workflow/pred_temperature_ridge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict 2 meter temperature with sea surface temperature using linear regression\n",
    "This notebook serves as an example of a basic workflow of data driven forecasting using machine learning with `s2spy` & `lilio` packages. <br>\n",
    "We will predict temperature in the southwestern US at seasonal time scales using ERA5 dataset with linear regression (Ridge). <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",
    "1. [Define a calendar (`lilio`)](#1---define-a-calendar-with-lilio)\n",
    "2. [Download/load input data (test data, accessible via `era5cli`)](#2---load-datasets)\n",
    "3. [Map the calendar to the data (`lilio`)](#3---map-the-calendar-to-the-data)\n",
    "4. [Train-test split (70%/30%)](#4---train-test-split-based-on-the-anchor-years-7030-split)\n",
    "5. [Preprocessing based on the training set (`s2spy`)](#5---fit-preprocessor-with-training-samples-and-preprocess-data)\n",
    "6. [Resample data to the calendar (`lilio`)](#6---resample-data-to-the-calendar)\n",
    "7. [Dimensionality reduction, model fitting and cross-validation (`lilio` & `scikit-learn`)](#7---train-test-split---dimensionality-reduction--model-fitting)\n",
    "8. [Training the model](#8---train-the-model-on-all-training-data)\n",
    "9. [Testing the model](#9---test-the-model-with-test-data)\n",
    "10. [Saving the trained model (`onnx`)](#10---save-model-to-onnx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The workflow is illustrated below:\n",
    "\n",
    "<img src=\"../assets/regression.PNG\" alt=\"Ridge\" width=\"900\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0 - Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lilio\n",
    "import urllib\n",
    "import xarray as xr\n",
    "from s2spy import preprocess\n",
    "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",
    "from skl2onnx import to_onnx\n",
    "import numpy as np\n",
    "import onnxruntime as ort"
   ]
  },
  {
   "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"
   ]
  },
  {
   "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)"
   ]
  },
  {
   "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": [
    "calendar.map_to_data(precursor_field)\n",
    "calendar.visualize(show_length=True)"
   ]
  },
  {
   "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",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>[2017-04-01, 2017-05-01)</td>\n",
       "      <td>[2017-06-01, 2017-07-01)</td>\n",
       "      <td>[2017-08-01, 2017-09-01)</td>\n",
       "      <td>[2017-10-01, 2017-11-01)</td>\n",
       "      <td>[2017-12-01, 2018-01-01)</td>\n",
       "      <td>[2018-02-01, 2018-03-01)</td>\n",
       "      <td>[2018-04-01, 2018-05-01)</td>\n",
       "      <td>[2018-06-01, 2018-07-01)</td>\n",
       "      <td>[2018-08-01, 2018-08-31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>[2016-04-01, 2016-05-01)</td>\n",
       "      <td>[2016-06-01, 2016-07-01)</td>\n",
       "      <td>[2016-08-01, 2016-09-01)</td>\n",
       "      <td>[2016-10-01, 2016-11-01)</td>\n",
       "      <td>[2016-12-01, 2017-01-01)</td>\n",
       "      <td>[2017-02-01, 2017-03-01)</td>\n",
       "      <td>[2017-04-01, 2017-05-01)</td>\n",
       "      <td>[2017-06-01, 2017-07-01)</td>\n",
       "      <td>[2017-08-01, 2017-08-31)</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",
       "2018         [2017-04-01, 2017-05-01)  [2017-06-01, 2017-07-01)   \n",
       "2017         [2016-04-01, 2016-05-01)  [2016-06-01, 2016-07-01)   \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",
       "2018         [2017-08-01, 2017-09-01)  [2017-10-01, 2017-11-01)   \n",
       "2017         [2016-08-01, 2016-09-01)  [2016-10-01, 2016-11-01)   \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",
       "2018         [2017-12-01, 2018-01-01)  [2018-02-01, 2018-03-01)   \n",
       "2017         [2016-12-01, 2017-01-01)  [2017-02-01, 2017-03-01)   \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",
       "2018         [2018-04-01, 2018-05-01)  [2018-06-01, 2018-07-01)   \n",
       "2017         [2017-04-01, 2017-05-01)  [2017-06-01, 2017-07-01)   \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)  \n",
       "2018         [2018-08-01, 2018-08-31)  \n",
       "2017         [2017-08-01, 2017-08-31)  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show first 5 anchor years in the calendar\n",
    "calendar.show()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 - Train-test split based on the anchor years (70%/30% split)\n",
    "This can be refered to as the \"outer cross-validation layer\"."
