workflow/comp_pred_ridge_and_LSTM.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict 2 meter temperature with sea surface temperature using deep learning and evaluate the predictions with linear regression results\n",
    "This notebook shows how to build a workflow of data driven forecasting using machine learning with `s2spy` & `lilio` packages. It highlights how these packages could facilitate (sub)seasonal forecasts with deep learning and creating baseline forecasts for evaluation.<br>\n",
    "\n",
    "We will predict temperature in US at seasonal time scales using ERA5 dataset with a LSTM network and evaluate the forecasts against those from linear regression (Ridge). <br>\n",
    "\n",
    "<img src=\"../assets/concept_test_case.png\" alt=\"usecase\" width=\"500\"/>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This recipe includes the following steps:\n",
    "- Define a calendar (`lilio`)\n",
    "- Download/load input data (test data, accessible via `era5cli`)\n",
    "- Map the calendar to the data (`lilio`)\n",
    "- Train-validate-test split (60%/20%/20%)\n",
    "- Preprocessing based on the training set (`s2spy`)\n",
    "- Resample data to the calendar (`lilio`)\n",
    "- Create LSTM model (`torch`)\n",
    "- Specify hyper-parameters (`wandb`)\n",
    "- Train LSTM model (`torch`)\n",
    "- Linear regression model training (incl. cross-validation) (`lilio` & `scikit-learn`)\n",
    "- Evaludate forecasts with baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lilio\n",
    "import numpy as np\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 torch.autograd import Variable\n",
    "\n",
    "# for reproducibility \n",
    "np.random.seed(1)\n",
    "torch.manual_seed(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Prepare data for data-driven forecasting\n",
    "\n",
    "In this section, we build a data pipeline to preprocess and resample data with `s2spy` & `lilio`. <br>\n",
    "We will see how `lilio` can help us manage and aggregate data with a user-defined `calendar`. Simple data preprocessing will be achieved using `s2spy`.<br>\n",
    "By following these steps, the raw input data will be ready for your data-driven forecasting, including a deep learning recipe and a regression-based workflow."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define a calendar with `lilio` to specify time range for targets and precursors."
   ]
  },
  {
   "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": [
    "#### Load test data SST and (clustered) T2M\n",
    "For the sake of batch size, we use 61 years (1961-2021) of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('t2m_daily_1959-2021_2deg_clustered_226_300E_30_70N.nc',\n",
       " <http.client.HTTPMessage at 0x2a29f9b80>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# URL of the dataset from zenodo\n",
    "sst_url = \"https://zenodo.org/record/8186914/files/sst_daily_1959-2021_5deg_Pacific_175_240E_25_50N.nc\"\n",
    "t2m_url = \"https://zenodo.org/record/8186914/files/t2m_daily_1959-2021_2deg_clustered_226_300E_30_70N.nc\"\n",
    "sst_field = \"sst_daily_1959-2021_5deg_Pacific_175_240E_25_50N.nc\"\n",
    "t2m_field = \"t2m_daily_1959-2021_2deg_clustered_226_300E_30_70N.nc\"\n",
    "\n",
    "urllib.request.urlretrieve(sst_url, sst_field)\n",
    "urllib.request.urlretrieve(t2m_url, t2m_field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "precursor_field = xr.open_dataset(sst_field)\n",
    "precursor_field = precursor_field.sel(time=slice(\"19610101\",\"20211231\"))\n",
    "target_field = xr.open_dataset(t2m_field)\n",
