{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "12a23686",
   "metadata": {},
   "source": [
    "# Including teleconnection indices as predictors\n",
    "\n",
    "In this tutorial, we demonstrate how to set the atmospheric circulation indices included in the pyESD package as predictor for developing downscaling model. Specifically, in this notebook, we rely on the *Recursive* predictor selection method for the predictor selection and used *LassoLarsCV* as the learning model to evaluate the performance of the models generated with and without the indices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e026af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all the models required\n",
    "import os \n",
    "import sys \n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "from collections import OrderedDict\n",
    "import socket\n",
    "\n",
    "# modules related to pyESD\n",
    "\n",
    "from pyESD.Weatherstation import read_station_csv\n",
    "from pyESD.standardizer import MonthlyStandardizer\n",
    "from pyESD.ESD_utils import store_pickle, store_csv\n",
    "from pyESD.splitter import KFold\n",
    "from pyESD.ESD_utils import Dataset\n",
    "from pyESD.Weatherstation import read_weatherstationnames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b7c18e",
   "metadata": {},
   "source": [
    "### Data repositories\n",
    "To avoid the repetition of the paths to the predictors and predictand datasets, we have included them in a script that can easily be imported in all the notebooks\n",
    "1. The ERA5 datasets loaded with the Dataset module implemented in the ```pyESD.ESD_utils```\n",
    "2. The constructed time series of the predictors are stored in a pickle file to avoid loading them again on the next run. We set the predictordir to store the datasets\n",
    "3. We use the ```read_weatherstationnames``` to generated a list of all the weather station names that would be used to create the weather station objects\n",
    "4. We import all these variable as ```from read_data import * ``` which is this file: [read_data.py](./read_data.py)\n",
    "\n",
    "All the weather stations are stored in [data](./data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393c7c1e",
   "metadata": {},
   "source": [
    "### Predictors setting \n",
    "\n",
    "1. list of predictors to be loaded. Note that these names are the variables names stored in the netCDF files containing the predictors datasets\n",
    "2. set the date range that would be used to selected the predictors. Here it should be the same as the training and validation period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf6c941c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the predictors that includes the teleconnection indices \n",
    "\n",
    "predictors_with_indices = [\"NAO\", \"EA\", \"SCAN\", \"EAWR\", \"t2m\", \"tp\",\"msl\", \"v10\", \"u10\", \n",
    "              \"u250\", \"u850\", \"u500\",\"u700\", \"u1000\",\"v250\", \"v850\", \"v500\",\"v700\", \"v1000\",\n",
    "              \"r250\", \"r850\", \"r500\",\"r700\", \"r1000\", \"z250\", \"z500\", \"z700\", \"z850\", \"z1000\", \n",
    "              \"t250\", \"t850\", \"t500\",\"t700\", \"t1000\",\"dtd250\", \"dtd850\", \"dtd500\",\"dtd700\", \"dtd1000\"\n",
    "              ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "71116efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the predictors without teleconnection indices\n",
    "predictors_without = [\"t2m\", \"tp\",\"msl\", \"v10\", \"u10\", \n",
    "              \"u250\", \"u850\", \"u500\",\"u700\", \"u1000\",\"v250\", \"v850\", \"v500\",\"v700\", \"v1000\",\n",
    "              \"r250\", \"r850\", \"r500\",\"r700\", \"r1000\", \"z250\", \"z500\", \"z700\", \"z850\", \"z1000\", \n",
    "              \"t250\", \"t850\", \"t500\",\"t700\", \"t1000\",\"dtd250\", \"dtd850\", \"dtd500\",\"dtd700\", \"dtd1000\"\n",
    "              ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa68417c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# date-range for model training and validation\n",
