workflow/pred_temperature_transformer.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict 2 meter temperature with sea surface temperature using transformer with multi-head attention\n",
    "This notebook serves as an example of a basic workflow of data driven forecasting using deep learning with `s2spy` & `lilio` packages. <br>\n",
    "We will predict temperature in US at seasonal time scales using a coarsened ERA5 dataset with multi-head attention transformer, which makes predictions on non-clustered data. <br>\n",
    "\n",
    "<img src=\"../assets/concept_test_case.png\" alt=\"usecase\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This recipe includes the following steps:\n",
    "- [Define a calendar (`lilio`)](#1---define-a-calendar-with-lilio)\n",
    "- [Download/load input data (test data, accessible via `era5cli`)](#2---load-datasets)\n",
    "- [Map the calendar to the data (`lilio`)](#3---map-the-calendar-to-the-data)\n",
    "- [Slice and preprocess data (`s2spy`)](#4---slice-and-preprocess-data)\n",
    "- [Reshape input data](#5---slice-and-reshape-input-data-for-the-transformer)\n",
    "- [Specify hyper-parameters (`wandb`)](#6---hyper-parameter-tuning-with-wb)\n",
    "- [Create transformer model (`torch`)](#7---create-the-transformer-model)\n",
    "- [Train model (`torch`)](#8---train-the-model)\n",
    "- [Evaluate the model](#9---evaluate-the-model)\n",
    "- [Save the model (`onnx`)](#10---save-the-model-to-onnx)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The workflow is illustrated below:\n",
    "\n",
    "<img src=\"../assets/dl.PNG\" alt=\"Transformer\" width=\"900\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0 - Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lilio\n",
    "import numpy as np\n",
    "import sys\n",
    "import time as tt\n",
    "import wandb\n",
    "import urllib\n",
    "import xarray as xr\n",
    "from s2spy import preprocess\n",
    "import torch\n",
    "from torch import nn\n",
    "from pathlib import Path\n",
    "\n",
    "sys.path.append(\"../src/\")\n",
    "from transformer import Transformer\n",
    "import utils"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - Define a calendar with `lilio`\n",
    "\n",
    "Specify a time range for precursor and target intervals. For more information on how this works, you can read the [lilio](https://lilio.readthedocs.io/en/latest/) documentation. \n",
    "\n",
    "In this example, we are creating a calendar with an anchor date of 8th January, a target of 30 days and 8 precursor intervals of 1 month each. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create custom calendar based on the time of interest\n",
    "calendar = lilio.Calendar(anchor=\"08-01\", allow_overlap=True)\n",
    "# add target periods\n",
    "calendar.add_intervals(\"target\", length=\"30d\")\n",
    "# add precursor periods\n",
    "periods_of_interest = 8\n",
    "calendar.add_intervals(\"precursor\", \"1M\", gap=\"1M\", n=periods_of_interest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Calendar(\n",
       "    anchor='08-01',\n",
       "    allow_overlap=True,\n",
       "    mapping=None,\n",
       "    intervals=[\n",
       "        Interval(role='target', length='30d', gap='0d'),\n",
       "        Interval(role='precursor', length='1M', gap='1M'),\n",
       "        Interval(role='precursor', length='1M', gap='1M'),\n",
       "        Interval(role='precursor', length='1M', gap='1M'),\n",
       "        Interval(role='precursor', length='1M', gap='1M'),\n",
       "        Interval(role='precursor', length='1M', gap='1M'),\n",
       "        Interval(role='precursor', length='1M', gap='1M'),\n",
       "        Interval(role='precursor', length='1M', gap='1M'),\n",
       "        Interval(role='precursor', length='1M', gap='1M')\n",
       "    ]\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check calendar\n",
    "calendar"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 - Load datasets\n",
    "\n",
    "Load Sea Surface Temperature (`sst`) over the Pacific Ocean and (clustered) 2m air temperature data (`t2m`) of the southwestern USA. Here we use data from 1959-2021 (63 years total). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(url, field, var):\n",
    "    '''Download a netCDF file from a URL and open a variable as xr.DataArray.'''\n",
    "    url_field = url + field\n",
    "    urllib.request.urlretrieve(url_field, field)\n",
    "    dataset = xr.open_dataset(field)\n",
    "    dataarray = dataset[var]\n",
    "    return dataarray "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# URL of the dataset from zenodo\n",
