02-forecasting_with_deep_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2d59165-02ca-4369-a94e-3e6ec73d65b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04e94915-cfe3-41eb-ab51-5b55074eccc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import os\n",
    "import warnings\n",
    "import category_encoders\n",
    "import gluonts\n",
    "import mxnet\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from pathlib import Path\n",
    "from sklearn.preprocessing import MinMaxScaler, OrdinalEncoder\n",
    "from category_encoders.hashing import HashingEncoder\n",
    "from gluonts.dataset.multivariate_grouper import MultivariateGrouper\n",
    "from gluonts.evaluation import Evaluator, MultivariateEvaluator\n",
    "from gluonts.evaluation.backtest import make_evaluation_predictions\n",
    "from gluonts.dataset.common import ListDataset\n",
    "from gluonts.dataset.field_names import FieldName\n",
    "from gluonts.model.deepar import DeepAREstimator\n",
    "from gluonts.model.deepvar import DeepVAREstimator\n",
    "from gluonts.model.predictor import Predictor\n",
    "from gluonts.mx.distribution import (\n",
    "    LowrankMultivariateGaussianOutput,\n",
    "    NegativeBinomialOutput, \n",
    ")\n",
    "from gluonts.mx.trainer import Trainer\n",
    "from mxnet.context import num_gpus\n",
    "from utils.evaluation import calc_eval_metric, WRMSSEEvaluator\n",
    "\n",
    "mxnet.random.seed(42)\n",
    "np.random.seed(42)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3935438-e3c0-41e5-87ab-5c5a64f53b3a",
   "metadata": {},
   "source": [
    "The Kaggle dataset was saved in the local directory `~/data/mofc-demand-forecast` in advance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1ea8ceb-ddf6-49d8-9311-77d26cc5282d",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = \"../../data/mofc-demand-forecast\"\n",
    "MODEL_PATH = \"models\"\n",
    "\n",
    "calendar = pd.read_csv(os.path.join(DATA_PATH, \"calendar.csv\"))\n",
    "selling_prices = pd.read_csv(os.path.join(DATA_PATH, \"sell_prices.csv\"))\n",
    "df_train_valid = pd.read_csv(os.path.join(DATA_PATH, \"sales_train_validation.csv\"))\n",
    "df_train_eval = pd.read_csv(os.path.join(DATA_PATH, \"sales_train_evaluation.csv\"))\n",
    "sample_submission = pd.read_csv(os.path.join(DATA_PATH, \"sample_submission.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "738144a6-729f-4e48-8844-8f7d284a8016",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "305 out of 30490 IDs were selected for testing.\n"
     ]
    }
   ],
   "source": [
    "key_names = [\"id\", \"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n",
    "all_ids = df_train_eval[\"id\"].unique()\n",
    "date_names = [\"d_\" + str(i) for i in range(1, 1942)]\n",
    "dates = calendar[\"date\"].unique()\n",
    "test_steps = 28\n",
    "\n",
    "key_pairs = list(itertools.product(all_ids, dates))\n",
    "key_pairs = pd.DataFrame(key_pairs, columns=[\"id\", \"date\"])\n",
    "\n",
    "sample_ratio = 0.01\n",
    "\n",
    "if sample_ratio == 1.0:\n",
    "    sampled_ids = all_ids\n",
    "else:\n",
    "    sampled_ids = np.random.choice(\n",
    "        all_ids, round(sample_ratio * len(all_ids)), replace=False\n",
    "    ).tolist()\n",
    "    \n",
    "print(\n",
    "    f\"{len(sampled_ids)} out of {len(all_ids)} IDs were selected for testing.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9edd18-b652-4fed-86e9-97a7cdc40e08",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7d39a4d-d7d8-432b-a50f-8d480b250bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df_train_eval[[\"id\"] + date_names]\n",
    "target = target.set_index(\"id\").T.reset_index()\n",
    "date_dict = calendar[[\"date\", \"d\"]].set_index(\"d\").to_dict()[\"date\"]\n",
    "target[\"index\"] = target[\"index\"].replace(date_dict)\n",
    "target.columns = [\"date\"] + target.columns[1:].tolist()\n",
    "target = target.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ae6db7f-f6ca-4fb8-8bec-7659a8681bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\"event_name_1\", \"event_type_1\", \"event_name_2\", \"event_type_2\"]\n",
    "events = calendar[[\"date\"] + feature_names].fillna(\"NA\")\n",
    "train = events[events[\"date\"] < dates[-2 * test_steps]][feature_names]\n",
    "\n",
    "encoder = HashingEncoder(drop_invariant=True)\n",
    "_ = encoder.fit(train)\n",
    "encoded = encoder.transform(events[feature_names])\n",
    "events = pd.concat([events[[\"date\"]], encoded], axis=1)\n",
    "\n",
    "time_related = calendar[[\"date\", \"wday\", \"month\"]]\n",
    "time_related[\"day\"] = time_related[\"date\"].map(lambda x: int(x.split(\"-\")[2]))\n",
    "\n",
    "feat_dynamic_cat = events.merge(time_related).set_index(\"date\")\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaled = scaler.fit_transform(feat_dynamic_cat)\n",
    "feat_dynamic_cat = pd.DataFrame(\n",
    "    scaled, columns=feat_dynamic_cat.columns, index=feat_dynamic_cat.index\n",
    ")\n",
    "n_feat_dynamic_cat = feat_dynamic_cat.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "042273de-a9e6-4422-a7d7-7ce528ef6f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = (\n",
    "    df_train_eval[[\"id\", \"store_id\", \"item_id\"]]\n",
    "    .merge(selling_prices, how=\"left\")\n",
    "    .drop([\"store_id\", \"item_id\"], axis=1)\n",
    ")\n",
    "week_to_date = calendar[[\"date\", \"wm_yr_wk\"]].drop_duplicates()\n",
