Time series transformer tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86ad0e30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tutorial url\n",
    "# https://huggingface.co/blog/time-series-transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4357ee0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7009beb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset monash_tsf (C:/Users/yozhan/.cache/huggingface/datasets/monash_tsf/tourism_monthly/1.0.0/fc869f3ae1577c9def2a919ab1dd0c3d4a7a44826b8e0e8fa423bb0161b629e2)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "553772b851a041aeb196651882da5fbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = load_dataset(\"monash_tsf\", \"tourism_monthly\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "27005a68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'])\n"
     ]
    }
   ],
   "source": [
    "train_example = dataset[\"train\"][1]\n",
    "print(train_example.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9415bae0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1979-01\n",
      "[65072.37109375, 48612.19921875, 58452.58984375, 57033.96875, 71498.953125, 79187.109375, 101896.1015625, 115971.796875, 94962.1484375, 80648.328125, 64196.078125, 50364.859375, 57624.05859375, 47163.87109375, 48874.0703125, 62737.609375, 69621.1328125, 71454.21875, 107916.796875, 120461.5, 99441.1796875, 84936.5390625, 62809.51953125, 54028.48046875, 58605.91015625, 50516.33984375, 55711.5390625, 55798.41015625, 65033.1796875, 89421.140625, 119027.8984375, 133411.296875, 112890.703125, 96718.140625, 76462.796875, 57951.6796875, 62094.69140625, 55118.23046875, 66128.3515625, 71334.2578125, 75644.71875, 98380.4296875, 127255.0, 146442.703125, 121934.796875, 88537.546875, 71126.1484375, 80209.5078125, 72614.40625, 64114.46875, 70382.5, 77124.5390625, 85675.6796875, 110282.1015625, 135361.296875, 165238.90625, 139575.0, 113179.796875, 92898.84375, 69113.3984375, 74665.15625, 70439.328125, 72906.4609375, 79968.9609375, 114617.0, 123295.703125, 158611.703125, 187202.40625, 145933.703125, 142136.0, 104437.6015625, 77719.5390625, 87537.671875, 74318.6484375, 78690.7421875, 93133.796875, 107493.0, 115749.5, 170180.0, 182643.703125, 150198.40625, 144580.09375, 88946.34375, 79351.4609375, 93303.1171875, 83155.078125, 84729.3984375, 118132.703125, 109726.8984375, 136382.296875, 184945.203125, 183298.09375, 182025.203125, 154070.09375, 107512.6015625, 95252.90625, 100485.796875, 85406.546875, 99865.203125, 104420.0, 131736.796875, 151818.703125, 204872.0, 200877.90625, 172959.40625, 143267.203125, 100896.703125, 91154.3671875, 100889.296875, 105025.5, 103881.0, 117237.1015625, 125309.8984375, 145201.703125, 175963.296875, 220512.40625, 179558.0, 145964.90625, 109480.0, 88714.75, 104875.703125, 92488.0, 103057.296875, 105123.703125, 130371.296875, 139400.0, 162441.09375, 198709.203125, 154935.703125, 151893.296875, 76786.90625, 79548.8984375, 109728.203125, 88898.578125, 82404.8984375, 101708.703125, 112285.3984375, 106262.3984375, 155264.0]\n"
     ]
    }
   ],
   "source": [
    "print(train_example[\"start\"])\n",
    "print(train_example[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7c5d086c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_example = dataset['validation'][1]\n",
    "validation_example.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dad75154",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1979-01-01 00:00:00\n",
      "[65072.37109375, 48612.19921875, 58452.58984375, 57033.96875, 71498.953125, 79187.109375, 101896.1015625, 115971.796875, 94962.1484375, 80648.328125, 64196.078125, 50364.859375, 57624.05859375, 47163.87109375, 48874.0703125, 62737.609375, 69621.1328125, 71454.21875, 107916.796875, 120461.5, 99441.1796875, 84936.5390625, 62809.51953125, 54028.48046875, 58605.91015625, 50516.33984375, 55711.5390625, 55798.41015625, 65033.1796875, 89421.140625, 119027.8984375, 133411.296875, 112890.703125, 96718.140625, 76462.796875, 57951.6796875, 62094.69140625, 55118.23046875, 66128.3515625, 71334.2578125, 75644.71875, 98380.4296875, 127255.0, 146442.703125, 121934.796875, 88537.546875, 71126.1484375, 80209.5078125, 72614.40625, 64114.46875, 70382.5, 77124.5390625, 85675.6796875, 110282.1015625, 135361.296875, 165238.90625, 