    "target_field = target_field.sel(time=slice(\"19610101\",\"20211231\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Kelvin to Celcius\n",
    "precursor_field[\"sst\"] = precursor_field[\"sst\"] - 273.15\n",
    "target_field[\"t2m\"] = target_field[\"t2m\"] - 273.15"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 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": [
    "##### Train-validate-test split based on the anchor years (60%/20%/20% split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get 70% of instance as training\n",
    "years = sorted(calendar.get_intervals().index)\n",
    "train_samples = round(len(years) * 0.6)\n",
    "test_samples = round(len(years) * 0.2)\n",
    "start_year = years[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Fit preprocessor with training samples and preprocess data\n",
    "In this step, we remove the trend and compute anomalies for the precursor field. Note that here we use raw daily data for detrending and computing 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
    ")\n",
    "\n",
    "# fit preprocessor with training data\n",
    "preprocessor.fit(\n",
    "    precursor_field.sel(\n",
    "        time=slice(str(start_year), str(start_year + train_samples - 1))\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocess the whole precursor field\n",
    "precursor_field_prep = preprocessor.transform(precursor_field)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Resample data to the calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "precursor_field_resample = lilio.resample(calendar, precursor_field_prep)\n",
    "target_field_resample = lilio.resample(calendar, target_field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select variables and intervals\n",
    "precursor_field_sel = precursor_field_resample['sst']\n",
    "target_series_sel = target_field_resample['t2m'].sel(cluster=3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Build workflow to train a LSTM network for forecasting\n",
    "In this section, we will start building a LSTM network using pytorch and train our neural network to forecast 2 meter temperture with sea surface temperature."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin with, We need to convert our data to `torch.Tensor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# slice and reshape input desired by transformer\n",
    "sequence_precursor = len(precursor_field_sel.i_interval) - 1 # we only take precursor parts of i intervals\n",
    "lat_precursor = len(precursor_field_sel.latitude)\n",
    "lon_precursor = len(precursor_field_sel.longitude)\n",
    "\n",
    "X_torch = torch.from_numpy(precursor_field_sel[:,:-1,:,:].data).type(torch.FloatTensor)\n",
    "y_torch = torch.from_numpy(target_series_sel[:,-1].data).type(torch.FloatTensor)\n",
    "\n",
    "X_torch = X_torch.view(-1, sequence_precursor, lat_precursor*lon_precursor)\n",
    "\n",
    "# turn nan to 0.0\n",
    "X_torch = torch.nan_to_num(X_torch, 0.0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split our data into train/cross-validate/test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train/validate/test split and use pytorch dataloader\n",
    "train_X_torch = X_torch[:train_samples]\n",
    "train_y_torch = y_torch[:train_samples]\n",
    "\n",
    "valid_X_torch = X_torch[train_samples:train_samples + test_samples]\n",
    "valid_y_torch = y_torch[train_samples:train_samples + test_samples]\n",
    "\n",
    "test_X_torch = X_torch[-test_samples:]\n",
    "test_y_torch = y_torch[-test_samples:]\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": [