    "prices = week_to_date.merge(prices, how=\"left\").drop(\n",
    "    [\"wm_yr_wk\"], axis=1\n",
    ")\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "train = prices[prices[\"date\"] < dates[-2 * test_steps]][[\"sell_price\"]]\n",
    "\n",
    "_ = scaler.fit(train)\n",
    "prices[\"sell_price\"] = scaler.transform(prices[[\"sell_price\"]])\n",
    "prices = prices.pivot(index=\"date\", columns=\"id\", values=\"sell_price\")\n",
    "prices = prices.fillna(method=\"bfill\")\n",
    "\n",
    "snap = calendar[[\"date\", \"snap_CA\", \"snap_TX\", \"snap_WI\"]]\n",
    "snap.columns = [\"date\", \"CA\", \"TX\", \"WI\"]\n",
    "snap = pd.melt(\n",
    "    snap,\n",
    "    id_vars=\"date\",\n",
    "    value_vars=[\"CA\", \"TX\", \"WI\"],\n",
    "    var_name=\"state_id\",\n",
    "    value_name=\"snap\",\n",
    ")\n",
    "snap = key_pairs.merge(df_train_eval[[\"id\", \"state_id\"]], how=\"left\").merge(\n",
    "    snap, on=[\"date\", \"state_id\"], how=\"left\"\n",
    ")\n",
    "snap = snap.pivot(index=\"date\", columns=\"id\", values=\"snap\")\n",
    "\n",
    "feat_dynamic_real = pd.concat([prices, snap], axis=1)\n",
    "n_feat_dynamic_real = int(feat_dynamic_real.shape[1] / target.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e5e5832-c6e2-4e0b-874d-4e50006d5af3",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n",
    "feat_static_cat = df_train_eval[[\"id\"] + feature_names]\n",
    "\n",
    "encoder = OrdinalEncoder()\n",
    "feat_static_cat[feature_names] = encoder.fit_transform(feat_static_cat[feature_names])\n",
    "feat_static_cat[feature_names] = feat_static_cat[feature_names].astype(int)\n",
    "feat_static_cat = feat_static_cat.set_index(\"id\").T\n",
    "\n",
    "cardinality = [len(category) for category in encoder.categories_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1e94eeb-ac21-4344-869b-c08b51092fb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_into_n_array(x, n):\n",
    "    return np.hsplit(x.values.T.ravel(), n)\n",
    "\n",
    "\n",
    "train_list = []\n",
    "for test_sampled_id in sampled_ids:\n",
    "    dict_by_id = {\n",
    "        FieldName.TARGET: target[test_sampled_id].iloc[:-test_steps].values,\n",
    "        FieldName.START: target.index[0],\n",
    "        FieldName.FEAT_DYNAMIC_REAL: split_into_n_array(\n",
    "            feat_dynamic_cat.iloc[: -2 * test_steps], \n",
    "            n_feat_dynamic_cat,\n",
    "        )\n",
    "        + split_into_n_array(\n",
    "            feat_dynamic_real[test_sampled_id].iloc[: -2 * test_steps],\n",
    "            n_feat_dynamic_real,\n",
    "        ),\n",
    "        FieldName.FEAT_STATIC_CAT: feat_static_cat[test_sampled_id].values,\n",
    "    }\n",
    "    train_list.append(dict_by_id)\n",
    "\n",
    "test_list = []\n",
    "for test_sampled_id in sampled_ids:\n",
    "    dict_by_id = {\n",
    "        FieldName.TARGET: target[test_sampled_id].values,\n",
    "        FieldName.START: target.index[0],\n",
    "        FieldName.FEAT_DYNAMIC_REAL: split_into_n_array(\n",
    "            feat_dynamic_cat.iloc[: -test_steps], \n",
    "            n_feat_dynamic_cat,\n",
    "        )\n",
    "        + split_into_n_array(\n",
    "            feat_dynamic_real[test_sampled_id].iloc[: -test_steps],\n",
    "            n_feat_dynamic_real,\n",
    "        ),\n",
    "        FieldName.FEAT_STATIC_CAT: feat_static_cat[test_sampled_id].values,\n",
    "    }\n",
    "    test_list.append(dict_by_id)\n",
    "    \n",
    "train_dataset = ListDataset(train_list, freq=\"D\")\n",
    "test_dataset = ListDataset(test_list, freq=\"D\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9b1c0b-04ba-4a34-978b-86b5b924af81",
   "metadata": {},
   "source": [
    "# DeepAR: Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aedcf2e0-d9af-4f1d-a51b-32fe46581f67",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"gpu\" if num_gpus() > 0 else \"cpu\"\n",
    "trainer = Trainer(\n",
    "    ctx=device,\n",
    "    epochs=200,\n",
    "    num_batches_per_epoch=50,\n",
    "    learning_rate=0.001,\n",
    "    hybridize=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b489c21-badd-4bfa-8407-e0f1cf0f815d",
   "metadata": {},
   "outputs": [],
   "source": [
    "deepar_estimator = DeepAREstimator(\n",
    "    freq=\"D\", \n",
    "    prediction_length=test_steps,\n",
    "    trainer=trainer,\n",
    "    context_length=2 * test_steps,\n",
    "    num_layers=2,\n",
    "    num_cells=40,\n",
    "    cell_type=\"lstm\",\n",
    "    dropout_rate=0.1,\n",
    "    use_feat_dynamic_real=True,\n",
    "    use_feat_static_cat=True,\n",
    "    use_feat_static_real=False,\n",
    "    cardinality=cardinality,\n",
    "    distr_output=NegativeBinomialOutput(),\n",
    "    batch_size=30,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "037efb06-81ec-4875-b587-1a0b41d28bac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:Serializing RepresentableBlockPredictor instances does not save the prediction network structure in a backwards-compatible manner. Be careful not to use this method in production.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping based on learning rate scheduler callback (min_lr was reached).\n",
      "CPU times: user 7min 53s, sys: 12.9 s, total: 8min 6s\n",
      "Wall time: 7min 54s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "deepar_predictor = deepar_estimator.train(train_dataset)\n",