    "from1958to2010 = pd.date_range(start=\"1958-01-01\", end=\"2010-12-31\", freq=\"MS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d34d40e",
   "metadata": {},
   "source": [
    "### control function\n",
    "\n",
    "Define the control function that performs the predictor selection and model training.\n",
    "1. read the station data as object that would apply all the ESD routines\n",
    "2. set predictors with the list of predictors defined and the radius to construct the regional means\n",
    "3. standardize the data with any of the standardizers. Here we use the MonthlyStandardizer method\n",
    "4. defined the scoring metrics to be used for the validation\n",
    "5. set the model to be used for the ESD training (here we will use the LassoLarsCV model)\n",
    "6. fit the model, here we have to define the predictor selector method (here: Recursive ) to be used for selecting the predictors\n",
    "7. get the selected predictors \n",
    "8. use the cross_validate_predict to get the cross-validation metrics of the model training \n",
    "9. store the selected predictors \n",
    "10. stored the validation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "38a7ec64",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_predictor_selection_example_to_test_indices(variable, cachedir, stationnames,\n",
    "                    station_datadir, predictors, predictordir, radius):\n",
    "    \"\"\"\n",
    "    Run an experiment using pyESD to perform predictor selection for a given variable.\n",
    "\n",
    "    Args:\n",
    "        variable (str): The target variable to predict, here Precipitation.\n",
    "        regressor (str): The regression method to use, here we use the RidgeCV regression to test all the predictor selection\n",
    "        methods.\n",
    "        selector_method (str): The method for selecting predictors (\"Recursive\", \"TreeBased\", \"Sequential\").\n",
    "        cachedir (str): Directory to store cached results, here all the files would be stored in the .\n",
    "        stationnames (list): List of station names. it would be loaded from the read_data file\n",
    "        station_datadir (str): Directory containing station data files: this is also set in the read the data file\n",
    "        predictors (list): List of predictor variables.\n",
    "        predictordir (str): Directory containing predictor data files.\n",
    "        radius (float): Radius for selecting predictors: also defined in the read_data file\n",
    "    \"\"\"\n",
    "    num_of_stations = len(stationnames)\n",
    "\n",
    "    # Loop through all stations\n",
    "    for i in range(num_of_stations):\n",
    "        stationname = stationnames[i]\n",
    "        \n",
    "        # set the exact path for the station  data\n",
    "        station_dir = os.path.join(station_datadir, stationname + \".csv\")\n",
    "        \n",
    "        # 1. create the station object using the read_station_csv and apply all the methods on the station object\n",
    "        \n",
    "        SO_instance = read_station_csv(filename=station_dir, varname=variable)\n",
    "\n",
    "        # 2. Setting predictors (generate the predictors using the defined predictor names)\n",
    "        SO_instance.set_predictors(variable, predictors, predictordir, radius)\n",
    "\n",
    "        # 3. Setting standardizer\n",
    "        SO_instance.set_standardizer(variable, standardizer=MonthlyStandardizer(detrending=False, scaling=False))\n",
    "        \n",
    "        # perform correlation\n",
    "        corr = SO_instance.predictor_correlation(variable, from1958to2010, ERA5Data, fit_predictors=True,\n",
    "                                         fit_predictand=True, method=\"pearson\", use_scipy=True)\n",
    "        \n",
    "        # 4. define the scoring metrics\n",
    "        scoring = [\"neg_root_mean_squared_error\", \"r2\", \"neg_mean_absolute_error\"]\n",
    "        \n",