139575.0, 113179.796875, 92898.84375, 69113.3984375, 74665.15625, 70439.328125, 72906.4609375, 79968.9609375, 114617.0, 123295.703125, 158611.703125, 187202.40625, 145933.703125, 142136.0, 104437.6015625, 77719.5390625, 87537.671875, 74318.6484375, 78690.7421875, 93133.796875, 107493.0, 115749.5, 170180.0, 182643.703125, 150198.40625, 144580.09375, 88946.34375, 79351.4609375, 93303.1171875, 83155.078125, 84729.3984375, 118132.703125, 109726.8984375, 136382.296875, 184945.203125, 183298.09375, 182025.203125, 154070.09375, 107512.6015625, 95252.90625, 100485.796875, 85406.546875, 99865.203125, 104420.0, 131736.796875, 151818.703125, 204872.0, 200877.90625, 172959.40625, 143267.203125, 100896.703125, 91154.3671875, 100889.296875, 105025.5, 103881.0, 117237.1015625, 125309.8984375, 145201.703125, 175963.296875, 220512.40625, 179558.0, 145964.90625, 109480.0, 88714.75, 104875.703125, 92488.0, 103057.296875, 105123.703125, 130371.296875, 139400.0, 162441.09375, 198709.203125, 154935.703125, 151893.296875, 76786.90625, 79548.8984375, 109728.203125, 88898.578125, 82404.8984375, 101708.703125, 112285.3984375, 106262.3984375, 155264.0, 170488.703125, 132573.703125, 132368.203125, 102293.8984375, 77060.4609375, 105801.703125, 70909.859375, 71229.46875, 99842.4375, 106268.8984375, 102494.3984375, 163841.796875, 177779.296875, 146222.296875, 143864.203125, 95231.2890625, 87472.6875, 98428.953125, 80790.8984375, 90682.2109375, 97428.0078125, 115606.6015625, 130511.796875, 175490.203125]\n"
     ]
    }
   ],
   "source": [
    "print(validation_example['start'])\n",
    "print(validation_example['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df44a30f",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = \"1M\"\n",
    "prediction_length = 24\n",
    "\n",
    "assert len(train_example[\"target\"]) + prediction_length == len(\n",
    "    validation_example[\"target\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef6e21e5",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "figure, axes = plt.subplots()\n",
    "axes.plot(train_example[\"target\"], color=\"blue\")\n",
    "axes.plot(validation_example[\"target\"], color=\"red\", alpha=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e02e6de",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = dataset[\"train\"]\n",
    "test_dataset = dataset[\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "030b3d23",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lru_cache is a decorator for some recursive calculation\n",
    "# the values will stored in cache so that cached values will be used in the future if repeating computation happens\n",
    "\n",
    "from functools import lru_cache\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "@lru_cache(10_000)\n",
    "def convert_to_pandas_period(date, freq):\n",
    "    return pd.Period(date, freq)\n",
    "\n",
    "def transform_start_field(batch, freq):\n",
    "    batch[\"start\"] = [convert_to_pandas_period(date, freq) for date in batch[\"start\"]]\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "965bdb44",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "train_dataset.set_transform(partial(transform_start_field, freq=freq))\n",
    "test_dataset.set_transform(partial(transform_start_field, freq=freq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f162ef59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'We specify a couple of additional parameters to the model:\\n\\nprediction_length (in our case, 24 months): this is the horizon that the decoder of the Transformer will learn to predict for;\\ncontext_length: the model will set the context_length (input of the encoder) \\n                equal to the prediction_length, if no context_length is specified;\\n                \\nlags for a given frequency: these specify how much we \"look back\", to be added as \\n                            additional features. e.g. for a Daily frequency we might consider \\n                            a look back of [1, 2, 7, 30, ...] or in other words look back 1, 2, ... \\n                            days while for Minute data we might consider [1, 30, 60, 60*24, ...] etc.;\\n                            \\nthe number of time features: in our case, this will be 2 as we\\'ll add MonthOfYear and Age features;\\n\\nthe