    "\n",
    "os.makedirs(os.path.join(MODEL_PATH, \"deepar\"), exist_ok=True)\n",
    "deepar_predictor.serialize(Path(os.path.join(MODEL_PATH, \"deepar\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9bab3d-d4b2-4d5b-a0fd-3f307ebfdbe6",
   "metadata": {},
   "source": [
    "# DeepAR: Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "758f8a4f-48b0-4a2c-978a-b621336b9276",
   "metadata": {},
   "outputs": [],
   "source": [
    "deepar_predictor = Predictor.deserialize(Path(os.path.join(MODEL_PATH, \"deepar\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b33084a2-5bbf-4f4a-841d-5e13c111b129",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.65 s, sys: 1.24 s, total: 10.9 s\n",
      "Wall time: 9.48 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "forecast_iter, ts_iter = make_evaluation_predictions( \n",
    "    dataset=test_dataset,\n",
    "    predictor=deepar_predictor, \n",
    "    num_samples=100,\n",
    ") \n",
    "forecasts = list(forecast_iter)\n",
    "tss = list(ts_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0f2f5f87-12e2-41ee-bdc9-66f1d6fc5e3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running evaluation: 100%|██████████| 305/305 [00:00<00:00, 7591.75it/s]\n"
     ]
    }
   ],
   "source": [
    "num_series = len(sampled_ids)\n",
    "\n",
    "evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])\n",
    "agg_metrics, item_metrics = evaluator(\n",
    "    iter(tss), iter(forecasts), num_series=num_series\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "907b3737-3aef-44d6-ae6e-795d7a665582",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 2.3164\n",
      "abs_error: 6790.0000\n",
      "abs_target_sum: 10118.0000\n",
      "abs_target_mean: 1.1848\n",
      "seasonal_error: 0.7838\n",
      "MASE: 1.3362\n",
      "MAPE: 0.7781\n",
      "sMAPE: 1.5539\n",
      "MSIS: 12.7885\n",
      "QuantileLoss[0.1]: 1871.0000\n",
      "Coverage[0.1]: 0.0050\n",
      "QuantileLoss[0.5]: 6790.0000\n",
      "Coverage[0.5]: 0.1432\n",
      "QuantileLoss[0.9]: 4674.2000\n",
      "Coverage[0.9]: 0.7123\n",
      "RMSE: 1.5220\n",
      "NRMSE: 1.2846\n",
      "ND: 0.6711\n",
      "wQuantileLoss[0.1]: 0.1849\n",
      "wQuantileLoss[0.5]: 0.6711\n",
      "wQuantileLoss[0.9]: 0.4620\n",
      "mean_absolute_QuantileLoss: 4445.0667\n",
      "mean_wQuantileLoss: 0.4393\n",
      "MAE_Coverage: 0.213\n"
     ]
    }
   ],
   "source": [
    "string = \"\"\n",
    "for key, value in agg_metrics.items():\n",
    "    if not np.isnan(value):\n",
    "        string += key + \": \" + f\"{value:.4f}\\n\"\n",
    "        \n",
    "print(string[:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eb414f5d-1229-4581-be1f-3d401b0b862a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08a1e803eb054f9f9155834957b30caa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sampled = (\n",
    "    df_train_eval.set_index(\"id\").loc[sampled_ids].reset_index()\n",
    ")\n",
    "df_train_sampled = df_sampled.loc[:, key_names + date_names[:-test_steps]]\n",
    "df_test_sampled = df_sampled.loc[:, date_names[-test_steps:]]\n",
    "\n",
    "wrmsse_evaluator = WRMSSEEvaluator(\n",
    "    df_train_sampled, df_test_sampled, calendar, selling_prices, test_steps\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2aae0735-1034-432e-aa67-92fab8069ea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DeepAR WRMSSE: 0.751280\n"
     ]
    }
   ],
   "source": [
    "predictions = [forecast.mean for forecast in forecasts]\n",
    "df_pred_sampled = pd.DataFrame(predictions, columns=df_test_sampled.columns)\n",
    "eval_metrics = calc_eval_metric(df_test_sampled, df_pred_sampled)\n",
    "\n",
    "wrmsse = wrmsse_evaluator.score(df_pred_sampled)\n",
    "\n",
    "print(f\"DeepAR WRMSSE: {wrmsse:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5b53b8ed-43b5-4419-9149-36cf741603ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_forecast(source, test_steps, plot_id=None, model_name=None, start_date=None):\n",
    "    if start_date is not None:\n",
    "        source = source[source[\"time\"] >= start_date]\n",
    "\n",
    "    points = (\n",
    "        alt.Chart(source)\n",
    "        .mark_circle(size=10.0, color=\"#000000\")\n",
    "        .encode(\n",
    "            x=alt.X(\"time:T\", axis=alt.Axis(title=\"Date\")),\n",
    "            y=alt.Y(\"y\", axis=alt.Axis(title=\"Demand\")),\n",
    "            tooltip=[\"time:T\", \"y:Q\"],\n",
    "        )\n",
    "    )\n",
    "\n",
    "    line = (\n",
    "        alt.Chart(source)\n",
    "        .mark_line(size=1.0, color=\"#4267B2\")\n",
    "        .encode(\n",
    "            x=\"time:T\",\n",
    "            y=\"fcst\",\n",
    "        )\n",
    "    )\n",
    "\n",
    "    band_90 = (\n",
    "        alt.Chart(source)\n",
    "        .mark_area(opacity=0.25, color=\"#4267B2\")\n",
    "        .encode(\n",
    "            x=\"time:T\",\n",
    "            y=\"fcst_lower_05\",\n",
    "            y2=\"fcst_upper_95\",\n",
    "        )\n",
    "    )\n",
    "\n",
    "    band_50 = (\n",
    "        alt.Chart(source)\n",
    "        .mark_area(opacity=0.5, color=\"#4267B2\")\n",
    "        .encode(\n",
    "            x=\"time:T\",\n",
    "            y=\"fcst_lower_25\",\n",
    "            y2=\"fcst_upper_75\",\n",
    "        )\n",
    "    )\n",
    "\n",
    "    rule = (\n",