   ]
  },
  {
   "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 70% of instance as training\n",
    "    train_samples = round(len(years) * 0.7)\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 -1)))\n",
    "\n",
    "    return train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_slice, test_slice = train_test_split(calendar, precursor_field)\n",
    "target_train_slice, target_test_slice = train_test_split(calendar, target_field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calendar.map_to_data(train_slice)\n",
    "calendar.visualize(show_length=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 - Fit preprocessor with training samples 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."
   ]
  },
  {
   "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"
     ]
    }
   ],
   "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(train_slice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_prep = preprocessor.transform(train_slice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6 - Resample data to the calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;data&#x27; (anchor_year: 42, i_interval: 9, latitude: 5,\n",
       "                          longitude: 13)&gt; Size: 197kB\n",
       "-0.3489 -0.4872 -0.3651 -0.1782 -0.528 ... -0.2286 -0.4388 -0.5503 -0.3707\n",
       "Coordinates:\n",
       "  * anchor_year  (anchor_year) int64 336B 1961 1962 1963 1964 ... 2000 2001 2002\n",
       "  * i_interval   (i_interval) int64 72B -8 -7 -6 -5 -4 -3 -2 -1 1\n",
       "  * longitude    (longitude) float64 104B 177.5 182.5 187.5 ... 232.5 237.5\n",
       "  * latitude     (latitude) float64 40B 47.5 42.5 37.5 32.5 27.5\n",
       "    left_bound   (anchor_year, i_interval) datetime64[ns] 3kB 1960-04-01 ... ...\n",
       "    right_bound  (anchor_year, i_interval) datetime64[ns] 3kB 1960-05-01 ... ...\n",
       "    is_target    (i_interval) bool 9B False False False ... False False True\n",
       "Attributes: (4)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'data'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>anchor_year</span>: 42</li><li><span class='xr-has-index'>i_interval</span>: 9</li><li><span class='xr-has-index'>latitude</span>: 5</li><li><span class='xr-has-index'>longitude</span>: 13</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-720c26cf-6624-4497-9584-9cdabfc4d585' class='xr-array-in' type='checkbox' ><label for='section-720c26cf-6624-4497-9584-9cdabfc4d585' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.3489 -0.4872 -0.3651 -0.1782 ... -0.2286 -0.4388 -0.5503 -0.3707</span></div><div class='xr-array-data'><pre>array([[[[-3.48891279e-01, -4.87247507e-01, -3.65085574e-01, ...,\n",
       "           3.76103632e-01,  2.11263347e-01,             nan],\n",
       "         [-1.19786722e+00, -8.47043191e-01, -8.33155979e-01, ...,\n",
       "           1.00031440e-01,  4.15586007e-01,             nan],\n",
       "         [-1.86643100e-01, -6.89918966e-01, -5.91849268e-01, ...,\n",
       "           1.18475440e-01,  6.22708808e-01,             nan],\n",
       "         [ 4.46762394e-01, -2.81852392e-01, -2.63694235e-01, ...,\n",
       "           9.70132442e-02,  3.89226989e-01,  4.43100913e-01],\n",
       "         [ 9.09136376e-01,  4.04767483e-01, -1.93544316e-01, ...,\n",
       "           2.87446860e-01,  6.98855127e-01,  7.22394631e-01]],\n",
       "\n",
       "        [[-5.29343424e-01, -6.78461895e-01, -8.09818620e-01, ...,\n",
       "           3.40722173e-01,  4.66558031e-01,             nan],\n",
       "         [-1.52794099e+00, -1.43985938e+00, -1.41322633e+00, ...,\n",
       "          -3.35928705e-02,  3.98051399e-01,             nan],\n",
       "         [-9.40379386e-01, -7.03313298e-01, -8.65325155e-01, ...,\n",
       "           3.13786177e-01, -7.32293912e-02,             nan],\n",
       "         [-8.65427742e-01, -4.75387065e-01,  8.65176474e-02, ...,\n",
       "           2.75132504e-01, -6.62072680e-02,  8.16046468e-02],\n",
       "         [ 6.82256818e-01,  7.38440743e-01,  5.62020434e-01, ...,\n",
       "...\n",
       "           4.55262978e-02, -5.27707264e-01,             nan],\n",