number of static categorical features: in our case, this will be just 1 as we\\'ll add a single \"time series ID\" feature;\\n\\nthe cardinality: the number of values of each static categorical feature, \\n                as a list which for our case will be [366] as we have 366 different time series\\n                \\nthe embedding dimension: the embedding dimension for each static categorical feature, \\n                        as a list, for example [3] means the model will learn an embedding \\n                        vector of size 3 for each of the 366 time series (regions). '"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"We specify a couple of additional parameters to the model:\n",
    "\n",
    "prediction_length (in our case, 24 months): this is the horizon that the decoder of the Transformer will learn to predict for;\n",
    "context_length: the model will set the context_length (input of the encoder) \n",
    "                equal to the prediction_length, if no context_length is specified;\n",
    "                \n",
    "lags for a given frequency: these specify how much we \"look back\", to be added as \n",
    "                            additional features. e.g. for a Daily frequency we might consider \n",
    "                            a look back of [1, 2, 7, 30, ...] or in other words look back 1, 2, ... \n",
    "                            days while for Minute data we might consider [1, 30, 60, 60*24, ...] etc.;\n",
    "                            \n",
    "the number of time features: in our case, this will be 2 as we'll add MonthOfYear and Age features;\n",
    "\n",
    "the number of static categorical features: in our case, this will be just 1 as we'll add a single \"time series ID\" feature;\n",
    "\n",
    "the cardinality: the number of values of each static categorical feature, \n",
    "                as a list which for our case will be [366] as we have 366 different time series\n",
    "                \n",
    "the embedding dimension: the embedding dimension for each static categorical feature, \n",
    "                        as a list, for example [3] means the model will learn an embedding \n",
    "                        vector of size 3 for each of the 366 time series (regions). \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "09f1f194",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 23, 24, 25, 35, 36, 37]\n"
     ]
    }
   ],
   "source": [
    "from gluonts.time_feature import get_lags_for_frequency\n",
    "\n",
    "lags_sequence = get_lags_for_frequency(freq)\n",
    "print(lags_sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ccd2aa3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<function month_of_year at 0x000002DFA39B55A0>]\n"
     ]
    }
   ],
   "source": [
    "from gluonts.time_feature import time_features_from_frequency_str\n",
    "\n",
    "time_features = time_features_from_frequency_str(freq)\n",
    "print(time_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c84c2203",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import TimeSeriesTransformerConfig, TimeSeriesTransformerForPrediction\n",
    "\n",
    "config = TimeSeriesTransformerConfig(\n",
    "    prediction_length=prediction_length,\n",
    "    # context length:\n",
    "    context_length=prediction_length * 2,\n",
    "    # lags coming from helper given the freq:\n",
    "    lags_sequence=lags_sequence,\n",
    "    # we'll add 2 time features (\"month of year\" and \"age\", see further):\n",
    "    num_time_features=len(time_features) + 1,\n",
    "    # we have a single static categorical feature, namely time series ID:\n",
    "    num_static_categorical_features=1,\n",
    "    # it has 366 possible values:\n",
    "    cardinality=[len(train_dataset)],\n",
    "    # the model will learn an embedding of size 2 for each of the 366 possible values:\n",
    "    embedding_dimension=[2],\n",
    "    \n",
    "    # transformer params:\n",
    "    encoder_layers=4,\n",
    "    decoder_layers=4,\n",
    "    d_model=32,\n",
    "        \n",
    ")\n",
    "\n",