    "url = \"https://zenodo.org/record/8186914/files/\"\n",
    "\n",
    "sst_field = \"sst_daily_1959-2021_5deg_Pacific_175_240E_25_50N.nc\" # Pacific Ocean SST\n",
    "t2m_field = \"t2m_daily_1959-2021_2deg_clustered_226_300E_30_70N.nc\" # Southwestern USA T2M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "precursor_field = load_data(url, sst_field, \"sst\")\n",
    "target_field = load_data(url, t2m_field, \"t2m\").sel(cluster=3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 - Map the calendar to the data\n",
    "After mapping the calendar to the field, we can visualize our calendar by calling the `visualize` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# map calendar to data\n",
    "calendar.map_to_data(precursor_field)\n",
    "calendar.visualize(show_length=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, we can get a list of all intervals by running the following line. There, you will find the intervals `-1` and `1`, which corresponds to the creation of a precursor interval (negative integer(s)) and a target interval (positive integer(s)), respectively. <br>\n",
    "\n",
    "For more information about the definition of intervals, and how `lilio` works, please check the [README](https://github.com/AI4S2S/lilio) of `lilio`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>i_interval</th>\n",
       "      <th>-8</th>\n",
       "      <th>-7</th>\n",
       "      <th>-6</th>\n",
       "      <th>-5</th>\n",
       "      <th>-4</th>\n",
       "      <th>-3</th>\n",
       "      <th>-2</th>\n",
       "      <th>-1</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anchor_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>[2020-04-01, 2020-05-01)</td>\n",
       "      <td>[2020-06-01, 2020-07-01)</td>\n",
       "      <td>[2020-08-01, 2020-09-01)</td>\n",
       "      <td>[2020-10-01, 2020-11-01)</td>\n",
       "      <td>[2020-12-01, 2021-01-01)</td>\n",
       "      <td>[2021-02-01, 2021-03-01)</td>\n",
       "      <td>[2021-04-01, 2021-05-01)</td>\n",
       "      <td>[2021-06-01, 2021-07-01)</td>\n",
       "      <td>[2021-08-01, 2021-08-31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>[2019-04-01, 2019-05-01)</td>\n",
       "      <td>[2019-06-01, 2019-07-01)</td>\n",
       "      <td>[2019-08-01, 2019-09-01)</td>\n",
       "      <td>[2019-10-01, 2019-11-01)</td>\n",
       "      <td>[2019-12-01, 2020-01-01)</td>\n",
       "      <td>[2020-02-01, 2020-03-01)</td>\n",
       "      <td>[2020-04-01, 2020-05-01)</td>\n",
       "      <td>[2020-06-01, 2020-07-01)</td>\n",
       "      <td>[2020-08-01, 2020-08-31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>[2018-04-01, 2018-05-01)</td>\n",
       "      <td>[2018-06-01, 2018-07-01)</td>\n",
       "      <td>[2018-08-01, 2018-09-01)</td>\n",
       "      <td>[2018-10-01, 2018-11-01)</td>\n",
       "      <td>[2018-12-01, 2019-01-01)</td>\n",
       "      <td>[2019-02-01, 2019-03-01)</td>\n",
       "      <td>[2019-04-01, 2019-05-01)</td>\n",
       "      <td>[2019-06-01, 2019-07-01)</td>\n",
       "      <td>[2019-08-01, 2019-08-31)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "i_interval                         -8                        -7  \\\n",
       "anchor_year                                                       \n",
       "2021         [2020-04-01, 2020-05-01)  [2020-06-01, 2020-07-01)   \n",
       "2020         [2019-04-01, 2019-05-01)  [2019-06-01, 2019-07-01)   \n",
       "2019         [2018-04-01, 2018-05-01)  [2018-06-01, 2018-07-01)   \n",
       "\n",
       "i_interval                         -6                        -5  \\\n",
       "anchor_year                                                       \n",
       "2021         [2020-08-01, 2020-09-01)  [2020-10-01, 2020-11-01)   \n",
       "2020         [2019-08-01, 2019-09-01)  [2019-10-01, 2019-11-01)   \n",
       "2019         [2018-08-01, 2018-09-01)  [2018-10-01, 2018-11-01)   \n",
       "\n",
       "i_interval                         -4                        -3  \\\n",
       "anchor_year                                                       \n",
       "2021         [2020-12-01, 2021-01-01)  [2021-02-01, 2021-03-01)   \n",
       "2020         [2019-12-01, 2020-01-01)  [2020-02-01, 2020-03-01)   \n",
       "2019         [2018-12-01, 2019-01-01)  [2019-02-01, 2019-03-01)   \n",
       "\n",
       "i_interval                         -2                        -1  \\\n",
       "anchor_year                                                       \n",
       "2021         [2021-04-01, 2021-05-01)  [2021-06-01, 2021-07-01)   \n",