    "        # 5. Setting model with cross-validation\n",
    "        SO_instance.set_model(variable, method=\"LassoLarsCV\", scoring=scoring,\n",
    "                              cv=KFold(n_splits=10))\n",
    "\n",
    "        # 6. Fitting model with predictor selector option\n",
    "        SO_instance.fit(variable, from1958to2010, ERA5Data, fit_predictors=True, predictor_selector=True,\n",
    "                            selector_method=\"Recursive\", selector_regressor=\"ARD\",\n",
    "                            cal_relative_importance=False)\n",
    "        \n",
    "        # 7. Extracting selected predictors\n",
    "        selected_predictors = SO_instance.selected_names(variable)\n",
    "\n",
    "        # 8. Training estimate for the same model\n",
    "        \n",
    "        score, ypred = SO_instance.cross_validate_and_predict(variable, from1958to2010, ERA5Data)\n",
    "\n",
    "        # 9-10. Storing results using pickle\n",
    "        store_pickle(stationname, \"selected_predictors_\" + \"Recursive\", selected_predictors, cachedir)\n",
    "        store_pickle(stationname, \"validation_score_\" + \"Recursive\", score, cachedir)\n",
    "        store_csv(stationname, \"corrwith_predictors_scipy\", corr, cachedir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5dd687d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from read_data import radius, station_prec_datadir, stationnames_prec, ERA5Data, predictordir, cachedir_prec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81456dea",
   "metadata": {},
   "source": [
    "### Experiment with the indices defined \n",
    "Perform the modelling with indices predictors (then store the data in a folder for the analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8a5df0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Freiburg 48.0232 7.8343 236.0\n",
      "13 : optimal number of predictors and selected variables are Index(['NAO', 'SCAN', 't2m', 'tp', 'v10', 'u10', 'u850', 'v1000', 'r1000',\n",
      "       't500', 'dtd250', 'dtd700', 'dtd1000'],\n",
      "      dtype='object')\n",
      "Konstanz 47.6952 9.1307 428.0\n",
      "11 : optimal number of predictors and selected variables are Index(['SCAN', 'EAWR', 't2m', 'tp', 'u10', 'u250', 'u1000', 'v850', 'v700',\n",
      "       'r700', 'dtd700'],\n",
      "      dtype='object')\n",
      "Mannheim 49.5063 8.5584 98.0\n",
      "7 : optimal number of predictors and selected variables are Index(['NAO', 'EA', 'SCAN', 't2m', 'tp', 'u250', 't700'], dtype='object')\n",
      "Nürnberg 49.503 11.0549 314.0\n",
      "39 : optimal number of predictors and selected variables are Index(['NAO', 'EA', 'SCAN', 'EAWR', 't2m', 'tp', 'msl', 'v10', 'u10', 'u250',\n",
      "       'u850', 'u500', 'u700', 'u1000', 'v250', 'v850', 'v500', 'v700',\n",
      "       'v1000', 'r250', 'r850', 'r500', 'r700', 'r1000', 'z250', 'z500',\n",
      "       'z700', 'z850', 'z1000', 't250', 't850', 't500', 't700', 't1000',\n",
      "       'dtd250', 'dtd850', 'dtd500', 'dtd700', 'dtd1000'],\n",
      "      dtype='object')\n",
      "Stuttgart (Schnarrenberg) 48.8281 9.2 314.0\n",
      "5 : optimal number of predictors and selected variables are Index(['tp', 'u10', 'r1000', 't250', 'dtd700'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "run_predictor_selection_example_to_test_indices(variable=\"Precipitation\",\n",
    "                                    cachedir=cachedir_prec, stationnames=stationnames_prec,\n",
    "                                    station_datadir=station_prec_datadir, predictors=predictors_with_indices, \n",
    "                                    predictordir=predictordir, radius=radius)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e07a36",
   "metadata": {},
   "source": [
    "### Experiment without the indices \n",
    "Perform the modelling with predictors without indices (then store the data in a folder for the analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "df1123a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Freiburg 48.0232 7.8343 236.0\n",
