb72//GcOFYzk0h1iWTh7z56It0ns8jjFPW7s+vAEe7c/By5emZmF32AuvipI/Njvf0nTwTDQLv+TuIxozfrjPCNrbMHde1MOdTEoKG2ZsiFUJmF+UR/P7e2nc373/XWQQW90CF91dNIpPpErvBv7DzAJ7+7g/5UcYt+XU2M269cwJ2bdw9pix/Jif4Uuw4c4Y/W/ow/e0cvf37XE9x9/fksnT+dv/jujiEFf8CDT+3ngZ+/Qm+/nyy49TUxvvKBC1k4802DhoXILsxffPxX/G3HHu64egGLZk5h9rSGgodP0BjwItUnUm38A0XwxDDFdDy++v4L2N/9+zEVfYC+/hR/+f3dHO9N0e9+8sTsz156fdiiD9DTlxqyvi4eY0pDbc4j+Vzbf/rhXbz/60+y9K4n+Ome1wu6t6zG2xGpPpE64t/96pFx9eIZyS3f2l7Qdv0O/VkFPB4zXk+eGPNr5Sq4uQpzpoELzFZv2MlP11zGT9dcNqZrATTejkj1iUzhv//JfXz6kV3ljjEqR3v66T56gpoYOW/kMpwVOW7uMlCYP5njGoBMA80zi+dMHXPR1ng7ItUlEk091VT0B9zzxB6uu3AWtWP4CT3S+WrO7pQrlsziX29bzicuP4f6mhiN9UPvFTDe5pnmpvqCPjREpPQm/BF/f8r57Obnyh2jIA89Pbb759bGbdiTqs1N9Xxkebo3z/7uY+w6cITPPfqcmmdEImjCF/4T/SlOjKW9pIr1p0a+AhhOjYS5eM7Uqh3HX0TGZ8IX/teOHC93hNCdVpfuxz/Wo3YNhywSTRO68N//5D6SPX1U27c50K4/Ugekxvo4/Snn9qtO9cVXEReR0aiuijgGXckePv3ILj5xXrmTjN2X/ugCLj6rmY+1P8P/29M1aF08Zty5YiGLZqnYi0hhJmzhT9+esPp88OK3ctXimQD844cu4ulfd/FI56vUxeHCM5u5+KxmFXsRGZcJW/g/eN9T5Y4wSMygoS5OT28/jg0ZMuLdZ53OOYljfOQPBv+L0jqvmdZ5zaWMKiITXFkKv5ldAdwDxIFvuPvny5EjbLUxw8z4zIoFXLHwVA8aSP9HcqD7GD19KS6ZP535icls3bq1vIFFJBJKXvjNLA58Bbgc2A88ZWab3L1one3LPSZ8PAZr/uPbedfbmge1w2c20Vx6zpvLFU9EIq4cR/zvBPa4+8sAZtYOXAMUrfBf8lf/XKyXGrXL3/FmLnv7m5k1rYGFM6eoHV5EKpZ5kcekz7tDsxuAK9z9Q8H8nwDvcvdbs563ClgFkEgkWtrb20e9j2cPnDqxm2iAg2O7EdWIDE6OgJkK3ruG2jjxmI284Sgkk0mamprG/TqlUC1ZqyUnKGtYqiVrGDnb2tq2u3tr9vKKPbnr7muBtQCtra2+bNmyUW976x2PMjC45SfO6+OLzxb2bSYm1/Jf3j2P5smTmNt8GrU18VC7UG7dupWxfJ/lVC1ZqyUnKGtYqiVrKXOWo/AfAOZkzM8OlhXNrjuvZO5tjxa07aKZk7n0nDO47oLZzE9MLmYsEZGKUI7C/xRwtpnNI13wbwLeX+yd7P38lSy6Y+Tif26ikXefNZ1zEpOZNe20QXeuEhGZqEpe+N29z8xuBX5Eujvnfe6+O4x97brzSrZu3creDywL4+VFRKpSWdr43f0x4LFy7FtEJOoicSMWERE5RYVfRCRiVPhFRCJGhV9EJGJKfuVuIczst8C+AjefDrxexDhhUtbiq5acoKxhqZasYeQ8093PyF5YFYV/PMzs6VyXLFciZS2+askJyhqWaslaypxq6hERiRgVfhGRiIlC4V9b7gBjoKzFVy05QVnDUi1ZS5Zzwrfxi4jIYFE44hcRkQwq/CIiETOhC7+ZXWFmL5jZHjO7rcxZ7jOzQ2a2K2PZ6Wb2uJm9GHydFiw3M/tykHunmV1Y4qxzzKzDzJ4zs91m9tFKzWtmk8zs52a2I8j62WD5PDPbFmR60MzqguX1wfyeYP3cUmUN9h83s2