       "2020         [2020-04-01, 2020-05-01)  [2020-06-01, 2020-07-01)   \n",
       "2019         [2019-04-01, 2019-05-01)  [2019-06-01, 2019-07-01)   \n",
       "\n",
       "i_interval                          1  \n",
       "anchor_year                            \n",
       "2021         [2021-08-01, 2021-08-31)  \n",
       "2020         [2020-08-01, 2020-08-31)  \n",
       "2019         [2019-08-01, 2019-08-31)  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calendar.show()[:3]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 - Slice and preprocess data\n",
    "In this step, we remove trend and take anomalies for the precursor field. Note that here we use raw daily data for detrending and taking anomalies. <br>\n",
    "\n",
    "In general, there are many \"flavors\" of preprocessing, like when to perform this operation, and in which order do we want to preprocess the data. To improve the transparency and reproducibility of our work, we think it is necessary to standardize these steps. To stick to the best practices, we suggest to preprocess your data in the following way.\n",
    "\n",
    "Here we split the data into a 60%/20%/20% train-validate-test split based on anchor years, preprocess the data and resample it to the calendar to prepare the input for the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split(calendar, field):\n",
    "    \"\"\"Split data into train and test sets based on calendar intervals.\"\"\"\n",
    "    years = sorted(calendar.get_intervals().index)\n",
    "    start_year = years[0]\n",
    "    end_year = years[-1]\n",
    "\n",
    "    # get 60% of instance as training\n",
    "    train_samples = round(len(years) * 0.6)\n",
    "    test_samples = round(len(years) * 0.2)\n",
    "    train = field.sel(time=slice(str(start_year), str(start_year + train_samples - 1)))\n",
    "    test = field.sel(time=slice(str(start_year + train_samples), str(end_year - test_samples)))\n",
    "    valid = field.sel(time=slice(str(end_year - test_samples), str(end_year -1)))\n",
    "\n",
    "    return train, test, valid\n",
    "\n",
    "def preprocess_data(preprocessor, X_data, y_data):\n",
    "    \"\"\"Remove trend and resample data to the calendar.\"\"\"\n",
    "    calendar.map_to_data(X_data)\n",
    "    preprocessor.fit(X_data)\n",
    "    prep = preprocessor.transform(X_data)\n",
    "\n",
    "    X_resample = lilio.resample(calendar, prep)\n",
    "    y_resample = lilio.resample(calendar, y_data)\n",
    "    return X_resample, y_resample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create train-test-validaton split\n",
    "train_slice, test_slice, valid_slice = train_test_split(calendar, precursor_field)\n",
    "target_train_slice, target_test_slice, target_valid_slice = train_test_split(calendar, target_field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create preprocessor\n",
    "preprocessor = preprocess.Preprocessor(\n",
    "    rolling_window_size=25,\n",
    "    timescale=\"monthly\",\n",
    "    detrend=\"linear\",\n",
    "    subtract_climatology=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniconda/base/envs/env_tf/lib/python3.9/site-packages/s2spy/preprocess.py:158: UserWarning: The temporal resolution of data does not completely match the target timescale. Please check your input data.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/Caskroom/miniconda/base/envs/env_tf/lib/python3.9/site-packages/s2spy/preprocess.py:158: UserWarning: The temporal resolution of data does not completely match the target timescale. Please check your input data.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/Caskroom/miniconda/base/envs/env_tf/lib/python3.9/site-packages/s2spy/preprocess.py:158: UserWarning: The temporal resolution of data does not completely match the target timescale. Please check your input data.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Preprocess data and resample to the calendar\n",
    "precursor_field_resample, target_field_resample = preprocess_data(preprocessor, train_slice, target_train_slice)\n",
    "test_resample, target_test_resample = preprocess_data(preprocessor, test_slice, target_test_slice)\n",
    "valid_resample, target_valid_resample = preprocess_data(preprocessor, valid_slice, target_valid_slice)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 - Reshape input data for the transformer\n",
    "\n",
    "Reshape the splits to create pytorch train, test, validate datasets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reshape_data(X_data, y_data):\n",
    "    \"\"\"Reshape data for pytorch model.\"\"\"\n",