    "##### Build LSTM model\n",
    "Build a LSTM model with `nn.LSTM` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim=1,\n",
    "                 batch_size=1, num_layers=1):\n",
    "        \"\"\"\n",
    "        Initialize the LSTM model in Pytorch and specify the basic model structure.\n",
    "        Expected input timeseries dimension [batch_size, sequence, channels]\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.batch_size = batch_size\n",
    "        self.num_layers = num_layers\n",
    "        # Define the LSTM layer\n",
    "        self.lstm = nn.LSTM(input_size = input_dim, hidden_size = hidden_dim,\n",
    "                            num_layers = num_layers, batch_first = True)\n",
    "\n",
    "        # Define the output layer\n",
    "        self.linear = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "    def init_hidden(self):\n",
    "        \"\"\"Initialize hidden state with random values.\"\"\"\n",
    "        return (torch.randn(self.num_layers, self.batch_size, self.hidden_dim),\n",
    "                torch.randn(self.num_layers, self.batch_size, self.hidden_dim))\n",
    "        \n",
    "    def forward(self, input):\n",
    "        (h_0, c_0) = self.init_hidden()\n",
    "        x, _ = self.lstm(input, (h_0, c_0))\n",
    "        x = self.linear(x)\n",
    "            \n",
    "        return x"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hyper-parameter tuning with W&B\n",
    "We use Weight&Biases to monitor the training process. It is very simple to integrate it into your workflow and more information about how to set it up can be found at https://docs.wandb.ai/quickstart. <br>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first take a look at the system information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pytorch version 2.1.2\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# call weights & biases service\n",
    "# wandb.login()\n",
    "\n",
    "# define hyperparameters and the \n",
    "hyperparameters = dict(\n",
    "    epoch = 150,\n",
    "    input_dim = lat_precursor*lon_precursor,\n",
    "    hidden_dim = lat_precursor*lon_precursor*2,\n",
    "    output_dim = 1,\n",
    "    batch_size = 4, \n",
    "    num_layers = 2,\n",
    "    learning_rate = 0.02,\n",
    "    dataset = 'Weather',\n",
    "    architecture = 'LSTM'\n",
    ")\n",
    "\n",
    "# initialize weights & biases service\n",
    "#mode = 'online'\n",
    "mode = 'disabled'\n",
    "wandb.init(config=hyperparameters, project='test-LSTM-ridge', entity='ai4s2s', mode=mode)\n",
    "config = wandb.config"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create data loaders with chosen batch size. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "##### Initialize and train model\n",
    "Create model using specified hyperparameter. Initialize model and choose loss function and optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model details:\n",
      " LSTM(\n",
      "  (lstm): LSTM(65, 130, num_layers=2, batch_first=True)\n",
      "  (linear): Linear(in_features=130, out_features=1, bias=True)\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: 0.02\n",
      "    maximize: False\n",
      "    weight_decay: 0\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize model\n",
    "model = LSTM(input_dim = config[\"input_dim\"],\n",
    "             hidden_dim = config[\"hidden_dim\"],\n",
    "             output_dim = config[\"output_dim\"], \n",
    "             batch_size = config[\"batch_size\"], \n",
    "             num_layers = config[\"num_layers\"]\n",