    "model = TimeSeriesTransformerForPrediction(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "76c9c724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\note that, similar to other models in the 🤗 Transformers library, \\nTimeSeriesTransformerModel corresponds to the encoder-decoder Transformer without any head on top, \\nand TimeSeriesTransformerForPrediction corresponds to TimeSeriesTransformerModel with a distribution head on top. \\nBy default, the model uses a Student-t distribution (but this is configurable):\\n'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Note that, similar to other models in the 🤗 Transformers library, \n",
    "TimeSeriesTransformerModel corresponds to the encoder-decoder Transformer without any head on top, \n",
    "and TimeSeriesTransformerForPrediction corresponds to TimeSeriesTransformerModel with a distribution head on top. \n",
    "By default, the model uses a Student-t distribution (but this is configurable):\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e97434e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'student_t'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.config.distribution_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "83e665f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gluonts.time_feature import (\n",
    "    time_features_from_frequency_str,\n",
    "    TimeFeature,\n",
    "    get_lags_for_frequency,\n",
    ")\n",
    "from gluonts.dataset.field_names import FieldName\n",
    "from gluonts.transform import (\n",
    "    AddAgeFeature,\n",
    "    AddObservedValuesIndicator,\n",
    "    AddTimeFeatures,\n",
    "    AsNumpyArray,\n",
    "    Chain,\n",
    "    ExpectedNumInstanceSampler,\n",
    "    InstanceSplitter,\n",
    "    RemoveFields,\n",
    "    SelectFields,\n",
    "    SetField,\n",
    "    TestSplitSampler,\n",
    "    Transformation,\n",
    "    ValidationSplitSampler,\n",
    "    VstackFeatures,\n",
    "    RenameFields,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3d6ce89",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import PretrainedConfig\n",
    "\n",
    "def create_transformation(freq: str, config: PretrainedConfig) -> Transformation:\n",
    "    remove_field_names = []\n",
    "    if config.num_static_real_features == 0:\n",
    "        remove_field_names.append(FieldName.FEAT_STATIC_REAL)\n",
    "    if config.num_dynamic_real_features == 0:\n",
    "        remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)\n",
    "    if config.num_static_categorical_features == 0:\n",
    "        remove_field_names.append(FieldName.FEAT_STATIC_CAT)\n",
    "    print(remove_field_names)\n",
    "\n",
    "    # a bit like torchvision.transforms.Compose\n",
    "    return Chain(\n",
    "        # step 1: remove static/dynamic fields if not specified\n",
    "        [RemoveFields(field_names=remove_field_names)]\n",
    "        # step 2: convert the data to NumPy (potentially not needed)\n",
    "        + (\n",
    "            [\n",
    "                AsNumpyArray(\n",
    "                    field=FieldName.FEAT_STATIC_CAT,\n",
    "                    expected_ndim=1,\n",
    "                    dtype=int,\n",
    "                )\n",
    "            ]\n",
    "            if config.num_static_categorical_features > 0\n",
    "            else []\n",
    "        )\n",
    "        + (\n",
    "            [\n",
    "                AsNumpyArray(\n",
    "                    field=FieldName.FEAT_STATIC_REAL,\n",
    "                    expected_ndim=1,\n",
    "                )\n",
    "            ]\n",
    "            if config.num_static_real_features > 0\n",
    "            else []\n",
    "        )\n",
    "        + [\n",
    "            AsNumpyArray(\n",
    "                field=FieldName.TARGET,\n",
    "                # we expect an extra dim for the multivariate case:\n",
    "                expected_ndim=1 if config.input_size == 1 else 2,\n",
    "            ),\n",
    "            # step 3: handle the NaN's by filling in the target with zero\n",
    "            # and return the mask (which is in the observed values)\n",
    "            # true for observed values, false for nan's\n",
    "            # the decoder uses this mask (no loss is incurred for unobserved values)\n",
    "            # see loss_weights inside the xxxForPrediction model\n",
    "            AddObservedValuesIndicator(\n",