    "    sequence_precursor = len(X_data.i_interval) - 1 # we only take precursor parts of i intervals\n",
    "    lat = len(X_data.latitude)\n",
    "    lon = len(X_data.longitude)\n",
    "\n",
    "    X_torch = torch.from_numpy(X_data[:,:-1,:,:].data).type(torch.FloatTensor)\n",
    "    y_torch = torch.from_numpy(y_data[:,-1].data).type(torch.FloatTensor)\n",
    "\n",
    "    X_torch = X_torch.view(-1, sequence_precursor, lat*lon)\n",
    "    y_torch = y_torch.unsqueeze(1).unsqueeze(1).repeat(1, 1, lat*lon)\n",
    "\n",
    "    # turn nan to 0.0\n",
    "    X_torch = torch.nan_to_num(X_torch, 0.0)\n",
    "    return X_torch, y_torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape data\n",
    "train_X_torch, train_y_torch = reshape_data(precursor_field_resample, target_field_resample)\n",
    "valid_X_torch, valid_y_torch = reshape_data(valid_resample, target_valid_resample)\n",
    "test_X_torch, test_y_torch = reshape_data(test_resample, target_test_resample)\n",
    "\n",
    "# pytorch train and test sets\n",
    "train_set = torch.utils.data.TensorDataset(train_X_torch, train_y_torch)\n",
    "valid_set = torch.utils.data.TensorDataset(valid_X_torch, valid_y_torch)\n",
    "test_set = torch.utils.data.TensorDataset(test_X_torch, test_y_torch)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6 - Hyper-parameter tuning with W&B\n",
    "System info and syncronize training information with [W&B server](https://docs.wandb.ai/), which is a tool for tracking experiments, version and iterate machine learning experiments, as well as evaluate model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pytorch version 2.3.1\n",
      "Is CUDA available? False\n",
      "Device to be used for computation: cpu\n"
     ]
    }
   ],
   "source": [
    "print (\"Pytorch version {}\".format(torch.__version__))\n",
    "use_cuda = torch.cuda.is_available()\n",
    "print(\"Is CUDA available? {}\".format(use_cuda))\n",
    "# use GPU if possible\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Device to be used for computation: {}\".format(device))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define hyperparameters, initialize config for wandb and syncronize training information with W&B server. If you have an API key and a login, you can uncomment the lines `wandb.login()`  and change `mode = 'disabled'` to `mode = 'online'`, to synchronise training. Otherwise, keep those lines commented.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
     ]
    }
   ],
   "source": [
    "# call weights & biases service\n",
    "#wandb.login()\n",
    "\n",
    "lat_precursor = len(precursor_field_resample.latitude)\n",
    "lon_precursor = len(precursor_field_resample.longitude)\n",
    "\n",
    "hyperparameters = dict(\n",
    "    epoch = 125,\n",
    "    num_encoder_layers = 1,\n",
    "    num_decoder_layers = 1,\n",
    "    dim_model = lat_precursor*lon_precursor,\n",
    "    num_heads = 1,\n",
    "    dim_feedforward = 12,\n",
    "    batch_size = 10,\n",
    "    dropout = 0.1,\n",
    "    learning_rate = 0.01,\n",
    "    periods_of_interest = periods_of_interest,\n",
    "    dataset = 'Weather',\n",
    "    architecture = 'Transformer'\n",
    ")\n",
    "\n",
    "# initialize weights & biases service\n",
    "#mode = 'online'\n",
    "mode = 'disabled'\n",
    "wandb.init(config=hyperparameters, project='test-transformer', entity='ai4s2s', mode=mode)\n",
    "config = wandb.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create data loader and use batch \n",
    "train_loader = torch.utils.data.DataLoader(train_set, batch_size = config.batch_size, shuffle = False)\n",
    "valid_loader = torch.utils.data.DataLoader(valid_set, batch_size = config.batch_size, shuffle = False)\n",
    "test_loader = torch.utils.data.DataLoader(test_set, batch_size = config.batch_size, shuffle = False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7 - Create the transformer model\n",
    "Create transformer. Initialize model and choose loss function and optimizer. <br>\n",
    "\n",
    "The architecture of the transformer is illustrated in the figure below, which is from the paper [Attention Is All You Need](https://arxiv.org/abs/1706.03762).\n",
    "\n",
    "<img src=\"../assets/transformer.webp\" alt=\"Transformer\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model details:\n",
      " Transformer(\n",
      "  (encoder): TransformerEncoder(\n",
      "    (layers): ModuleList(\n",
      "      (0): TransformerEncoderLayer(\n",
      "        (attention): Residual(\n",
      "          (sublayer): MultiHeadAttention(\n",