    "        alt.Chart(source[[\"time\"]].iloc[-test_steps : -test_steps + 1])\n",
    "        .mark_rule(size=1.0, color=\"#FF0000\", strokeDash=[2, 2])\n",
    "        .encode(x=\"time:T\")\n",
    "    )\n",
    "\n",
    "    title = \"Demand Forecast\"\n",
    "    if plot_id is not None:\n",
    "        title += f\" for '{plot_id}'\"\n",
    "    if model_name is not None:\n",
    "        title = f\"{model_name}: \" + title\n",
    "\n",
    "    return (points + line + band_90 + band_50 + rule).properties(title=title, width=1000, height=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b0319af-1f7a-4617-ad8a-8ad635f8b92b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-63bc11c7821d4cf892176cf218c010e8\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-63bc11c7821d4cf892176cf218c010e8\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-63bc11c7821d4cf892176cf218c010e8\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"vconcat\": [{\"layer\": [{\"mark\": {\"type\": \"circle\", \"color\": \"#000000\", \"size\": 10.0}, \"encoding\": {\"tooltip\": [{\"type\": \"temporal\", \"field\": \"time\"}, {\"type\": \"quantitative\", \"field\": \"y\"}], \"x\": {\"type\": \"temporal\", \"axis\": {\"title\": \"Date\"}, \"field\": \"time\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Demand\"}, \"field\": \"y\"}}}, {\"mark\": {\"type\": \"line\", \"color\": \"#4267B2\", \"size\": 1.0}, \"encoding\": {\"x\": {\"type\": \"temporal\", \"field\": \"time\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"fcst\"}}}, {\"mark\": {\"type\": \"area\", \"color\": \"#4267B2\", \"opacity\": 0.25}, \"encoding\": {\"x\": {\"type\": \"temporal\", \"field\": \"time\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"fcst_lower_05\"}, \"y2\": {\"field\": \"fcst_upper_95\"}}}, {\"mark\": {\"type\": \"area\", \"color\": 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\"2016-03-05T00:00:00\", \"y\": 6, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-06T00:00:00\", \"y\": 7, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-07T00:00:00\", \"y\": 4, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-08T00:00:00\", \"y\": 4, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-09T00:00:00\", \"y\": 13, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-10T00:00:00\", \"y\": 0, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-11T00:00:00\", \"y\": 6, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-12T00:00:00\", \"y\": 1, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-13T00:00:00\", \"y\": 5, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-14T00:00:00\", \"y\": 6, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-15T00:00:00\", \"y\": 3, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-16T00:00:00\", \"y\": 6, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-17T00:00:00\", \"y\": 2, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-18T00:00:00\", \"y\": 12, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-19T00:00:00\", \"y\": 5, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-20T00:00:00\", \"y\": 2, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-21T00:00:00\", \"y\": 0, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-22T00:00:00\", \"y\": 3, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-23T00:00:00\", \"y\": 4, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-24T00:00:00\", \"y\": 1, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-25T00:00:00\", \"y\": 4, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-26T00:00:00\", \"y\": 6, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-27T00:00:00\", \"y\": 3, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-28T00:00:00\", \"y\": 3, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-29T00:00:00\", \"y\": 4, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-30T00:00:00\", \"y\": 1, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-03-31T00:00:00\", \"y\": 11, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-01T00:00:00\", \"y\": 6, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-02T00:00:00\", \"y\": 3, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-03T00:00:00\", \"y\": 15, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-04T00:00:00\", \"y\": 3, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-05T00:00:00\", \"y\": 0, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-06T00:00:00\", \"y\": 4, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-07T00:00:00\", \"y\": 5, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-08T00:00:00\", \"y\": 11, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-09T00:00:00\", \"y\": 9, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-10T00:00:00\", \"y\": 6, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-11T00:00:00\", \"y\": 6, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-12T00:00:00\", \"y\": 0, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-13T00:00:00\", \"y\": 3, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-14T00:00:00\", \"y\": 6, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-15T00:00:00\", \"y\": 7, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-16T00:00:00\", \"y\": 6, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-17T00:00:00\", \"y\": 7, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-18T00:00:00\", \"y\": 5, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-19T00:00:00\", \"y\": 14, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-20T00:00:00\", \"y\": 4, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-21T00:00:00\", \"y\": 5, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-22T00:00:00\", \"y\": 2, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-23T00:00:00\", \"y\": 4, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-24T00:00:00\", \"y\": 4, \"fcst\": null, \"fcst_lower_05\": null, \"fcst_lower_25\": null, \"fcst_upper_75\": null, \"fcst_upper_95\": null}, {\"time\": \"2016-04-25T00:00:00\", \"y\": 4, \"fcst\": 4.329999923706055, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 9.0}, {\"time\": \"2016-04-26T00:00:00\", \"y\": 4, \"fcst\": 3.9000000953674316, \"fcst_lower_05\": 0.0, \"fcst_lower_25\": 3.0, \"fcst_upper_75\": 5.0, \"fcst_upper_95\": 8.0}, {\"time\": \"2016-04-27T00:00:00\", \"y\": 1, \"fcst\": 4.010000228881836, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 5.0, \"fcst_upper_95\": 8.0}, {\"time\": \"2016-04-28T00:00:00\", \"y\": 4, \"fcst\": 4.760000228881836, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 3.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 10.0}, {\"time\": \"2016-04-29T00:00:00\", \"y\": 12, \"fcst\": 4.260000228881836, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 9.0}, {\"time\": \"2016-04-30T00:00:00\", \"y\": 7, \"fcst\": 6.139999866485596, \"fcst_lower_05\": 2.0, \"fcst_lower_25\": 4.0, \"fcst_upper_75\": 8.0, \"fcst_upper_95\": 12.0}, {\"time\": \"2016-05-01T00:00:00\", \"y\": 3, \"fcst\": 7.389999866485596, \"fcst_lower_05\": 2.0, \"fcst_lower_25\": 4.0, \"fcst_upper_75\": 10.0, \"fcst_upper_95\": 14.0}, {\"time\": \"2016-05-02T00:00:00\", \"y\": 5, \"fcst\": 5.260000228881836, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 3.0, \"fcst_upper_75\": 7.0, \"fcst_upper_95\": 12.0}, {\"time\": \"2016-05-03T00:00:00\", \"y\": 5, \"fcst\": 4.289999961853027, \"fcst_lower_05\": 0.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 10.0}, {\"time\": \"2016-05-04T00:00:00\", \"y\": 2, \"fcst\": 4.099999904632568, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 9.0}, {\"time\": \"2016-05-05T00:00:00\", \"y\": 3, \"fcst\": 4.960000038146973, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 7.0, \"fcst_upper_95\": 10.0}, {\"time\": \"2016-05-06T00:00:00\", \"y\": 5, \"fcst\": 5.179999828338623, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 3.0, \"fcst_upper_75\": 7.0, \"fcst_upper_95\": 11.0}, {\"time\": \"2016-05-07T00:00:00\", \"y\": 8, \"fcst\": 6.239999771118164, \"fcst_lower_05\": 2.0, \"fcst_lower_25\": 4.0, \"fcst_upper_75\": 8.0, \"fcst_upper_95\": 14.0}, {\"time\": \"2016-05-08T00:00:00\", \"y\": 6, \"fcst\": 5.010000228881836, \"fcst_lower_05\": 0.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 7.0, \"fcst_upper_95\": 11.0}, {\"time\": \"2016-05-09T00:00:00\", \"y\": 4, \"fcst\": 4.679999828338623, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 3.0, \"fcst_upper_75\": 7.0, \"fcst_upper_95\": 10.0}, {\"time\": \"2016-05-10T00:00:00\", \"y\": 3, \"fcst\": 4.329999923706055, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 10.0}, {\"time\": \"2016-05-11T00:00:00\", \"y\": 5, \"fcst\": 3.819999933242798, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 5.0, \"fcst_upper_95\": 8.0}, {\"time\": \"2016-05-12T00:00:00\", \"y\": 5, \"fcst\": 3.7699999809265137, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 5.0, \"fcst_upper_95\": 8.0}, {\"time\": \"2016-05-13T00:00:00\", \"y\": 4, \"fcst\": 4.420000076293945, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 9.0}, {\"time\": \"2016-05-14T00:00:00\", \"y\": 10, \"fcst\": 4.730000019073486, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 3.0, \"fcst_upper_75\": 7.0, \"fcst_upper_95\": 10.0}, {\"time\": \"2016-05-15T00:00:00\", \"y\": 6, \"fcst\": 5.420000076293945, \"fcst_lower_05\": 0.0, \"fcst_lower_25\": 4.0, \"fcst_upper_75\": 7.0, \"fcst_upper_95\": 11.0}, {\"time\": \"2016-05-16T00:00:00\", \"y\": 3, \"fcst\": 4.309999942779541, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 10.0}, {\"time\": \"2016-05-17T00:00:00\", \"y\": 5, \"fcst\": 4.199999809265137, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 9.0}, {\"time\": \"2016-05-18T00:00:00\", \"y\": 3, \"fcst\": 3.9200000762939453, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 5.0, \"fcst_upper_95\": 8.0}, {\"time\": \"2016-05-19T00:00:00\", \"y\": 6, \"fcst\": 4.070000171661377, \"fcst_lower_05\": 0.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 8.0}, {\"time\": \"2016-05-20T00:00:00\", \"y\": 4, \"fcst\": 3.9100000858306885, \"fcst_lower_05\": 0.0, \"fcst_lower_25\": 2.0, \"fcst_upper_75\": 5.0, \"fcst_upper_95\": 9.0}, {\"time\": \"2016-05-21T00:00:00\", \"y\": 7, \"fcst\": 4.739999771118164, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 3.0, \"fcst_upper_75\": 6.0, \"fcst_upper_95\": 10.0}, {\"time\": \"2016-05-22T00:00:00\", \"y\": 2, \"fcst\": 5.559999942779541, \"fcst_lower_05\": 1.0, \"fcst_lower_25\": 3.0, \"fcst_upper_75\": 8.0, \"fcst_upper_95\": 10.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_perf_indices = eval_metrics[\"smape\"].dropna().sort_values()[:3].index\n",
    "plots = []\n",
    "\n",
    "for index in best_perf_indices:\n",
    "    plot_id = pd.Series(sampled_ids).iloc[index]\n",
    "\n",
    "    y = target[[plot_id]].reset_index()\n",
    "    y.columns = [\"time\", \"y\"]\n",
    "    y[\"time\"] = pd.to_datetime(y[\"time\"])\n",
    "\n",
    "    forecast = pd.DataFrame(\n",