       "         [ 4.10107295e-01, -9.76260822e-02,  2.05809578e-02, ...,\n",
       "           2.58960046e-01, -8.52817173e-02,             nan],\n",
       "         [ 6.48029126e-01,  2.53901198e-01,  3.77086851e-02, ...,\n",
       "           8.39693083e-05, -9.43558441e-01,             nan],\n",
       "         [ 3.31345473e-01, -1.26539776e-02, -2.44078809e-01, ...,\n",
       "          -1.91307852e-01, -7.83378417e-01, -9.48998325e-01],\n",
       "         [ 4.47053149e-01, -2.40257033e-02, -1.30256984e-01, ...,\n",
       "          -6.20565723e-01, -5.85451369e-01, -4.61605703e-01]],\n",
       "\n",
       "        [[-6.42990943e-01, -5.19604146e-01, -6.81941964e-01, ...,\n",
       "           6.81335228e-01, -9.14699771e-01,             nan],\n",
       "         [-5.56056560e-01, -1.04693235e+00, -8.81313533e-01, ...,\n",
       "           3.51343833e-01, -1.24980226e-01,             nan],\n",
       "         [-2.27695386e-02, -1.13106194e+00, -1.00771036e+00, ...,\n",
       "          -1.31864612e-01, -7.37750017e-01,             nan],\n",
       "         [ 5.61024438e-01,  7.87387399e-01,  9.53788306e-01, ...,\n",
       "          -2.32044386e-01, -5.49654553e-01, -3.67964277e-01],\n",
       "         [ 1.45872746e+00,  1.29229119e+00,  9.10479275e-01, ...,\n",
       "          -4.38808459e-01, -5.50251103e-01, -3.70679846e-01]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-c436953b-82c0-43d0-ba9a-b49eed67969b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c436953b-82c0-43d0-ba9a-b49eed67969b' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>anchor_year</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1961 1962 1963 ... 2000 2001 2002</div><input id='attrs-7b4b300d-9ff5-4ed4-bb26-663f68a3b8d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b4b300d-9ff5-4ed4-bb26-663f68a3b8d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15e8b0cc-43e4-4d03-802f-68d87554be90' class='xr-var-data-in' type='checkbox'><label for='data-15e8b0cc-43e4-4d03-802f-68d87554be90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>anchor year</dd><dt><span>units :</span></dt><dd>year</dd><dt><span>description :</span></dt><dd>The anchor date is the start of the period you want to forecast, and is an abstract date and does not include a year. Anchor years are used to create a full date with the anchor date (here: 08-01).</dd></dl></div><div class='xr-var-data'><pre>array([1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972,\n",
       "       1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984,\n",
       "       1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "       1997, 1998, 1999, 2000, 2001, 2002])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_interval</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-8 -7 -6 -5 -4 -3 -2 -1 1</div><input id='attrs-d349a57a-35f9-4d70-ace4-dc7fbaa41aa6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d349a57a-35f9-4d70-ace4-dc7fbaa41aa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1af48366-6b36-4562-90c3-a19e957e7b0b' class='xr-var-data-in' type='checkbox'><label for='data-1af48366-6b36-4562-90c3-a19e957e7b0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>interval index</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>description :</span></dt><dd>The index of each Lilio Calendar interval. Positive values denote intervals after the anchor date (targets), while negative values represent intervals before the anchor date (precursors).</dd></dl></div><div class='xr-var-data'><pre>array([-8, -7, -6, -5, -4, -3, -2, -1,  1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>177.5 182.5 187.5 ... 232.5 237.5</div><input id='attrs-65dc0fd7-2d75-458b-bfd6-a6a73e2c7f3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65dc0fd7-2d75-458b-bfd6-a6a73e2c7f3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db05ed56-a7d4-4e15-8bed-a34f6acdfccf' class='xr-var-data-in' type='checkbox'><label for='data-db05ed56-a7d4-4e15-8bed-a34f6acdfccf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([177.5, 182.5, 187.5, 192.5, 197.5, 202.5, 207.5, 212.5, 217.5, 222.5,\n",