      "            (heads): ModuleList(\n",
      "              (0): AttentionHead(\n",
      "                (q): Linear(in_features=65, out_features=65, bias=True)\n",
      "                (k): Linear(in_features=65, out_features=65, bias=True)\n",
      "                (v): Linear(in_features=65, out_features=65, bias=True)\n",
      "              )\n",
      "            )\n",
      "            (linear): Linear(in_features=65, out_features=65, bias=True)\n",
      "          )\n",
      "          (norm): LayerNorm((65,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (feed_forward): Residual(\n",
      "          (sublayer): Sequential(\n",
      "            (0): Linear(in_features=65, out_features=12, bias=True)\n",
      "            (1): ReLU()\n",
      "            (2): Linear(in_features=12, out_features=65, bias=True)\n",
      "          )\n",
      "          (norm): LayerNorm((65,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (decoder): TransformerDecoder(\n",
      "    (layers): ModuleList(\n",
      "      (0): TransformerDecoderLayer(\n",
      "        (attention_1): Residual(\n",
      "          (sublayer): MultiHeadAttention(\n",
      "            (heads): ModuleList(\n",
      "              (0): AttentionHead(\n",
      "                (q): Linear(in_features=65, out_features=65, bias=True)\n",
      "                (k): Linear(in_features=65, out_features=65, bias=True)\n",
      "                (v): Linear(in_features=65, out_features=65, bias=True)\n",
      "              )\n",
      "            )\n",
      "            (linear): Linear(in_features=65, out_features=65, bias=True)\n",
      "          )\n",
      "          (norm): LayerNorm((65,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (attention_2): Residual(\n",
      "          (sublayer): MultiHeadAttention(\n",
      "            (heads): ModuleList(\n",
      "              (0): AttentionHead(\n",
      "                (q): Linear(in_features=65, out_features=65, bias=True)\n",
      "                (k): Linear(in_features=65, out_features=65, bias=True)\n",
      "                (v): Linear(in_features=65, out_features=65, bias=True)\n",
      "              )\n",
      "            )\n",
      "            (linear): Linear(in_features=65, out_features=65, bias=True)\n",
      "          )\n",
      "          (norm): LayerNorm((65,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (feed_forward): Residual(\n",
      "          (sublayer): Sequential(\n",
      "            (0): Linear(in_features=65, out_features=12, bias=True)\n",
      "            (1): ReLU()\n",
      "            (2): Linear(in_features=12, out_features=65, bias=True)\n",
      "          )\n",
      "          (norm): LayerNorm((65,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (linear): Linear(in_features=65, out_features=65, bias=True)\n",
      "  )\n",
      ")\n",
      "Optimizer details:\n",
      " Adam (\n",
      "Parameter Group 0\n",
      "    amsgrad: False\n",
      "    betas: (0.9, 0.999)\n",
      "    capturable: False\n",
      "    differentiable: False\n",
      "    eps: 1e-08\n",
      "    foreach: None\n",
      "    fused: None\n",
      "    lr: 0.01\n",
      "    maximize: False\n",
      "    weight_decay: 0\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize model\n",
    "model = Transformer(num_encoder_layers = config[\"num_encoder_layers\"],\n",
    "                    num_decoder_layers = config[\"num_decoder_layers\"],\n",
    "                    dim_model = config[\"dim_model\"], \n",
    "                    num_heads = config[\"num_heads\"], \n",
    "                    dim_feedforward = config[\"dim_feedforward\"], \n",
    "                    dropout = config[\"dropout\"])\n",
    "# Specify loss function\n",
    "criterion = nn.MSELoss()\n",
    "# Choose optimizer\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)\n",
    "# Print model and optimizer details\n",
    "print('Model details:\\n', model)\n",
    "print('Optimizer details:\\n',optimizer)\n",
    "wandb.watch(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59694\n"
     ]
    }
   ],
   "source": [
    "# display the total number of parameters\n",
    "utils.total_num_param(model)\n",
    "# for more details about the trainable parameter in each layer\n",
    "#utils.param_trainable(model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8 - Train the model\n",
    "\n",
    "Now we can train the model using the training set that we created. Then we switch the model into eval mode for cross-validation with the validation set. We can then plot the training and validation loss against eachother. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch : 0 [0/36(0%)]\tLoss: 87444.593750\n",