    ")\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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start the training and cross validation loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch : 0 [0/36(0%)]\tLoss: 505.867676\n",
      "Epoch : 0 [4/36(11%)]\tLoss: 452.694275\n",
      "Epoch : 0 [8/36(22%)]\tLoss: 346.542755\n",
      "Epoch : 0 [12/36(33%)]\tLoss: 244.391663\n",
      "Epoch : 0 [16/36(44%)]\tLoss: 194.889648\n",
      "Epoch : 0 [20/36(56%)]\tLoss: 144.906769\n",
      "Epoch : 0 [24/36(67%)]\tLoss: 92.627434\n",
      "Epoch : 0 [28/36(78%)]\tLoss: 186.805725\n",
      "Epoch : 0 [32/36(89%)]\tLoss: 29.274612\n",
      "Epoch : 1 [0/36(0%)]\tLoss: 7.995927\n",
      "Epoch : 1 [4/36(11%)]\tLoss: 1.477770\n",
      "Epoch : 1 [8/36(22%)]\tLoss: 0.448855\n",
      "Epoch : 1 [12/36(33%)]\tLoss: 4.436897\n",
      "Epoch : 1 [16/36(44%)]\tLoss: 8.041482\n",
      "Epoch : 1 [20/36(56%)]\tLoss: 12.566885\n",
      "Epoch : 1 [24/36(67%)]\tLoss: 19.384514\n",
      "Epoch : 1 [28/36(78%)]\tLoss: 139.261978\n",
      "Epoch : 1 [32/36(89%)]\tLoss: 27.007771\n",
      "Epoch : 2 [0/36(0%)]\tLoss: 30.385290\n",
      "Epoch : 2 [4/36(11%)]\tLoss: 28.568874\n",
      "Epoch : 2 [8/36(22%)]\tLoss: 19.039696\n",
      "Epoch : 2 [12/36(33%)]\tLoss: 16.996681\n",
      "Epoch : 2 [16/36(44%)]\tLoss: 8.943427\n",
      "Epoch : 2 [20/36(56%)]\tLoss: 5.135639\n",
      "Epoch : 2 [24/36(67%)]\tLoss: 3.159722\n",
      "Epoch : 2 [28/36(78%)]\tLoss: 2.431393\n",
      "Epoch : 2 [32/36(89%)]\tLoss: 1.751465\n",
      "Epoch : 3 [0/36(0%)]\tLoss: 0.228410\n",
      "Epoch : 3 [4/36(11%)]\tLoss: 0.989159\n",
      "Epoch : 3 [8/36(22%)]\tLoss: 2.946949\n",
      "Epoch : 3 [12/36(33%)]\tLoss: 3.109305\n",
      "Epoch : 3 [16/36(44%)]\tLoss: 7.060810\n",
      "Epoch : 3 [20/36(56%)]\tLoss: 10.174531\n",
      "Epoch : 3 [24/36(67%)]\tLoss: 8.076360\n",
      "Epoch : 3 [28/36(78%)]\tLoss: 5.851216\n",
      "Epoch : 3 [32/36(89%)]\tLoss: 6.118279\n",
      "Epoch : 4 [0/36(0%)]\tLoss: 1.951184\n",
      "Epoch : 4 [4/36(11%)]\tLoss: 1.577984\n",
      "Epoch : 4 [8/36(22%)]\tLoss: 1.678289\n",
      "Epoch : 4 [12/36(33%)]\tLoss: 0.527433\n",
      "Epoch : 4 [16/36(44%)]\tLoss: 1.190358\n",
      "Epoch : 4 [20/36(56%)]\tLoss: 1.935280\n",
      "Epoch : 4 [24/36(67%)]\tLoss: 1.164880\n",
      "Epoch : 4 [28/36(78%)]\tLoss: 1.919948\n",
      "Epoch : 4 [32/36(89%)]\tLoss: 2.428894\n",
      "Epoch : 5 [0/36(0%)]\tLoss: 2.488867\n",
      "Epoch : 5 [4/36(11%)]\tLoss: 3.262023\n",
      "Epoch : 5 [8/36(22%)]\tLoss: 1.541615\n",
      "Epoch : 5 [12/36(33%)]\tLoss: 2.100674\n",
      "Epoch : 5 [16/36(44%)]\tLoss: 1.187463\n",
      "Epoch : 5 [20/36(56%)]\tLoss: 1.697389\n",
      "Epoch : 5 [24/36(67%)]\tLoss: 1.287882\n",
      "Epoch : 5 [28/36(78%)]\tLoss: 1.646257\n",
      "Epoch : 5 [32/36(89%)]\tLoss: 1.771013\n",
      "Epoch : 6 [0/36(0%)]\tLoss: 0.518693\n",
      "Epoch : 6 [4/36(11%)]\tLoss: 0.841204\n",
      "Epoch : 6 [8/36(22%)]\tLoss: 0.508522\n",
      "Epoch : 6 [12/36(33%)]\tLoss: 0.362200\n",
      "Epoch : 6 [16/36(44%)]\tLoss: 1.480759\n",
      "Epoch : 6 [20/36(56%)]\tLoss: 3.007697\n",
      "Epoch : 6 [24/36(67%)]\tLoss: 1.922528\n",
      "Epoch : 6 [28/36(78%)]\tLoss: 1.522995\n",
      "Epoch : 6 [32/36(89%)]\tLoss: 2.033061\n",
      "Epoch : 7 [0/36(0%)]\tLoss: 0.230049\n",
      "Epoch : 7 [4/36(11%)]\tLoss: 0.716538\n",
      "Epoch : 7 [8/36(22%)]\tLoss: 0.486276\n",
      "Epoch : 7 [12/36(33%)]\tLoss: 0.357329\n",
      "Epoch : 7 [16/36(44%)]\tLoss: 0.915678\n",