    "        [forecasts[index].mean]\n",
    "        + [forecasts[index].quantile(p) for p in [0.05, 0.25, 0.75, 0.95]],\n",
    "        columns=forecasts[index].index,\n",
    "    ).T.reset_index()\n",
    "    forecast.columns = [\n",
    "        \"time\",\n",
    "        \"fcst\",\n",
    "        \"fcst_lower_05\",\n",
    "        \"fcst_lower_25\",\n",
    "        \"fcst_upper_75\",\n",
    "        \"fcst_upper_95\",\n",
    "    ]\n",
    "\n",
    "    source = y.merge(forecast, how=\"left\")\n",
    "    p = plot_forecast(\n",
    "        source, test_steps, plot_id=plot_id, model_name=\"DeepAR\", start_date=\"2015-05-23\"\n",
    "    )\n",
    "    \n",
    "    plots.append(p)\n",
    "    \n",
    "alt.VConcatChart(vconcat=plots)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4426be8f-ef22-46aa-95b3-4c5ee243b659",
   "metadata": {},
   "source": [
    "# DeepVAR: Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b36b98a3-ba02-42bb-8460-87802361d8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_grouper = MultivariateGrouper(max_target_dim=num_series)\n",
    "train_dataset = train_grouper(train_dataset)\n",
    "\n",
    "test_grouper = MultivariateGrouper(max_target_dim=num_series)\n",
    "test_dataset = test_grouper(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8f616055-ff46-413f-b267-e61af09bbb9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Trainer(\n",
    "    ctx=device,\n",
    "    epochs=200,\n",
    "    num_batches_per_epoch=50,\n",
    "    learning_rate=0.001,\n",
    "    hybridize=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f32c52db-6eb8-43ae-b862-24704543728e",
   "metadata": {},
   "outputs": [],
   "source": [
    "deepvar_estimator = DeepVAREstimator(\n",
    "    freq=\"D\", \n",
    "    prediction_length=test_steps,\n",
    "    target_dim=num_series,\n",
    "    trainer=trainer,\n",
    "    context_length=2 * test_steps,\n",
    "    num_layers=2,\n",
    "    num_cells=40,\n",
    "    cell_type=\"lstm\",\n",
    "    dropout_rate=0.2,\n",
    "    cardinality=cardinality,\n",
    "    distr_output=LowrankMultivariateGaussianOutput(dim=num_series, rank=5),\n",
    "    batch_size=30,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "508b550c-5463-4eaa-9456-efc49f1b7dba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:30<00:00,  1.63it/s, epoch=1/200, avg_epoch_loss=1.2e+17] \n",
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      "WARNING:root:Serializing RepresentableBlockPredictor instances does not save the prediction network structure in a backwards-compatible manner. Be careful not to use this method in production.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping based on learning rate scheduler callback (min_lr was reached).\n",
      "CPU times: user 36min 51s, sys: 59.2 s, total: 37min 50s\n",
      "Wall time: 37min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "deepvar_predictor = deepvar_estimator.train(train_dataset)\n",
    "\n",
    "os.makedirs(os.path.join(MODEL_PATH, \"deepvar\"), exist_ok=True)\n",
    "deepvar_predictor.serialize(Path(os.path.join(MODEL_PATH, \"deepvar\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c94554-75b9-495b-ad1e-1498009a1f56",
   "metadata": {},
   "source": [
    "# DeepVAR: Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0b0cad13-e71f-4d9e-a09b-2548565ee8a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "deepvar_predictor = Predictor.deserialize(Path(os.path.join(MODEL_PATH, \"deepvar\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cc5b9267-049b-4b3b-9023-aae28bcbec42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 411 ms, sys: 90.7 ms, total: 502 ms\n",
      "Wall time: 384 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "forecast_iter, ts_iter = make_evaluation_predictions( \n",
    "    dataset=test_dataset,\n",
    "    predictor=deepvar_predictor, \n",
    "    num_samples=100,\n",
    ") \n",
    "forecasts = list(forecast_iter)\n",
    "tss = list(ts_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b231b76d-97ce-4418-8532-65e3b56b5c59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running evaluation: 1it [00:00, 91.08it/s]\n"
     ]
    }
   ],
   "source": [
    "evaluator = MultivariateEvaluator(quantiles=[0.1, 0.5, 0.9])\n",
    "agg_metrics = evaluator.calculate_aggregate_multivariate_metrics(\n",
    "    iter(tss), iter(forecasts), np.mean\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "96be2e24-589c-4bac-bf44-c697f8b89753",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 3.0940\n",
      "abs_error: 48.5044\n",
      "abs_target_sum: 33.1738\n",
      "abs_target_mean: 1.1848\n",
      "seasonal_error: 0.1384\n",
      "MASE: 12.5198\n",
      "MAPE: 1.4848\n",
      "sMAPE: 2.0000\n",
      "MSIS: 113.5319\n",
      "QuantileLoss[0.1]: 14.8709\n",
      "Coverage[0.1]: 0.0000\n",
      "QuantileLoss[0.5]: 48.5044\n",
      "Coverage[0.5]: 0.0000\n",
      "QuantileLoss[0.9]: 42.2355\n",
      "Coverage[0.9]: 0.0000\n",
      "RMSE: 1.7590\n",
      "NRMSE: 1.4847\n",
      "ND: 1.4621\n",
      "wQuantileLoss[0.1]: 0.4483\n",
      "wQuantileLoss[0.5]: 1.4621\n",
      "wQuantileLoss[0.9]: 1.2732\n",
      "mean_absolute_QuantileLoss: 35.2036\n",
      "mean_wQuantileLoss: 1.0612\n",
      "MAE_Coverage: 0.500\n"
     ]
    }
   ],
   "source": [
    "string = \"\"\n",
    "for key, value in agg_metrics.items():\n",
    "    if not np.isnan(value):\n",