      "Epoch : 0 [20/36(50%)]\tLoss: 87112.765625\n",
      "Epoch : 1 [0/36(0%)]\tLoss: 86021.132812\n",
      "Epoch : 1 [20/36(50%)]\tLoss: 85657.000000\n",
      "Epoch : 2 [0/36(0%)]\tLoss: 84488.148438\n",
      "Epoch : 2 [20/36(50%)]\tLoss: 83976.101562\n",
      "Epoch : 3 [0/36(0%)]\tLoss: 82669.226562\n",
      "Epoch : 3 [20/36(50%)]\tLoss: 82029.156250\n",
      "Epoch : 4 [0/36(0%)]\tLoss: 80531.914062\n",
      "Epoch : 4 [20/36(50%)]\tLoss: 79766.187500\n",
      "Epoch : 5 [0/36(0%)]\tLoss: 78118.304688\n",
      "Epoch : 5 [20/36(50%)]\tLoss: 77138.968750\n",
      "Epoch : 6 [0/36(0%)]\tLoss: 75393.929688\n",
      "Epoch : 6 [20/36(50%)]\tLoss: 74263.726562\n",
      "Epoch : 7 [0/36(0%)]\tLoss: 72353.695312\n",
      "Epoch : 7 [20/36(50%)]\tLoss: 71090.867188\n",
      "Epoch : 8 [0/36(0%)]\tLoss: 69019.460938\n",
      "Epoch : 8 [20/36(50%)]\tLoss: 67539.929688\n",
      "Epoch : 9 [0/36(0%)]\tLoss: 65363.859375\n",
      "Epoch : 9 [20/36(50%)]\tLoss: 63742.972656\n",
      "Epoch : 10 [0/36(0%)]\tLoss: 61430.726562\n",
      "Epoch : 10 [20/36(50%)]\tLoss: 59682.890625\n",
      "Epoch : 11 [0/36(0%)]\tLoss: 57267.574219\n",
      "Epoch : 11 [20/36(50%)]\tLoss: 55400.617188\n",
      "Epoch : 12 [0/36(0%)]\tLoss: 52904.105469\n",
      "Epoch : 12 [20/36(50%)]\tLoss: 50961.785156\n",
      "Epoch : 13 [0/36(0%)]\tLoss: 48434.851562\n",
      "Epoch : 13 [20/36(50%)]\tLoss: 46384.691406\n",
      "Epoch : 14 [0/36(0%)]\tLoss: 43714.503906\n",
      "Epoch : 14 [20/36(50%)]\tLoss: 41678.660156\n",
      "Epoch : 15 [0/36(0%)]\tLoss: 39062.265625\n",
      "Epoch : 15 [20/36(50%)]\tLoss: 36992.203125\n",
      "Epoch : 16 [0/36(0%)]\tLoss: 34443.000000\n",
      "Epoch : 16 [20/36(50%)]\tLoss: 32402.460938\n",
      "Epoch : 17 [0/36(0%)]\tLoss: 29968.242188\n",
      "Epoch : 17 [20/36(50%)]\tLoss: 27935.378906\n",
      "Epoch : 18 [0/36(0%)]\tLoss: 25592.330078\n",
      "Epoch : 18 [20/36(50%)]\tLoss: 23728.117188\n",
      "Epoch : 19 [0/36(0%)]\tLoss: 21501.140625\n",
      "Epoch : 19 [20/36(50%)]\tLoss: 19749.160156\n",
      "Epoch : 20 [0/36(0%)]\tLoss: 17695.662109\n",
      "Epoch : 20 [20/36(50%)]\tLoss: 16046.192383\n",
      "Epoch : 21 [0/36(0%)]\tLoss: 14232.882812\n",
      "Epoch : 21 [20/36(50%)]\tLoss: 12791.376953\n",
      "Epoch : 22 [0/36(0%)]\tLoss: 11163.440430\n",
      "Epoch : 22 [20/36(50%)]\tLoss: 9891.311523\n",
      "Epoch : 23 [0/36(0%)]\tLoss: 8504.294922\n",
      "Epoch : 23 [20/36(50%)]\tLoss: 7418.319824\n",
      "Epoch : 24 [0/36(0%)]\tLoss: 6249.827148\n",
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      "Epoch : 28 [0/36(0%)]\tLoss: 1193.629395\n",
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      "Epoch : 29 [0/36(0%)]\tLoss: 679.258179\n",
      "Epoch : 29 [20/36(50%)]\tLoss: 517.292053\n",
      "Epoch : 30 [0/36(0%)]\tLoss: 350.477112\n",
      "Epoch : 30 [20/36(50%)]\tLoss: 261.299316\n",
      "Epoch : 31 [0/36(0%)]\tLoss: 156.614014\n",
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      "Epoch : 32 [0/36(0%)]\tLoss: 56.878883\n",
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      "Epoch : 96 [0/36(0%)]\tLoss: 0.921264\n",
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      "Epoch : 99 [0/36(0%)]\tLoss: 0.443661\n",
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      "Epoch : 100 [0/36(0%)]\tLoss: 0.539481\n",
      "Epoch : 100 [20/36(50%)]\tLoss: 0.637506\n",
      "Epoch : 101 [0/36(0%)]\tLoss: 0.647896\n",
      "Epoch : 101 [20/36(50%)]\tLoss: 0.707450\n",
      "Epoch : 102 [0/36(0%)]\tLoss: 0.746711\n",
      "Epoch : 102 [20/36(50%)]\tLoss: 1.348640\n",
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      "Epoch : 103 [20/36(50%)]\tLoss: 1.065842\n",
      "Epoch : 104 [0/36(0%)]\tLoss: 0.607411\n",
      "Epoch : 104 [20/36(50%)]\tLoss: 1.104639\n",
      "Epoch : 105 [0/36(0%)]\tLoss: 0.918489\n",
      "Epoch : 105 [20/36(50%)]\tLoss: 0.766802\n",
      "Epoch : 106 [0/36(0%)]\tLoss: 0.313380\n",
      "Epoch : 106 [20/36(50%)]\tLoss: 1.280994\n",
      "Epoch : 107 [0/36(0%)]\tLoss: 0.362659\n",
      "Epoch : 107 [20/36(50%)]\tLoss: 1.072277\n",
      "Epoch : 108 [0/36(0%)]\tLoss: 0.920924\n",
      "Epoch : 108 [20/36(50%)]\tLoss: 1.132445\n",
      "Epoch : 109 [0/36(0%)]\tLoss: 0.578207\n",
      "Epoch : 109 [20/36(50%)]\tLoss: 1.275079\n",
      "Epoch : 110 [0/36(0%)]\tLoss: 0.664828\n",
      "Epoch : 110 [20/36(50%)]\tLoss: 1.424034\n",
      "Epoch : 111 [0/36(0%)]\tLoss: 0.948056\n",