       "       227.5, 232.5, 237.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.5 42.5 37.5 32.5 27.5</div><input id='attrs-e1371be6-298d-48ab-aa8e-ae36801eeda1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1371be6-298d-48ab-aa8e-ae36801eeda1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8081d124-2a1a-4f10-9a35-743f3b013e0f' class='xr-var-data-in' type='checkbox'><label for='data-8081d124-2a1a-4f10-9a35-743f3b013e0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([47.5, 42.5, 37.5, 32.5, 27.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>left_bound</span></div><div class='xr-var-dims'>(anchor_year, i_interval)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-04-01 ... 2002-08-01</div><input id='attrs-711989d9-8c19-4ca7-9722-a38c25b97bbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-711989d9-8c19-4ca7-9722-a38c25b97bbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2518bd5a-a78a-4a4b-a051-db1421761a87' class='xr-var-data-in' type='checkbox'><label for='data-2518bd5a-a78a-4a4b-a051-db1421761a87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>Left bound of the interval</dd><dt><span>closed :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;1960-04-01T00:00:00.000000000&#x27;, &#x27;1960-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1960-08-01T00:00:00.000000000&#x27;, &#x27;1960-10-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1960-12-01T00:00:00.000000000&#x27;, &#x27;1961-02-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1961-04-01T00:00:00.000000000&#x27;, &#x27;1961-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1961-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1961-04-01T00:00:00.000000000&#x27;, &#x27;1961-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1961-08-01T00:00:00.000000000&#x27;, &#x27;1961-10-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1961-12-01T00:00:00.000000000&#x27;, &#x27;1962-02-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1962-04-01T00:00:00.000000000&#x27;, &#x27;1962-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1962-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1962-04-01T00:00:00.000000000&#x27;, &#x27;1962-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1962-08-01T00:00:00.000000000&#x27;, &#x27;1962-10-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1962-12-01T00:00:00.000000000&#x27;, &#x27;1963-02-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1963-04-01T00:00:00.000000000&#x27;, &#x27;1963-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1963-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1963-04-01T00:00:00.000000000&#x27;, &#x27;1963-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1963-08-01T00:00:00.000000000&#x27;, &#x27;1963-10-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1963-12-01T00:00:00.000000000&#x27;, &#x27;1964-02-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1964-04-01T00:00:00.000000000&#x27;, &#x27;1964-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1964-08-01T00:00:00.000000000&#x27;],\n",
       "...\n",
       "       [&#x27;1998-04-01T00:00:00.000000000&#x27;, &#x27;1998-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1998-08-01T00:00:00.000000000&#x27;, &#x27;1998-10-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1998-12-01T00:00:00.000000000&#x27;, &#x27;1999-02-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1999-04-01T00:00:00.000000000&#x27;, &#x27;1999-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1999-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1999-04-01T00:00:00.000000000&#x27;, &#x27;1999-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1999-08-01T00:00:00.000000000&#x27;, &#x27;1999-10-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1999-12-01T00:00:00.000000000&#x27;, &#x27;2000-02-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2000-04-01T00:00:00.000000000&#x27;, &#x27;2000-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2000-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2000-04-01T00:00:00.000000000&#x27;, &#x27;2000-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2000-08-01T00:00:00.000000000