    "                target_field=FieldName.TARGET,\n",
    "                output_field=FieldName.OBSERVED_VALUES,\n",
    "            ),\n",
    "            # step 4: add temporal features based on freq of the dataset\n",
    "            # month of year in the case when freq=\"M\"\n",
    "            # these serve as positional encodings\n",
    "            AddTimeFeatures(\n",
    "                start_field=FieldName.START,\n",
    "                target_field=FieldName.TARGET,\n",
    "                output_field=FieldName.FEAT_TIME,\n",
    "                time_features=time_features_from_frequency_str(freq),\n",
    "                pred_length=config.prediction_length,\n",
    "            ),\n",
    "            # step 5: add another temporal feature (just a single number)\n",
    "            # tells the model where in its life the value of the time series is,\n",
    "            # sort of a running counter\n",
    "            AddAgeFeature(\n",
    "                target_field=FieldName.TARGET,\n",
    "                output_field=FieldName.FEAT_AGE,\n",
    "                pred_length=config.prediction_length,\n",
    "                log_scale=True,\n",
    "            ),\n",
    "            # step 6: vertically stack all the temporal features into the key FEAT_TIME\n",
    "            VstackFeatures(\n",
    "                output_field=FieldName.FEAT_TIME,\n",
    "                input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]\n",
    "                + (\n",
    "                    [FieldName.FEAT_DYNAMIC_REAL]\n",
    "                    if config.num_dynamic_real_features > 0\n",
    "                    else []\n",
    "                ),\n",
    "            ),\n",
    "            # step 7: rename to match HuggingFace names\n",
    "            RenameFields(\n",
    "                mapping={\n",
    "                    FieldName.FEAT_STATIC_CAT: \"static_categorical_features\",\n",
    "                    FieldName.FEAT_STATIC_REAL: \"static_real_features\",\n",
    "                    FieldName.FEAT_TIME: \"time_features\",\n",
    "                    FieldName.TARGET: \"values\",\n",
    "                    FieldName.OBSERVED_VALUES: \"observed_mask\",\n",
    "                }\n",
    "            ),\n",
    "        ]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "73c8d1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gluonts.transform.sampler import InstanceSampler\n",
    "from typing import Optional\n",
    "\n",
    "def create_instance_splitter(\n",
    "    config: PretrainedConfig,\n",
    "    mode: str,\n",
    "    train_sampler: Optional[InstanceSampler] = None,\n",
    "    validation_sampler: Optional[InstanceSampler] = None,\n",
    ") -> Transformation:\n",
    "    assert mode in [\"train\", \"validation\", \"test\"]\n",
    "\n",
    "    instance_sampler = {\n",
    "        \"train\": train_sampler\n",
    "        or ExpectedNumInstanceSampler(\n",
    "            num_instances=1.0, min_future=config.prediction_length\n",
    "        ),\n",
    "        \"validation\": validation_sampler\n",
    "        or ValidationSplitSampler(min_future=config.prediction_length),\n",
    "        \"test\": TestSplitSampler(),\n",
    "    }[mode]\n",
    "\n",
    "    return InstanceSplitter(\n",
    "        target_field=\"values\",\n",
    "        is_pad_field=FieldName.IS_PAD,\n",
    "        start_field=FieldName.START,\n",
    "        forecast_start_field=FieldName.FORECAST_START,\n",
    "        instance_sampler=instance_sampler,\n",
    "        past_length=config.context_length + max(config.lags_sequence),\n",
    "        future_length=config.prediction_length,\n",
    "        time_series_fields=[\"time_features\", \"observed_mask\"],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "82d5a50c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Iterable\n",
    "\n",
    "import torch\n",
    "from gluonts.itertools import Cached, Cyclic\n",
    "from gluonts.dataset.loader import as_stacked_batches\n",
    "\n",
    "\n",
    "def create_train_dataloader(\n",
    "    config: PretrainedConfig,\n",
    "    freq,\n",
    "    data,\n",
    "    batch_size: int,\n",
    "    num_batches_per_epoch: int,\n",