    "        string += key + \": \" + f\"{value:.4f}\\n\"\n",
    "        \n",
    "print(string[:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c565b0ec-521f-4bb7-a6b1-43dfa271da59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DeepVAR WRMSSE: 4.693253\n"
     ]
    }
   ],
   "source": [
    "predictions = [forecast.mean for forecast in forecasts]\n",
    "df_pred_sampled = pd.DataFrame(predictions[0].T, columns=df_test_sampled.columns)\n",
    "eval_metrics = calc_eval_metric(df_test_sampled, df_pred_sampled)\n",
    "\n",
    "wrmsse = wrmsse_evaluator.score(df_pred_sampled)\n",
    "\n",
    "print(f\"DeepVAR WRMSSE: {wrmsse:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "06edae37-8a0c-4dcc-a505-d665cf4440a0",
   "metadata": {},
   "outputs": [
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\"fcst_upper_95\": 26.325298309326172}, {\"time\": \"2016-05-13T00:00:00\", \"y\": 5, \"fcst\": 2.452754020690918, \"fcst_lower_05\": -12.432278633117676, \"fcst_lower_25\": -5.647396564483643, \"fcst_upper_75\": 8.561280250549316, \"fcst_upper_95\": 23.261463165283203}, {\"time\": \"2016-05-14T00:00:00\", \"y\": 8, \"fcst\": 3.0235700607299805, \"fcst_lower_05\": -16.04867935180664, \"fcst_lower_25\": -4.104671001434326, \"fcst_upper_75\": 10.164166450500488, \"fcst_upper_95\": 21.509618759155273}, {\"time\": \"2016-05-15T00:00:00\", \"y\": 8, \"fcst\": 2.1172842979431152, \"fcst_lower_05\": -13.629556655883789, \"fcst_lower_25\": -4.583441257476807, \"fcst_upper_75\": 8.68236255645752, \"fcst_upper_95\": 19.46981430053711}, {\"time\": \"2016-05-16T00:00:00\", \"y\": 1, \"fcst\": 2.468151330947876, \"fcst_lower_05\": -14.702634811401367, \"fcst_lower_25\": -4.431719779968262, \"fcst_upper_75\": 10.910258293151855, \"fcst_upper_95\": 21.440736770629883}, {\"time\": \"2016-05-17T00:00:00\", \"y\": 0, \"fcst\": 3.1642026901245117, \"fcst_lower_05\": -14.0421781539917, \"fcst_lower_25\": -2.682413101196289, \"fcst_upper_75\": 9.97868824005127, \"fcst_upper_95\": 17.420730590820312}, {\"time\": \"2016-05-18T00:00:00\", \"y\": 5, \"fcst\": 3.3373782634735107, \"fcst_lower_05\": -14.235172271728516, \"fcst_lower_25\": -2.3331961631774902, \"fcst_upper_75\": 10.389640808105469, \"fcst_upper_95\": 19.729965209960938}, {\"time\": \"2016-05-19T00:00:00\", \"y\": 6, \"fcst\": 1.056272268295288, \"fcst_lower_05\": -17.71896743774414, \"fcst_lower_25\": -7.490850448608398, \"fcst_upper_75\": 9.672163009643555, \"fcst_upper_95\": 17.248910903930664}, {\"time\": \"2016-05-20T00:00:00\", \"y\": 9, \"fcst\": 2.9698896408081055, \"fcst_lower_05\": -14.822786331176758, \"fcst_lower_25\": -4.616722583770752, \"fcst_upper_75\": 12.227141380310059, \"fcst_upper_95\": 22.220603942871094}, {\"time\": \"2016-05-21T00:00:00\", \"y\": 10, \"fcst\": 4.630611896514893, \"fcst_lower_05\": -14.496088981628418, \"fcst_lower_25\": -1.9896211624145508, \"fcst_upper_75\": 12.113494873046875, \"fcst_upper_95\": 21.995025634765625}, {\"time\": \"2016-05-22T00:00:00\", \"y\": 8, \"fcst\": 4.977942943572998, \"fcst_lower_05\": -14.53730583190918, \"fcst_lower_25\": -2.3482112884521484, \"fcst_upper_75\": 13.191612243652344, \"fcst_upper_95\": 20.7875919342041}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_perf_indices = eval_metrics[\"smape\"].dropna().sort_values()[:3].index\n",
    "plots = []\n",
    "\n",
    "for index in best_perf_indices:\n",
    "    plot_id = pd.Series(sampled_ids).iloc[index]\n",
    "\n",
    "    y = target[[plot_id]].reset_index()\n",
    "    y.columns = [\"time\", \"y\"]\n",
    "    y[\"time\"] = pd.to_datetime(y[\"time\"])\n",
    "\n",
    "    forecast = pd.DataFrame(\n",
    "        [forecasts[0].mean[:, index][-test_steps:]]\n",
    "        + [forecasts[0].quantile(p)[:, index][-test_steps:] for p in [0.05, 0.25, 0.75, 0.95]],\n",
    "        columns=forecasts[0].index,\n",
    "    ).T.reset_index()\n",
    "    forecast.columns = [\n",
    "        \"time\",\n",
    "        \"fcst\",\n",
    "        \"fcst_lower_05\",\n",
    "        \"fcst_lower_25\",\n",
    "        \"fcst_upper_75\",\n",
    "        \"fcst_upper_95\",\n",
    "    ]\n",
    "\n",
    "    source = y.merge(forecast, how=\"left\")\n",
    "    p = plot_forecast(\n",
    "        source, test_steps, plot_id=plot_id, model_name=\"DeepVAR\", start_date=\"2015-05-23\"\n",
    "    )\n",
    "    \n",
    "    plots.append(p)\n",
    "    \n",
    "alt.VConcatChart(vconcat=plots)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3decfb94-68e3-43a0-9fa9-61522595226f",
   "metadata": {},
   "source": [
    "# DeepAR: Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5488e7ec-c421-4ebe-86ec-0d64f9880fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\"event_name_1\", \"event_type_1\", \"event_name_2\", \"event_type_2\"]\n",
    "events = calendar[[\"date\"] + feature_names].fillna(\"NA\")\n",
    "train = events[events[\"date\"] < dates[-test_steps]][feature_names]\n",
    "\n",
    "encoder = HashingEncoder(drop_invariant=True)\n",
    "_ = encoder.fit(train)\n",
    "encoded = encoder.transform(events[feature_names])\n",
    "events = pd.concat([events[[\"date\"]], encoded], axis=1)\n",
    "\n",
    "feat_dynamic_cat = events.merge(time_related).set_index(\"date\")\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaled = scaler.fit_transform(feat_dynamic_cat)\n",
    "feat_dynamic_cat = pd.DataFrame(\n",