&#x27;, &#x27;2000-10-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2000-12-01T00:00:00.000000000&#x27;, &#x27;2001-02-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2001-04-01T00:00:00.000000000&#x27;, &#x27;2001-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2001-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2001-04-01T00:00:00.000000000&#x27;, &#x27;2001-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2001-08-01T00:00:00.000000000&#x27;, &#x27;2001-10-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2001-12-01T00:00:00.000000000&#x27;, &#x27;2002-02-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2002-04-01T00:00:00.000000000&#x27;, &#x27;2002-06-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2002-08-01T00:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>right_bound</span></div><div class='xr-var-dims'>(anchor_year, i_interval)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-05-01 ... 2002-08-31</div><input id='attrs-b6f2d480-a21a-4ff9-bf72-754fd9139154' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6f2d480-a21a-4ff9-bf72-754fd9139154' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f51e3ca8-df27-401b-9b0b-8ea4f1e281b7' class='xr-var-data-in' type='checkbox'><label for='data-f51e3ca8-df27-401b-9b0b-8ea4f1e281b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>Right bound of the interval</dd><dt><span>closed :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;1960-05-01T00:00:00.000000000&#x27;, &#x27;1960-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1960-09-01T00:00:00.000000000&#x27;, &#x27;1960-11-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1961-01-01T00:00:00.000000000&#x27;, &#x27;1961-03-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1961-05-01T00:00:00.000000000&#x27;, &#x27;1961-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1961-08-31T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1961-05-01T00:00:00.000000000&#x27;, &#x27;1961-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1961-09-01T00:00:00.000000000&#x27;, &#x27;1961-11-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1962-01-01T00:00:00.000000000&#x27;, &#x27;1962-03-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1962-05-01T00:00:00.000000000&#x27;, &#x27;1962-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1962-08-31T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1962-05-01T00:00:00.000000000&#x27;, &#x27;1962-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1962-09-01T00:00:00.000000000&#x27;, &#x27;1962-11-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1963-01-01T00:00:00.000000000&#x27;, &#x27;1963-03-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1963-05-01T00:00:00.000000000&#x27;, &#x27;1963-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1963-08-31T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1963-05-01T00:00:00.000000000&#x27;, &#x27;1963-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1963-09-01T00:00:00.000000000&#x27;, &#x27;1963-11-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1964-01-01T00:00:00.000000000&#x27;, &#x27;1964-03-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1964-05-01T00:00:00.000000000&#x27;, &#x27;1964-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1964-08-31T00:00:00.000000000&#x27;],\n",
       "...\n",
       "       [&#x27;1998-05-01T00:00:00.000000000&#x27;, &#x27;1998-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1998-09-01T00:00:00.000000000&#x27;, &#x27;1998-11-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1999-01-01T00:00:00.000000000&#x27;, &#x27;1999-03-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1999-05-01T00:00:00.000000000&#x27;, &#x27;1999-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1999-08-31T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1999-05-01T00:00:00.000000000&#x27;, &#x27;1999-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;1999-09-01T00:00:00.000000000&#x27;, &#x27;1999-11-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-03-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2000-05-01T00:00:00.000000000&#x27;, &#x27;2000-