    "    shuffle_buffer_length: Optional[int] = None,\n",
    "    cache_data: bool = True,\n",
    "    **kwargs,\n",
    ") -> Iterable:\n",
    "    PREDICTION_INPUT_NAMES = [\n",
    "        \"past_time_features\",\n",
    "        \"past_values\",\n",
    "        \"past_observed_mask\",\n",
    "        \"future_time_features\",\n",
    "    ]\n",
    "    if config.num_static_categorical_features > 0:\n",
    "        PREDICTION_INPUT_NAMES.append(\"static_categorical_features\")\n",
    "\n",
    "    if config.num_static_real_features > 0:\n",
    "        PREDICTION_INPUT_NAMES.append(\"static_real_features\")\n",
    "\n",
    "    TRAINING_INPUT_NAMES = PREDICTION_INPUT_NAMES + [\n",
    "        \"future_values\",\n",
    "        \"future_observed_mask\",\n",
    "    ]\n",
    "\n",
    "    transformation = create_transformation(freq, config)\n",
    "    transformed_data = transformation.apply(data, is_train=True)\n",
    "    if cache_data:\n",
    "        transformed_data = Cached(transformed_data)\n",
    "\n",
    "    # we initialize a Training instance\n",
    "    instance_splitter = create_instance_splitter(config, \"train\")\n",
    "\n",
    "    # the instance splitter will sample a window of\n",
    "    # context length + lags + prediction length (from the 366 possible transformed time series)\n",
    "    # randomly from within the target time series and return an iterator.\n",
    "    stream = Cyclic(transformed_data).stream()\n",
    "    training_instances = instance_splitter.apply(\n",
    "        stream, is_train=True\n",
    "    )\n",
    "    \n",
    "    return as_stacked_batches(\n",
    "        training_instances,\n",
    "        batch_size=batch_size,\n",
    "        shuffle_buffer_length=shuffle_buffer_length,\n",
    "        field_names=TRAINING_INPUT_NAMES,\n",
    "        output_type=torch.tensor,\n",
    "        num_batches_per_epoch=num_batches_per_epoch,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b3f4fb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_test_dataloader(\n",
    "    config: PretrainedConfig,\n",
    "    freq,\n",
    "    data,\n",
    "    batch_size: int,\n",
    "    **kwargs,\n",
    "):\n",
    "    PREDICTION_INPUT_NAMES = [\n",
    "        \"past_time_features\",\n",
    "        \"past_values\",\n",
    "        \"past_observed_mask\",\n",
    "        \"future_time_features\",\n",
    "    ]\n",
    "    if config.num_static_categorical_features > 0:\n",
    "        PREDICTION_INPUT_NAMES.append(\"static_categorical_features\")\n",
    "\n",
    "    if config.num_static_real_features > 0:\n",
    "        PREDICTION_INPUT_NAMES.append(\"static_real_features\")\n",
    "\n",
    "    transformation = create_transformation(freq, config)\n",
    "    transformed_data = transformation.apply(data, is_train=False)\n",
    "\n",
    "    # we create a Test Instance splitter which will sample the very last\n",
    "    # context window seen during training only for the encoder.\n",
    "    instance_sampler = create_instance_splitter(config, \"test\")\n",
    "\n",
    "    # we apply the transformations in test mode\n",
    "    testing_instances = instance_sampler.apply(transformed_data, is_train=False)\n",
    "    \n",
    "    return as_stacked_batches(\n",
    "        testing_instances,\n",
    "        batch_size=batch_size,\n",
    "        output_type=torch.tensor,\n",
    "        field_names=PREDICTION_INPUT_NAMES,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2cbf8ec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['feat_static_real', 'feat_dynamic_real']\n",
      "['feat_static_real', 'feat_dynamic_real']\n"
     ]
    }
   ],
   "source": [
    "train_dataloader = create_train_dataloader(\n",
    "    config=config,\n",
    "    freq=freq,\n",
    "    data=train_dataset,\n",
    "    batch_size=256,\n",
    "    num_batches_per_epoch=100,\n",
    ")\n",
    "\n",
    "test_dataloader = create_test_dataloader(\n",
    "    config=config,\n",
    "    freq=freq,\n",
    "    data=test_dataset,\n",
    "    batch_size=64,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "064793ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "past_time_features torch.Size([256, 85, 2]) torch.FloatTensor\n",
      "past_values torch.Size([256, 85]) torch.FloatTensor\n",