    "    scaled, columns=feat_dynamic_cat.columns, index=feat_dynamic_cat.index\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d4599568-2a70-41a8-9578-28dd87ab7bfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = (\n",
    "    df_train_eval[[\"id\", \"store_id\", \"item_id\"]]\n",
    "    .merge(selling_prices, how=\"left\")\n",
    "    .drop([\"store_id\", \"item_id\"], axis=1)\n",
    ")\n",
    "week_to_date = calendar[[\"date\", \"wm_yr_wk\"]].drop_duplicates()\n",
    "prices = week_to_date.merge(prices, how=\"left\").drop(\n",
    "    [\"wm_yr_wk\"], axis=1\n",
    ")\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "train = prices[prices[\"date\"] < dates[-test_steps]][[\"sell_price\"]]\n",
    "\n",
    "_ = scaler.fit(train)\n",
    "prices[\"sell_price\"] = scaler.transform(prices[[\"sell_price\"]])\n",
    "prices = prices.pivot(index=\"date\", columns=\"id\", values=\"sell_price\")\n",
    "prices = prices.fillna(method=\"bfill\")\n",
    "\n",
    "feat_dynamic_real = pd.concat([prices, snap], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "638d0ca7-6209-4301-8eb1-f11c7803eb66",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_list = []\n",
    "for each_id in all_ids:\n",
    "    dict_by_id = {\n",
    "        FieldName.TARGET: target[each_id].values,\n",
    "        FieldName.START: target.index[0],\n",
    "        FieldName.FEAT_DYNAMIC_REAL: split_into_n_array(\n",
    "            feat_dynamic_cat.iloc[:-test_steps],\n",
    "            n_feat_dynamic_cat,\n",
    "        )\n",
    "        + split_into_n_array(\n",
    "            feat_dynamic_real[each_id].iloc[:-test_steps],\n",
    "            n_feat_dynamic_real,\n",
    "        ),\n",
    "        FieldName.FEAT_STATIC_CAT: feat_static_cat[each_id].values,\n",
    "    }\n",
    "    train_list.append(dict_by_id)\n",
    "\n",
    "test_list = []\n",
    "for each_id in all_ids:\n",
    "    dict_by_id = {\n",
    "        FieldName.TARGET: np.append(\n",
    "            target[each_id].values, np.repeat(np.nan, test_steps)\n",
    "        ),\n",
    "        FieldName.START: target.index[0],\n",
    "        FieldName.FEAT_DYNAMIC_REAL: split_into_n_array(\n",
    "            feat_dynamic_cat,\n",
    "            n_feat_dynamic_cat,\n",
    "        )\n",
    "        + split_into_n_array(\n",
    "            feat_dynamic_real[each_id],\n",
    "            n_feat_dynamic_real,\n",
    "        ),\n",
    "        FieldName.FEAT_STATIC_CAT: feat_static_cat[each_id].values,\n",
    "    }\n",
    "    test_list.append(dict_by_id)\n",
    "\n",
    "train_dataset = ListDataset(train_list, freq=\"D\")\n",
    "test_dataset = ListDataset(test_list, freq=\"D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e2d6b7a-4c6c-47e1-bd5f-7b8ca1b0aba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Trainer(\n",
    "    ctx=device,\n",
    "    epochs=200,\n",
    "    num_batches_per_epoch=50,\n",
    "    learning_rate=0.001,\n",
    "    hybridize=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "11339b98-262e-4575-883a-a83e0d5e69cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "deepar_estimator = DeepAREstimator(\n",
    "    freq=\"D\", \n",
    "    prediction_length=test_steps,\n",
    "    trainer=trainer,\n",
    "    context_length=2 * test_steps,\n",
    "    num_layers=2,\n",
    "    num_cells=40,\n",
    "    cell_type=\"lstm\",\n",
    "    dropout_rate=0.2,\n",
    "    use_feat_dynamic_real=True,\n",
    "    use_feat_static_cat=True,\n",
    "    use_feat_static_real=False,\n",
    "    cardinality=cardinality,\n",
    "    distr_output=NegativeBinomialOutput(),  \n",
    "    batch_size=30,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "37a3930c-7a7a-4bec-8efb-90a97758924c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping based on learning rate scheduler callback (min_lr was reached).\n",
      "CPU times: user 7min 42s, sys: 17 s, total: 7min 59s\n",
      "Wall time: 7min 46s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "deepar_predictor = deepar_estimator.train(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "12c2d95d-6766-4a24-98f0-cf4b1e217680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15min 47s, sys: 2min 17s, total: 18min 5s\n",
      "Wall time: 15min 57s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "forecast_iter, ts_iter = make_evaluation_predictions( \n",
    "    dataset=test_dataset,\n",
    "    predictor=deepar_predictor, \n",
    "    num_samples=100,\n",
    ") \n",
    "forecasts = list(forecast_iter)\n",
    "tss = list(ts_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8c9cc279-f6f8-45f3-8c43-4adfa734f17c",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\"F\" + str(i) for i in range(1, 29)]\n",
    "valid_submission = df_train_eval.loc[\n",
    "    :, [\"id\"] + date_names[-test_steps:]\n",
    "]\n",
    "valid_submission.columns = [\"id\"] + column_names\n",
    "valid_submission[\"id\"] = valid_submission[\"id\"].str.replace(\"evaluation\", \"validation\")\n",
    "eval_submission = pd.DataFrame(\n",
    "    [forecast.mean for forecast in forecasts],\n",
    "    columns=column_names,\n",
    "    index=all_ids,\n",
    ").reset_index()\n",
    "eval_submission.columns = [\"id\"] + eval_submission.columns[1:].tolist()\n",
    "submission = pd.concat([valid_submission, eval_submission])\n",
    "submission.to_csv(\"submission.csv\", index=False)"
   ]
  }
 ],
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