      "Epoch : 111 [20/36(50%)]\tLoss: 0.943093\n",
      "Epoch : 112 [0/36(0%)]\tLoss: 0.832289\n",
      "Epoch : 112 [20/36(50%)]\tLoss: 1.299415\n",
      "Epoch : 113 [0/36(0%)]\tLoss: 0.523372\n",
      "Epoch : 113 [20/36(50%)]\tLoss: 1.186358\n",
      "Epoch : 114 [0/36(0%)]\tLoss: 0.668931\n",
      "Epoch : 114 [20/36(50%)]\tLoss: 0.859344\n",
      "Epoch : 115 [0/36(0%)]\tLoss: 1.089994\n",
      "Epoch : 115 [20/36(50%)]\tLoss: 0.844235\n",
      "Epoch : 116 [0/36(0%)]\tLoss: 0.886663\n",
      "Epoch : 116 [20/36(50%)]\tLoss: 0.848208\n",
      "Epoch : 117 [0/36(0%)]\tLoss: 0.913828\n",
      "Epoch : 117 [20/36(50%)]\tLoss: 0.692517\n",
      "Epoch : 118 [0/36(0%)]\tLoss: 0.533346\n",
      "Epoch : 118 [20/36(50%)]\tLoss: 0.798096\n",
      "Epoch : 119 [0/36(0%)]\tLoss: 0.594368\n",
      "Epoch : 119 [20/36(50%)]\tLoss: 0.808265\n",
      "Epoch : 120 [0/36(0%)]\tLoss: 0.622331\n",
      "Epoch : 120 [20/36(50%)]\tLoss: 0.890031\n",
      "Epoch : 121 [0/36(0%)]\tLoss: 0.790811\n",
      "Epoch : 121 [20/36(50%)]\tLoss: 0.979813\n",
      "Epoch : 122 [0/36(0%)]\tLoss: 0.563968\n",
      "Epoch : 122 [20/36(50%)]\tLoss: 0.973354\n",
      "Epoch : 123 [0/36(0%)]\tLoss: 0.656007\n",
      "Epoch : 123 [20/36(50%)]\tLoss: 0.754987\n",
      "Epoch : 124 [0/36(0%)]\tLoss: 0.854421\n",
      "Epoch : 124 [20/36(50%)]\tLoss: 0.874253\n",
      "--- 0.04140973885854085 minutes ---\n"
     ]
    }
   ],
   "source": [
    "# calculate the time for the code execution\n",
    "start_time = tt.time()\n",
    "\n",
    "# switch model into training mode\n",
    "model.train()\n",
    "\n",
    "hist_train = []\n",
    "hist_valid = []\n",
    "for epoch in range(config.epoch):\n",
    "    # training loop\n",
    "    # switch model into train mode\n",
    "    model.train()\n",
    "    hist_train_step = 0\n",
    "    for batch_idx, (X_batch, y_batch) in enumerate(train_loader):\n",
    "        var_X_batch = torch.autograd.Variable(X_batch).to(device)\n",
    "        var_y_batch = torch.autograd.Variable(y_batch).to(device)\n",
    "        optimizer.zero_grad()\n",
    "        # note: decoder input is the last instance of encoder input\n",
    "        output = model(var_X_batch, var_X_batch.clone()[:,-1:,:])\n",
    "        loss = criterion(output, var_y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        wandb.log({'train_loss': loss.item()})\n",
    "        if batch_idx % 2 == 0:\n",
    "            print(f'Epoch : {epoch} [{batch_idx*len(X_batch)}/{len(train_loader.dataset)}'\n",
    "                  f'({100.* batch_idx / len(train_loader):.0f}%)]\\tLoss: {loss.item():.6f}')\n",
    "        hist_train_step += loss.item()\n",
    "\n",
    "    hist_train.append(hist_train_step / len(train_loader.dataset))\n",
    "\n",
    "    # cross-validation loop\n",
    "    # switch model into evaluation mode\n",
    "    model.eval()\n",
    "    hist_valid_step = 0\n",
    "\n",
    "    for batch_idx, (X_batch, y_batch) in enumerate(valid_loader):\n",
    "        var_X_batch = torch.autograd.Variable(X_batch).to(device)\n",
    "        var_y_batch = torch.autograd.Variable(y_batch).to(device)\n",
    "        optimizer.zero_grad()\n",
    "        with torch.no_grad():\n",
    "            output = model(var_X_batch, var_X_batch.clone()[:,-1:,:])\n",
    "            loss = criterion(output, var_y_batch)\n",
    "        wandb.log({'validation_loss': loss.item()})\n",
    "        hist_valid_step += loss.item()\n",
    "\n",
    "    hist_valid.append(hist_valid_step / len(valid_loader.dataset))\n",
    "\n",
    "print (f\"--- {(tt.time() - start_time)/60} minutes ---\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check the training loss and validation loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12, 5))\n",
    "fig.add_subplot(1, 2, 1)\n",
    "plt.plot(np.asarray(hist_train), 'b', label=\"Training loss\")\n",
    "plt.plot(np.asarray(hist_valid), 'r', label=\"Validation loss\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title(\"MSE\")\n",
    "plt.legend()\n",
    "\n",
    "fig.add_subplot(1, 2, 2)\n",
    "plt.plot(np.log(np.asarray(hist_train)), 'b', label=\"Training loss\")\n",
    "plt.plot(np.log(np.asarray(hist_valid)), 'r', label=\"Validation loss\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Logarithmic loss')\n",
    "plt.title(\"Logarithmic MSE\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the checkpoint model training\n",
    "\n",
    "output_path = Path.cwd().parent / \"models\"\n",
    "output_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "checkpoint_path = output_path / 'transformer_train_checkpoint.pt'\n",