      "past_observed_mask torch.Size([256, 85]) torch.FloatTensor\n",
      "future_time_features torch.Size([256, 24, 2]) torch.FloatTensor\n",
      "static_categorical_features torch.Size([256, 1]) torch.IntTensor\n",
      "future_values torch.Size([256, 24]) torch.FloatTensor\n",
      "future_observed_mask torch.Size([256, 24]) torch.FloatTensor\n"
     ]
    }
   ],
   "source": [
    "batch = next(iter(train_dataloader))\n",
    "for k, v in batch.items():\n",
    "    print(k, v.shape, v.type())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9a1e2036",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# perform forward pass\n",
    "outputs = model(\n",
    "    past_values=batch[\"past_values\"],\n",
    "    past_time_features=batch[\"past_time_features\"],\n",
    "    past_observed_mask=batch[\"past_observed_mask\"],\n",
    "    static_categorical_features=batch[\"static_categorical_features\"]\n",
    "    if config.num_static_categorical_features > 0\n",
    "    else None,\n",
    "    static_real_features=batch[\"static_real_features\"]\n",
    "    if config.num_static_real_features > 0\n",
    "    else None,\n",
    "    future_values=batch[\"future_values\"],\n",
    "    future_time_features=batch[\"future_time_features\"],\n",
    "    future_observed_mask=batch[\"future_observed_mask\"],\n",
    "    output_hidden_states=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "29b4d896",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 9.22168254852295\n",
      "CPU times: total: 0 ns\n",
      "Wall time: 1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "print(\"Loss:\", outputs.loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b00eda51",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.025437355041504\n",
      "CPU times: total: 32min 9s\n",
      "Wall time: 4min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from accelerate import Accelerator\n",
    "from torch.optim import AdamW\n",
    "\n",
    "accelerator = Accelerator()\n",
    "device = accelerator.device\n",
    "\n",
    "model.to(device)\n",
    "optimizer = AdamW(model.parameters(), lr=6e-4, betas=(0.9, 0.95), weight_decay=1e-1)\n",
    "\n",
    "model, optimizer, train_dataloader = accelerator.prepare(\n",
    "    model,\n",
    "    optimizer,\n",
    "    train_dataloader,\n",
    ")\n",
    "\n",
    "model.train()\n",
    "for epoch in range(1):\n",
    "    for idx, batch in enumerate(train_dataloader):\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(\n",
    "            static_categorical_features=batch[\"static_categorical_features\"].to(device)\n",
    "            if config.num_static_categorical_features > 0\n",
    "            else None,\n",
    "            static_real_features=batch[\"static_real_features\"].to(device)\n",
    "            if config.num_static_real_features > 0\n",
    "            else None,\n",
    "            past_time_features=batch[\"past_time_features\"].to(device),\n",
    "            past_values=batch[\"past_values\"].to(device),\n",
    "            future_time_features=batch[\"future_time_features\"].to(device),\n",
    "            future_values=batch[\"future_values\"].to(device),\n",
    "            past_observed_mask=batch[\"past_observed_mask\"].to(device),\n",
    "            future_observed_mask=batch[\"future_observed_mask\"].to(device),\n",
    "        )\n",
    "        loss = outputs.loss\n",
    "\n",
    "        # Backpropagation\n",
    "        accelerator.backward(loss)\n",
    "        optimizer.step()\n",
    "\n",
    "        if idx % 100 == 0:\n",
    "            print(loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2d4001c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "\n",
    "forecasts = []\n",
    "\n",
    "for batch in test_dataloader:\n",
    "    outputs = model.generate(\n",
    "        static_categorical_features=batch[\"static_categorical_features\"].to(device)\n",
    "        if config.num_static_categorical_features > 0\n",
    "        else None,\n",
    "        static_real_features=batch[\"static_real_features\"].to(device)\n",
    "        if config.num_static_real_features > 0\n",
    "        else None,\n",
    "        past_time_features=batch[\"past_time_features\"].to(device),\n",