      "Epoch : 7 [20/36(56%)]\tLoss: 1.926165\n",
      "Epoch : 7 [24/36(67%)]\tLoss: 1.173285\n",
      "Epoch : 7 [28/36(78%)]\tLoss: 1.434052\n",
      "Epoch : 7 [32/36(89%)]\tLoss: 1.779217\n",
      "Epoch : 8 [0/36(0%)]\tLoss: 0.839366\n",
      "Epoch : 8 [4/36(11%)]\tLoss: 1.359515\n",
      "Epoch : 8 [8/36(22%)]\tLoss: 0.459496\n",
      "Epoch : 8 [12/36(33%)]\tLoss: 0.590961\n",
      "Epoch : 8 [16/36(44%)]\tLoss: 0.812750\n",
      "Epoch : 8 [20/36(56%)]\tLoss: 1.875119\n",
      "Epoch : 8 [24/36(67%)]\tLoss: 1.207282\n",
      "Epoch : 8 [28/36(78%)]\tLoss: 1.333973\n",
      "Epoch : 8 [32/36(89%)]\tLoss: 1.730658\n",
      "Epoch : 9 [0/36(0%)]\tLoss: 0.385528\n",
      "Epoch : 9 [4/36(11%)]\tLoss: 0.843140\n",
      "Epoch : 9 [8/36(22%)]\tLoss: 0.441374\n",
      "Epoch : 9 [12/36(33%)]\tLoss: 0.347518\n",
      "Epoch : 9 [16/36(44%)]\tLoss: 1.051812\n",
      "Epoch : 9 [20/36(56%)]\tLoss: 2.250989\n",
      "Epoch : 9 [24/36(67%)]\tLoss: 1.350498\n",
      "Epoch : 9 [28/36(78%)]\tLoss: 1.318907\n",
      "Epoch : 9 [32/36(89%)]\tLoss: 1.736196\n",
      "Epoch : 10 [0/36(0%)]\tLoss: 0.433923\n",
      "Epoch : 10 [4/36(11%)]\tLoss: 0.962020\n",
      "Epoch : 10 [8/36(22%)]\tLoss: 0.403214\n",
      "Epoch : 10 [12/36(33%)]\tLoss: 0.434955\n",
      "Epoch : 10 [16/36(44%)]\tLoss: 0.866781\n",
      "Epoch : 10 [20/36(56%)]\tLoss: 1.939277\n",
      "Epoch : 10 [24/36(67%)]\tLoss: 1.204248\n",
      "Epoch : 10 [28/36(78%)]\tLoss: 1.356030\n",
      "Epoch : 10 [32/36(89%)]\tLoss: 1.725341\n",
      "Epoch : 11 [0/36(0%)]\tLoss: 0.534779\n",
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      "Epoch : 146 [24/36(67%)]\tLoss: 0.284999\n",
      "Epoch : 146 [28/36(78%)]\tLoss: 0.259438\n",
      "Epoch : 146 [32/36(89%)]\tLoss: 0.885424\n",
      "Epoch : 147 [0/36(0%)]\tLoss: 0.292113\n",
      "Epoch : 147 [4/36(11%)]\tLoss: 0.302506\n",
      "Epoch : 147 [8/36(22%)]\tLoss: 0.050570\n",
      "Epoch : 147 [12/36(33%)]\tLoss: 0.370950\n",
      "Epoch : 147 [16/36(44%)]\tLoss: 0.205285\n",
      "Epoch : 147 [20/36(56%)]\tLoss: 0.023955\n",
      "Epoch : 147 [24/36(67%)]\tLoss: 0.287696\n",
      "Epoch : 147 [28/36(78%)]\tLoss: 0.183236\n",
      "Epoch : 147 [32/36(89%)]\tLoss: 0.581991\n",
      "Epoch : 148 [0/36(0%)]\tLoss: 0.198944\n",
      "Epoch : 148 [4/36(11%)]\tLoss: 0.460699\n",
      "Epoch : 148 [8/36(22%)]\tLoss: 0.107921\n",
      "Epoch : 148 [12/36(33%)]\tLoss: 0.486799\n",
      "Epoch : 148 [16/36(44%)]\tLoss: 0.290345\n",
      "Epoch : 148 [20/36(56%)]\tLoss: 0.066388\n",
      "Epoch : 148 [24/36(67%)]\tLoss: 0.287538\n",
      "Epoch : 148 [28/36(78%)]\tLoss: 0.417752\n",
      "Epoch : 148 [32/36(89%)]\tLoss: 0.616971\n",
      "Epoch : 149 [0/36(0%)]\tLoss: 0.167938\n",
      "Epoch : 149 [4/36(11%)]\tLoss: 0.319601\n",
      "Epoch : 149 [8/36(22%)]\tLoss: 0.315520\n",
      "Epoch : 149 [12/36(33%)]\tLoss: 0.327837\n",
      "Epoch : 149 [16/36(44%)]\tLoss: 0.098548\n",
      "Epoch : 149 [20/36(56%)]\tLoss: 0.204136\n",
      "Epoch : 149 [24/36(67%)]\tLoss: 0.313341\n",
      "Epoch : 149 [28/36(78%)]\tLoss: 0.094533\n",
      "Epoch : 149 [32/36(89%)]\tLoss: 0.615084\n",
      "--- 0.11056145032246907 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 = Variable(X_batch).to(device)\n",
    "        var_y_batch = 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)\n",
    "        loss = criterion(output[:,-1,:].squeeze(), var_y_batch) # we only need the last instance from output sequence\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        wandb.log({'train_loss': loss.item()})\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 = Variable(X_batch).to(device)\n",