    "\n",
    "torch.save({\n",
    "            'epoch': epoch,\n",
    "            'model_state_dict': model.state_dict(),\n",
    "            'optimizer_state_dict': optimizer.state_dict(),\n",
    "            'loss': loss.item()\n",
    "            }, checkpoint_path)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9 - Evaluate the model\n",
    "Now we can evaluate our model with the testing set and compare the predictions with the ground truth (y component of the testing set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# switch model into evaluation mode\n",
    "model.eval()\n",
    "hist_test = []\n",
    "predictions = []\n",
    "hist_test_step = 0\n",
    "for batch_idx, (X_batch, y_batch) in enumerate(test_loader):\n",
    "    var_X_batch = torch.autograd.Variable(X_batch).to(device)\n",
    "    var_y_batch = torch.autograd.Variable(y_batch).to(device)\n",
    "    optimizer.zero_grad()\n",
    "    with torch.no_grad():\n",
    "        output = model(var_X_batch, var_X_batch.clone()[:,-1:,:])\n",
    "        loss = criterion(output, var_y_batch)\n",
    "    wandb.log({'testing_loss': loss.item()})\n",
    "    predictions.append(torch.mean(output.squeeze(), dim=1).cpu().detach().numpy())\n",
    "    hist_test_step += loss.item()\n",
    "\n",
    "hist_test.append(hist_test_step / len(test_loader.dataset))\n",
    "# call wandb finish to stop logging\n",
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE loss is 0.141\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"The MSE loss is {hist_test[0]:.3f}\")\n",
    "\n",
    "fig = plt.figure()\n",
    "instances = np.arange(len(np.concatenate(predictions)))\n",
    "plt.scatter(instances, np.concatenate(predictions), label=\"Predictions\")\n",
    "plt.scatter(instances, np.mean(test_y_torch.squeeze().numpy(), 1), label=\"Ground truth\")\n",
    "plt.xlabel(\"Experiment\")\n",
    "plt.ylabel(\"TS\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 - Save the model to ONNX\n",
    "\n",
    "Finally, we can save the model to [ONNX](https://onnx.ai/) so that we can use the model again. We then also load and run the model again to check that this was done correctly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniconda/base/envs/env_tf/lib/python3.9/site-packages/torch/onnx/utils.py:631: UserWarning: ONNX Preprocess - Removing mutation from node aten::add_ on block input: '0'. This changes graph semantics. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/jit/passes/onnx/remove_inplace_ops_for_onnx.cpp:355.)\n",
      "  _C._jit_pass_onnx_remove_inplace_ops_for_onnx(graph, module)\n",
      "/opt/homebrew/Caskroom/miniconda/base/envs/env_tf/lib/python3.9/site-packages/torch/onnx/utils.py:631: UserWarning: ONNX Preprocess - Removing mutation from node aten::add_ on block input: '1'. This changes graph semantics. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/jit/passes/onnx/remove_inplace_ops_for_onnx.cpp:355.)\n",
      "  _C._jit_pass_onnx_remove_inplace_ops_for_onnx(graph, module)\n"
     ]
    }
   ],
   "source": [
    "import onnxruntime as ort\n",
    "import torch.onnx as torch_onnx\n",
    "\n",
    "# Export the model\n",
    "model_path = output_path / 'transformer_model.onnx'\n",
    "torch_onnx.export(model, (test_X_torch, test_X_torch.clone()[:,-1:,:]), model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's load the model and plot the means of the outputs as was done earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the ONNX model\n",
    "session = ort.InferenceSession(model_path)\n",
    "\n",
    "# Run the model\n",
    "input_names = [i.name for i in session.get_inputs()]\n",
    "inputs = {input_names[0]: test_X_torch.cpu().detach().numpy(), input_names[1]: test_X_torch.clone()[:,-1:,:].cpu().detach().numpy()}\n",
    "outputs = session.run(None, inputs)[0]\n",
    "\n",
    "# Take the mean of each instance for plotting\n",
    "outputs_mean = []\n",
    "for i in outputs:\n",
    "    outputs_mean.append(i[0].mean())\n",
    "\n",
    "print(outputs_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "instances = np.arange(len(np.concatenate(predictions)))\n",
    "plt.scatter(np.arange(len(outputs_mean)), outputs_mean, label =\"Loaded model\")\n",
    "plt.scatter(instances, np.mean(test_y_torch.squeeze().numpy(), 1), label=\"Ground truth\")\n",
    "plt.xlabel(\"Experiment\")\n",
    "plt.ylabel(\"TS\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "s2spy",
   "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": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}