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2000-08-31T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2000-05-01T00:00:00.000000000&#x27;, &#x27;2000-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2000-09-01T00:00:00.000000000&#x27;, &#x27;2000-11-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2001-01-01T00:00:00.000000000&#x27;, &#x27;2001-03-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2001-05-01T00:00:00.000000000&#x27;, &#x27;2001-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2001-08-31T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2001-05-01T00:00:00.000000000&#x27;, &#x27;2001-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2001-09-01T00:00:00.000000000&#x27;, &#x27;2001-11-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-03-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2002-05-01T00:00:00.000000000&#x27;, &#x27;2002-07-01T00:00:00.000000000&#x27;,\n",
       "        &#x27;2002-08-31T00:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>is_target</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False True</div><input id='attrs-e3ad228c-4348-4e3b-b441-d08e92a2952a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3ad228c-4348-4e3b-b441-d08e92a2952a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bc99972-f831-4ed1-b00d-d443246402d0' class='xr-var-data-in' type='checkbox'><label for='data-2bc99972-f831-4ed1-b00d-d443246402d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>Target flag</dd><dt><span>description :</span></dt><dd>Denotes if an interval was marked as a target interval in the Lilio Calendar.</dd></dl></div><div class='xr-var-data'><pre>array([False, False, False, False, False, False, False, False,  True])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e06fa780-d341-4a96-9a6c-636cdc0c468f' class='xr-section-summary-in' type='checkbox'  ><label for='section-e06fa780-d341-4a96-9a6c-636cdc0c468f' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0c03a1b9-6b99-4351-9fcf-2fa87e9edc75' class='xr-index-data-in' type='checkbox'/><label for='index-0c03a1b9-6b99-4351-9fcf-2fa87e9edc75' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([177.5, 182.5, 187.5, 192.5, 197.5, 202.5, 207.5, 212.5, 217.5, 222.5,\n",
       "       227.5, 232.5, 237.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7481c39b-db41-4a14-b0b7-61dcd53241a6' class='xr-index-data-in' type='checkbox'/><label for='index-7481c39b-db41-4a14-b0b7-61dcd53241a6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([47.5, 42.5, 37.5, 32.5, 27.5], dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>anchor_year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-28299e03-f552-466d-87e6-d9814a07e1be' class='xr-index-data-in' type='checkbox'/><label for='index-28299e03-f552-466d-87e6-d9814a07e1be' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972,\n",
       "       1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984,\n",
       "       1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "       1997, 1998, 1999, 2000, 2001, 2002],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;anchor_year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i_interval</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-53a3875c-1b2a-4d2a-a589-5344a806b397' class='xr-index-data-in' type='checkbox'/><label for='index-53a3875c-1b2a-4d2a-a589-5344a806b397' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-8, -7, -6, -5, -4, -3, -2, -1, 1], dtype=&#x27;int64&#x27;, name=&#x27;i_interval&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0d66fe1-6bf1-4d6b-8838-240e428c94cc' class='xr-section-summary-in' type='checkbox'  ><label for='section-b0d66fe1-6bf1-4d6b-8838-240e428c94cc' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>lilio_version :</span></dt><dd>0.5.0</dd><dt><span>lilio_calendar_anchor_date :</span></dt><dd>08-01</dd><dt><span>lilio_calendar_code :</span></dt><dd>Calendar(\n",
       "    anchor=&#x27;08-01&#x27;,\n",
       "    allow_overlap=True,\n",
       "    mapping=(&#x27;data&#x27;, Timestamp(&#x27;1960-01-01 00:00:00&#x27;), Timestamp(&#x27;2002-12-31 00:00:00&#x27;)),\n",
       "    intervals=[\n",