    "        var_y_batch = Variable(y_batch).to(device)\n",
    "        optimizer.zero_grad()\n",
    "        with torch.no_grad():\n",
    "            output = model(var_X_batch)\n",
    "            loss = criterion(output[:,-1,:].squeeze(), 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": 22,
   "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.semilogy(np.asarray(hist_train), 'b', label=\"Training loss\")\n",
    "plt.semilogy(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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaluate model\n",
    "Now we can evaluate our model with testing set and compare the predictions with the ground truth."
   ]
  },
  {
   "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 = Variable(X_batch).to(device)\n",
    "    var_y_batch = Variable(y_batch).to(device)\n",
    "    optimizer.zero_grad()\n",
    "    with torch.no_grad():\n",
    "        output = model(var_X_batch)\n",
    "        loss = criterion(output[:,-1,:].squeeze(), var_y_batch)\n",
    "    wandb.log({'testing_loss': loss.item()})\n",
    "    predictions.append(output.squeeze().cpu().detach().numpy()[:,-1])\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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the predictions versus ground truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE loss is 0.243\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ground_truth = test_y_torch.squeeze().numpy()\n",
    "\n",
    "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, ground_truth, label=\"Ground truth\")\n",
    "plt.scatter(instances, [ground_truth.mean()] * len(instances), label=\"Mean of ground truth\")\n",
    "plt.xlabel(\"Experiment\")\n",
    "plt.ylabel(\"TS\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Evaluate predictions with baseline model\n",
    "To better evaluate the predictions, we perform a linear regression (ridge) with dimensionality reduction as baseline.\n",
    "\n",
    "Train-test split will be performed based on the previous split, which can be refered to as \"inner cross-validation layer\". <br>\n",
    "For each split, we will perform dimensionality reduction and fit the model. The best model will be selected based on the mean squared error."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with importing and initializing the response guided dimensionality reduction (RGDR) operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from s2spy import RGDR\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "# cross-validation with Kfold\n",
    "k_fold_splits = 5\n",
    "kfold = KFold(n_splits=k_fold_splits)\n",
    "cv = lilio.traintest.TrainTestSplit(kfold)\n",
    "\n",
    "# create lists for saving models and predictions\n",
    "models = []\n",
    "RGDRs = []\n",
    "rmse_train = []\n",
    "rmse_test = []\n",
    "train_test_splits = []\n",
    "\n",
    "# prepare operator for dimensionality reduction\n",
    "target_intervals = 1\n",
    "lag = 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can perform dimentionality reduction and train ridge model for each split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# cross validation based dimensionality reduction and model training\n",
    "for x_train, x_test, y_train, y_test in cv.split(precursor_field_sel[:-test_samples],\n",
    "                                                 y=target_series_sel[:-test_samples]):\n",
    "    # log train/test splits with anchor years\n",
    "    train_test_splits.append({\n",