fMbHOF59xrZs+aWaeZPR0sq7iff7D/qWa23sx+aWbPm9nFlZjVzM4N3s+Bx+/M7GNlyeruE/JBesjnl4C3AXXADmBBGfNcClwI7MpYdjdwWzB9G3BXMP0+4Aek7/R4EbCtxFlnABcG05OBXwELKjFvsM+mYLoW2BZkeAi4KVj+NeC/B9P/A/haMH0T8GCJ39uPA98BNgfzlZpzLzA9a1nF/fyD/a8DPhRM1wFTKzVrRuY48BpwZjmylvwbLuEbezHwo4z5TwGfKnOmuVmF/wVgRjA9A3ghmP574OZczytT7o3A5ZWeFzgN+AXwLtJXQNZk/y6Qvg/ExcF0TfA8K1G+2cAW4DJgc/AHXXE5g33mKvwV9/MHpgC/zn5vKjFrVr73Aj8tV9aJ3NQzC3glY35/sKySJNz9N8H0a0AimK6Y7EETwwWkj6QrMm/QfNIJHAIeJ/2f3mF378uR52TWYP0RoLlEUf8PsBpIBfPNFZoTwIF/MrPtZrYqWFaJP/95wG+BbwZNaN8ws8YKzZrpJuCBYLrkWSdy4a8qnv5Ir6i+tWbWBGwAPubuv8tcV0l53b3f3ZeQPqJ+J/D28iYaysyuAg65+/ZyZxmlS9z9QuAPgQ+b2aWZKyvo519Dugn1q+5+AXCUdHPJSRWUFYDgPM4K4LvZ60qVdSIX/tBv6l4EB81sBkDw9VCwvOzZzayWdNG/392/Fyyu2LwA7n4Y6CDdZDLVzAbuMJeZ52TWYP0UoKsE8ZYCK8xsL9BOurnnngrMCYC7Hwi+HgIeJv2BWok///3AfnffFsyvJ/1BUIlZB/wh8At3PxjMlzzrRC78J2/qHnzC3gRsKnOmbJuAlcH0StJt6QPLPxic1b8IOJLxr2DozMyAe4Hn3f1vKjmvmZ1hZlOD6QbS5yKeJ/0BcMMwWQe+hxuAJ4KjrFC5+6fcfba7zyX9u/iEu3+g0nICmFmjmU0emCbdHr2LCvz5u/trwCtmdm6waDnwXCVmzXAzp5p5BjKVNmupT2qU+ATK+0j3SHkJ+HSZszwA/AboJX2UcgvpNtstwIvAPwOnB8814CtB7meB1hJnvYT0v5s7gc7g8b5KzAucDzwTZN0F3BEsfxvwc2AP6X+p64Plk4L5PcH6t5Xhd2EZp3r1VFzOINOO4LF74G+nEn/+wf6XAE8HvwOPANMqOGsj6f/cpmQsK3lWDdkgIhIxE7mpR0REclDhFxGJGBV+EZGIUeEXEYkYFX4RkYhR4RfJYmZuZt/OmK8xs9/aqRE1E2a22dIjgj5nZo8Fy+daMPqqmZ1mZvdbeoTLXWb2k+BKaJGyq8n/FJHIOQosMrMGdz9G+qKwzCsm7wQed/d7AMzs/Byv8VHgoLufFzznXNLXcIiUnY74RXJ7DLgymM6+0nIG6YvwAHD3nTm2n0HGh4W7v+DuPSHkFBkzFX6R3NqBm8xsEumrg7dlrPsKcK+lb1bzaTObmWP7+4A1ZvYzM/vfZnZ2CTKLjIoKv0gOwVH8XNJH+49lrfsR6WENvk56JNBnzOyMrOd0Bs/5a+B04Ckze0fowUVGQW38IsPbBHyB9Ng6g8bCd/d/J30nre8EJ30vBbZnPScJfA/4npmlSI939Hz4sUVGpiN+keHdB3zW3Z/NXGhml5nZacH0ZOAs4N+ynrM0496pdaRvXbmvJKlF8tARv8gw3H0/8OUcq1qAvzWzPtIHT99w96ds8A3RzwK+GgxxHQMeJX1/A5Gy0+icIiIRo6YeEZGIUeEXEYkYFX4RkYhR4RcRiRgVfhGRiFHhFxGJGBV+EZGI+f8bmVGLMkg5mgAAAABJRU5ErkJggg==\n", 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" + ] + }, + "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": "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}