       "        Interval(role=&#x27;target&#x27;, length=&#x27;30d&#x27;, gap=&#x27;0d&#x27;),\n",
       "        Interval(role=&#x27;precursor&#x27;, length=&#x27;1M&#x27;, gap=&#x27;1M&#x27;),\n",
       "        Interval(role=&#x27;precursor&#x27;, length=&#x27;1M&#x27;, gap=&#x27;1M&#x27;),\n",
       "        Interval(role=&#x27;precursor&#x27;, length=&#x27;1M&#x27;, gap=&#x27;1M&#x27;),\n",
       "        Interval(role=&#x27;precursor&#x27;, length=&#x27;1M&#x27;, gap=&#x27;1M&#x27;),\n",
       "        Interval(role=&#x27;precursor&#x27;, length=&#x27;1M&#x27;, gap=&#x27;1M&#x27;),\n",
       "        Interval(role=&#x27;precursor&#x27;, length=&#x27;1M&#x27;, gap=&#x27;1M&#x27;),\n",
       "        Interval(role=&#x27;precursor&#x27;, length=&#x27;1M&#x27;, gap=&#x27;1M&#x27;),\n",
       "        Interval(role=&#x27;precursor&#x27;, length=&#x27;1M&#x27;, gap=&#x27;1M&#x27;)\n",
       "    ]\n",
       ")</dd><dt><span>history :</span></dt><dd>2024-11-15 09:59:49 UTC - Resampled with a Lilio calendar. See: https://github.com/AI4S2S/lilio\n",
       "</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'data' (anchor_year: 42, i_interval: 9, latitude: 5,\n",
       "                          longitude: 13)> Size: 197kB\n",
       "-0.3489 -0.4872 -0.3651 -0.1782 -0.528 ... -0.2286 -0.4388 -0.5503 -0.3707\n",
       "Coordinates:\n",
       "  * anchor_year  (anchor_year) int64 336B 1961 1962 1963 1964 ... 2000 2001 2002\n",
       "  * i_interval   (i_interval) int64 72B -8 -7 -6 -5 -4 -3 -2 -1 1\n",
       "  * longitude    (longitude) float64 104B 177.5 182.5 187.5 ... 232.5 237.5\n",
       "  * latitude     (latitude) float64 40B 47.5 42.5 37.5 32.5 27.5\n",
       "    left_bound   (anchor_year, i_interval) datetime64[ns] 3kB 1960-04-01 ... ...\n",
       "    right_bound  (anchor_year, i_interval) datetime64[ns] 3kB 1960-05-01 ... ...\n",
       "    is_target    (i_interval) bool 9B False False False ... False False True\n",
       "Attributes: (4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.set_options(display_expand_data = False, display_expand_attrs = False)\n",
    "\n",
    "precursor_field_resample = lilio.resample(calendar, train_prep)\n",
    "target_field_resample = lilio.resample(calendar, target_train_slice)\n",
    "\n",
    "precursor_field_resample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7 - Train-test split -> dimensionality reduction & model fitting\n",
    "Train-test split based on the previous split, which can be refered to as \"inner cross-validation layer\" (outer cv loop -> inner cv loop). <br>\n",
    "For each split, we will perform dimensionality reduction and fit the model.\n",
    "\n",
    "For simplicity, in this example we only perform dimensionality reduction with RGDR with only one target interval and one lag. But in practice you can have multiple target intervals and multiple lags. <br>\n",
    "For more information, here is a nice example about multiple target intervals in the [tutorial notebook](https://github.com/AI4S2S/s2spy/blob/main/docs/notebooks/tutorial_RGDR.ipynb) of RGDR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    "predictions = []\n",
    "rmse_train = []\n",
    "rmse_test = []\n",
    "train_test_splits = []\n",
    "\n",
    "# prepare operator for dimensionality reduction\n",
    "target_intervals = 1\n",
    "lag = 1\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",
    "\n",
    "# cross validation based dimensionality reduction and model training\n",
    "for x_train, x_test, y_train, y_test in cv.split(precursor_field_resample, y=target_field_resample):\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.fit(x_train, y_train)\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",
    "    predictions.append(prediction)\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": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the RMSE for both training and testing for each experiment (split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8 - Train the model on all training data\n",
    "\n",
    "Next, we can train the model on the entire training dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model on all training data\n",