    "        \"train\": x_train.anchor_year.values,\n",
    "        \"test\": x_test.anchor_year.values,\n",
    "    })\n",
    "    # fit dimensionality reduction operator RGDR\n",
    "    rgdr = RGDR(\n",
    "        target_intervals=target_intervals,\n",
    "        lag=lag,\n",
    "        eps_km=600,\n",
    "        alpha=0.05,\n",
    "        min_area_km2=0\n",
    "    )\n",
    "    rgdr.fit(x_train, y_train)\n",
    "    # save dimensionality reduction operator\n",
    "    RGDRs.append(rgdr)\n",
    "    # transform to train and test data\n",
    "    clusters_train = rgdr.transform(x_train)\n",
    "    clusters_test = rgdr.transform(x_test)\n",
    "    # train model\n",
    "    ridge = Ridge(alpha=1.0)\n",
    "    model = ridge.fit(clusters_train.isel(i_interval=0), y_train.sel(i_interval=1))\n",
    "    # save model\n",
    "    models.append(model)\n",
    "    # predict and save results\n",
    "    prediction = model.predict(clusters_test.isel(i_interval=0))\n",
    "    # calculate and save rmse\n",
    "    rmse_train.append(mean_squared_error(y_train.sel(i_interval=1),\n",
    "                                         model.predict(clusters_train.isel(i_interval=0))))\n",
    "    rmse_test.append(mean_squared_error(y_test.sel(i_interval=1),\n",
    "                                        prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "plt.plot(range(k_fold_splits), rmse_train, \"b--\", label = \"train loss\")\n",
    "plt.plot(range(k_fold_splits), rmse_test, \"r\", label = \"test loss\")\n",
    "ax = fig.gca()\n",
    "ax.set_xticks(range(k_fold_splits))\n",
    "plt.xlabel(\"Experiment\")\n",
    "plt.ylabel(\"RMSE\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot, we can pick up the model with the smallest error. We will use that one as baseline and compare it with predictions from LSTM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dimensionality reduction with the RGDR operator used by the best model\n",
    "clusters_test = RGDRs[np.argmax(rmse_test)].transform(precursor_field_sel[-test_samples:])\n",
    "# predict with the best model\n",
    "predictions_baseline = models[np.argmax(rmse_test)].predict(clusters_test.isel(i_interval=0))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot all predictions and the ground truth and check the errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE of LSTM forecasts is 0.973\n",
      "The MSE of baseline ridge forecasts is 1.798\n",
      "The MSE of mean of training data is 97.538\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\n",
    "    f\"The MSE of LSTM forecasts is {mean_squared_error(ground_truth, np.concatenate(predictions)):.3f}\"\n",
    ")\n",
    "print(\n",
    "    f\"The MSE of baseline ridge forecasts is {mean_squared_error(ground_truth, predictions_baseline):.3f}\"\n",
    ")\n",
    "print(\n",
    "    f\"The MSE of mean of training data is {mean_squared_error(ground_truth, [target_series_sel[:-test_samples].mean()] * len(instances)):.3f}\"\n",
    ")\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.scatter(instances, np.concatenate(predictions), label=\"Predictions-LSTM\")\n",
    "plt.scatter(instances, ground_truth, label=\"Ground truth\")\n",
    "plt.scatter(instances, [ground_truth.mean()] * len(instances), label=\"Mean of ground truth\")\n",
    "plt.scatter(instances, predictions_baseline, label=\"Predictions-Ridge\")\n",
    "plt.xlabel(\"Experiment\")\n",
    "plt.ylabel(\"TS\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ai4s2s",
   "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
}