    "        past_values=batch[\"past_values\"].to(device),\n",
    "        future_time_features=batch[\"future_time_features\"].to(device),\n",
    "        past_observed_mask=batch[\"past_observed_mask\"].to(device),\n",
    "    )\n",
    "    forecasts.append(outputs.sequences.cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f924996a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 100, 24)\n"
     ]
    }
   ],
   "source": [
    "print(forecasts[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d3a9c5db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(366, 100, 24)\n"
     ]
    }
   ],
   "source": [
    "forecasts = np.vstack(forecasts)\n",
    "print(forecasts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7dd17819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b3f56e729e3434ba3a89ce3681b6443",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/5.50k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb80639aba454f75b9620c0c720eefaf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/6.65k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from evaluate import load\n",
    "from gluonts.time_feature import get_seasonality\n",
    "\n",
    "mase_metric = load(\"mase\")\n",
    "smape_metric = load(\"smape\")\n",
    "\n",
    "forecast_median = np.median(forecasts, 1)\n",
    "\n",
    "mase_metrics = []\n",
    "smape_metrics = []\n",
    "for item_id, ts in enumerate(test_dataset):\n",
    "    training_data = ts[\"target\"][:-prediction_length]\n",
    "    ground_truth = ts[\"target\"][-prediction_length:]\n",
    "    mase = mase_metric.compute(\n",
    "        predictions=forecast_median[item_id], \n",
    "        references=np.array(ground_truth), \n",
    "        training=np.array(training_data), \n",
    "        periodicity=get_seasonality(freq))\n",
    "    mase_metrics.append(mase[\"mase\"])\n",
    "    \n",
    "    smape = smape_metric.compute(\n",
    "        predictions=forecast_median[item_id], \n",
    "        references=np.array(ground_truth), \n",
    "    )\n",
    "    smape_metrics.append(smape[\"smape\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b0d85234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MASE: 3.1912513800374036\n",
      "sMAPE: 0.34196129648343504\n"
     ]
    }
   ],
   "source": [
    "print(f\"MASE: {np.mean(mase_metrics)}\")\n",
    "print(f\"sMAPE: {np.mean(smape_metrics)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "29d1ffc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mase_metrics, smape_metrics, alpha=0.3)\n",
    "plt.xlabel(\"MASE\")\n",
    "plt.ylabel(\"sMAPE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d0be3a68",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates\n",
    "\n",
    "def plot(ts_index):\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    index = pd.period_range(\n",
    "        start=test_dataset[ts_index][FieldName.START],\n",
    "        periods=len(test_dataset[ts_index][FieldName.TARGET]),\n",
    "        freq=freq,\n",
    "    ).to_timestamp()\n",
    "\n",
    "    # Major ticks every half year, minor ticks every month,\n",
    "    ax.xaxis.set_major_locator(mdates.MonthLocator(bymonth=(1, 7)))\n",
    "    ax.xaxis.set_minor_locator(mdates.MonthLocator())\n",
    "\n",
    "    ax.plot(\n",
    "        index[-2*prediction_length:], \n",
    "        test_dataset[ts_index][\"target\"][-2*prediction_length:],\n",
    "        label=\"actual\",\n",
    "    )\n",
    "\n",
    "    plt.plot(\n",
    "        index[-prediction_length:], \n",
    "        np.median(forecasts[ts_index], axis=0),\n",
    "        label=\"median\",\n",
    "    )\n",
    "    \n",
    "    plt.fill_between(\n",
    "        index[-prediction_length:],\n",
    "        forecasts[ts_index].mean(0) - forecasts[ts_index].std(axis=0), \n",
    "        forecasts[ts_index].mean(0) + forecasts[ts_index].std(axis=0), \n",
    "        alpha=0.3, \n",
    "        interpolate=True,\n",
    "        label=\"+/- 1-std\",\n",
    "    )\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "015533fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(334)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bae5b44c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}