    "\n",
    "rgdr.fit(precursor_field_resample, target_field_resample)\n",
    "\n",
    "# transform to data\n",
    "clusters = rgdr.transform(precursor_field_resample)\n",
    "\n",
    "# train model\n",
    "ridge = Ridge(alpha=1.0)\n",
    "model = ridge.fit(clusters.isel(i_interval=0), target_field_resample.sel(i_interval=1))\n",
    "\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [-0.45365665 -0.25226673 -0.22953138  0.36843263]\n",
      "Intercept: 296.1227898053248\n"
     ]
    }
   ],
   "source": [
    "print(\"Coefficients:\", ridge.coef_)\n",
    "print(\"Intercept:\", ridge.intercept_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9 - Test the model with test data \n",
    "\n",
    "Now we can prepare the test dataset to make predictions. First we map the test data to the calendar and create clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare test data\n",
    "calendar.map_to_data(test_slice)\n",
    "test_prep = preprocessor.transform(test_slice)\n",
    "precursor_test_resample = lilio.resample(calendar, test_prep)\n",
    "target_test_resample = lilio.resample(calendar, target_test_slice)\n",
    "\n",
    "clusters_test = rgdr.transform(precursor_test_resample)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For illustration purposes, we can plot the RGDR correlation map as well as the clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2908b1670>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(figsize=(10, 3), ncols=2)\n",
    "\n",
    "# Visualize correlation map after RGDR().fit(precursor_field, target_timeseries)\n",
    "rgdr.corr_map.plot(ax=ax1)\n",
    "\n",
    "# Visualize p-values map\n",
    "rgdr.pval_map.plot(ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2937be3d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rgdr.cluster_map.plot(cmap='RdYlBu', vmin=-2, vmax=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can make predictions and evaluate the performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE: 0.6636285049769847\n",
      "Test MSE: 1.1147914984210299\n"
     ]
    }
   ],
   "source": [
    "# Predict on the training set\n",
    "y_train_pred = ridge.predict(clusters.isel(i_interval=0))\n",
    "\n",
    "# Predict on the test set\n",
    "y_test_pred = ridge.predict(clusters_test.isel(i_interval=0))\n",
    "\n",
    "# Evaluate performance\n",
    "mse_train = mean_squared_error(target_field_resample.sel(i_interval=1), y_train_pred)\n",
    "mse_test = mean_squared_error(target_test_resample.sel(i_interval=1), y_test_pred)\n",
    "\n",
    "print(\"Training MSE:\", mse_train)\n",
    "print(\"Test MSE:\", mse_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 - Save model to ONNX\n",
    "\n",
    "Finally, let's save the model to [onnx](https://onnx.ai/), which is an open format built to represent machine learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save model to ONNX \n",
    "onnx_model = to_onnx(model, clusters.isel(i_interval=0)[:1].values.astype(np.float32), target_opset=12)\n",
    "# save model\n",
    "with open(\"./model.onnx\", \"wb\") as f:\n",
    "    f.write(onnx_model.SerializeToString())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[295.24966],\n",
       "       [296.19693],\n",
       "       [295.97415],\n",
       "       [296.27057],\n",
       "       [295.411  ],\n",
       "       [294.8868 ],\n",
       "       [297.02994],\n",
       "       [297.16443],\n",
       "       [296.9564 ],\n",
       "       [295.50986],\n",
       "       [294.83182],\n",
       "       [295.13818],\n",
       "       [295.96295],\n",
       "       [296.5652 ],\n",
       "       [296.2868 ],\n",
       "       [295.16876],\n",
       "       [296.08533]], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the ONNX model\n",
    "model_path = 'model.onnx'\n",
    "session = ort.InferenceSession(model_path)\n",
    "pred_ort = session.run(None, {\"X\": clusters_test.isel(i_interval=0).values.astype(np.float32)})[0]\n",
    "\n",
    "pred_ort\n"
   ]
  }
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