      "29 : optimal number of predictors and selected variables are Index(['t2m', 'tp', 'v10', 'u10', 'u250', 'u850', 'u500', 'u700', 'u1000',\n",
      "       'v250', 'v850', 'v500', 'v700', 'v1000', 'r250', 'r850', 'r500', 'r700',\n",
      "       'r1000', 'z1000', 't250', 't850', 't500', 't1000', 'dtd250', 'dtd850',\n",
      "       'dtd500', 'dtd700', 'dtd1000'],\n",
      "      dtype='object')\n",
      "Konstanz 47.6952 9.1307 428.0\n",
      "35 : optimal number of predictors and selected variables are Index(['t2m', 'tp', 'msl', 'v10', 'u10', 'u250', 'u850', 'u500', 'u700',\n",
      "       'u1000', 'v250', 'v850', 'v500', 'v700', 'v1000', 'r250', 'r850',\n",
      "       'r500', 'r700', 'r1000', 'z250', 'z500', 'z700', 'z850', 'z1000',\n",
      "       't250', 't850', 't500', 't700', 't1000', 'dtd250', 'dtd850', 'dtd500',\n",
      "       'dtd700', 'dtd1000'],\n",
      "      dtype='object')\n",
      "Mannheim 49.5063 8.5584 98.0\n",
      "6 : optimal number of predictors and selected variables are Index(['t2m', 'tp', 'u500', 'v250', 't500', 't700'], dtype='object')\n",
      "Nürnberg 49.503 11.0549 314.0\n",
      "33 : optimal number of predictors and selected variables are Index(['t2m', 'tp', 'msl', 'v10', 'u10', 'u250', 'u850', 'u500', 'u700',\n",
      "       'u1000', 'v250', 'v850', 'v500', 'v700', 'v1000', 'r250', 'r850',\n",
      "       'r500', 'r700', 'r1000', 'z250', 'z500', 'z700', 't250', 't850', 't500',\n",
      "       't700', 't1000', 'dtd250', 'dtd850', 'dtd500', 'dtd700', 'dtd1000'],\n",
      "      dtype='object')\n",
      "Stuttgart (Schnarrenberg) 48.8281 9.2 314.0\n",
      "31 : optimal number of predictors and selected variables are Index(['t2m', 'tp', 'msl', 'v10', 'u10', 'u250', 'u850', 'u500', 'u700',\n",
      "       'u1000', 'v850', 'v500', 'v700', 'v1000', 'r250', 'r850', 'r500',\n",
      "       'r700', 'r1000', 'z700', 'z1000', 't250', 't850', 't500', 't700',\n",
      "       't1000', 'dtd250', 'dtd850', 'dtd500', 'dtd700', 'dtd1000'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "run_predictor_selection_example_to_test_indices(variable=\"Precipitation\",\n",
    "                                    cachedir=cachedir_prec, stationnames=stationnames_prec,\n",
    "                                    station_datadir=station_prec_datadir, predictors=predictors_without, \n",
    "                                    predictordir=predictordir, radius=radius)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8fbc14ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_results_exp1 = \"C:/Users/dboateng/Desktop/Python_scripts/ESD_Package/examples/tutorials/predictor_selection_with_indices/\"\n",
    "path_to_results_exp2 = \"C:/Users/dboateng/Desktop/Python_scripts/ESD_Package/examples/tutorials/predictor_selection_without_indices/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a0810a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyESD.plot import barplot\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(20,15), sharey=False, sharex=True)\n",
    "\n",
    "barplot(methods=[\"Recursive\"], stationnames=stationnames_prec , path_to_data=path_to_results_exp1, \n",
    "        xlabel=\"Stations\", ylabel=\"CV R² (with indices)\", varname= \"test_r2\", varname_std =\"test_r2_std\",\n",
    "        filename=\"validation_score_\", legend=True, ax=ax1,)\n",
    "\n",
    "barplot(methods=[\"Recursive\"], stationnames=stationnames_prec , path_to_data=path_to_results_exp2, \n",
    "        xlabel=\"Stations\", ylabel=\"CV R²\", varname= \"test_r2\", varname_std =\"test_r2_std\",\n",
    "        filename=\"validation_score_\", legend=True, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2ddd1bb",
   "metadata": {},
   "source": [
    "The models with indices estimate slight increase in performance in comparison to the models without indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6293a268",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}