nsw-2016-storm-impact/notebooks/07_evaluate_model_performance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate prediction metrics \n",
    "- This notebook is used to check out each of our storm impact prediction models performed in comparison to our observed storm impacts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup notebook\n",
    "Import our required packages and set default plotting options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable autoreloading of our modules. \n",
    "# Most of the code will be located in the /src/ folder, \n",
    "# and then called from the notebook.\n",
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.debugger import set_trace\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import decimal\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "import plotly.plotly as py\n",
    "import plotly.tools as tls\n",
    "import plotly.figure_factory as ff\n",
    "from plotly import tools\n",
    "import plotly.io as pio\n",
    "from scipy import stats\n",
    "import math\n",
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "import colorlover as cl\n",
    "from tqdm import tqdm_notebook\n",
    "from ipywidgets import widgets, Output\n",
    "from IPython.display import display, clear_output, Image, HTML\n",
    "from scipy import stats\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "from matplotlib.lines import Line2D\n",
    "from cycler import cycler\n",
    "from scipy.interpolate import interp1d\n",
    "from pandas.api.types import CategoricalDtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matplot lib default settings\n",
    "plt.rcParams[\"figure.figsize\"] = (10,6)\n",
    "plt.rcParams['axes.grid']=True\n",
    "plt.rcParams['grid.alpha'] = 0.5\n",
    "plt.rcParams['grid.color'] = \"grey\"\n",
    "plt.rcParams['grid.linestyle'] = \"--\"\n",
    "plt.rcParams['axes.grid']=True\n",
    "\n",
    "# https://stackoverflow.com/a/20709149\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [\n",
    "       r'\\usepackage{siunitx}',   # i need upright \\micro symbols, but you need...\n",
    "       r'\\sisetup{detect-all}',   # ...this to force siunitx to actually use your fonts\n",
    "       r'\\usepackage{helvet}',    # set the normal font here\n",
    "       r'\\usepackage{amsmath}',\n",
    "       r'\\usepackage{sansmath}',  # load up the sansmath so that math -> helvet\n",
    "       r'\\sansmath',              # <- tricky! -- gotta actually tell tex to use!\n",
    "]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import data\n",
    "Import our data from the `./data/interim/` folder and load it into pandas dataframes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
    "    print('Importing {}'.format(csv))\n",
    "    return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
    "\n",
    "df_waves = df_from_csv('waves.csv', index_col=[0, 1])\n",
    "df_tides = df_from_csv('tides.csv', index_col=[0, 1])\n",
    "df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
    "df_sites = df_from_csv('sites.csv', index_col=[0])\n",
    "df_profile_features_crest_toes = df_from_csv('profile_features_crest_toes.csv', index_col=[0,1])\n",
    "\n",
    "# Note that the forecasted data sets should be in the same order for impacts and twls\n",
    "impacts = {\n",
    "    'forecasted': {\n",
    "    'postintertidal_slope_hol86': df_from_csv('impacts_forecasted_postintertidal_slope_hol86.csv', index_col=[0]),\n",
    "    'postintertidal_slope_nie91': df_from_csv('impacts_forecasted_postintertidal_slope_nie91.csv', index_col=[0]),\n",
    "    'postintertidal_slope_pow18': df_from_csv('impacts_forecasted_postintertidal_slope_pow18.csv', index_col=[0]),\n",
    "    'postintertidal_slope_sto06': df_from_csv('impacts_forecasted_postintertidal_slope_sto06.csv', index_col=[0]),\n",
    "    'postmean_slope_hol86': df_from_csv('impacts_forecasted_postmean_slope_hol86.csv', index_col=[0]),\n",
    "    'postmean_slope_nie91': df_from_csv('impacts_forecasted_postmean_slope_nie91.csv', index_col=[0]),\n",
    "    'postmean_slope_pow18': df_from_csv('impacts_forecasted_postmean_slope_pow18.csv', index_col=[0]),\n",
    "    'postmean_slope_sto06': df_from_csv('impacts_forecasted_postmean_slope_sto06.csv', index_col=[0]),\n",
    "    'preintertidal_slope_hol86': df_from_csv('impacts_forecasted_preintertidal_slope_hol86.csv', index_col=[0]),\n",
    "    'preintertidal_slope_nie91': df_from_csv('impacts_forecasted_preintertidal_slope_nie91.csv', index_col=[0]),\n",
    "    'preintertidal_slope_pow18': df_from_csv('impacts_forecasted_preintertidal_slope_pow18.csv', index_col=[0]),\n",
    "    'preintertidal_slope_sto06': df_from_csv('impacts_forecasted_preintertidal_slope_sto06.csv', index_col=[0]),\n",
    "    'premean_slope_hol86': df_from_csv('impacts_forecasted_premean_slope_hol86.csv', index_col=[0]),\n",
    "    'premean_slope_nie91': df_from_csv('impacts_forecasted_premean_slope_nie91.csv', index_col=[0]),\n",
    "    'premean_slope_pow18': df_from_csv('impacts_forecasted_premean_slope_pow18.csv', index_col=[0]),\n",
    "    'premean_slope_sto06': df_from_csv('impacts_forecasted_premean_slope_sto06.csv', index_col=[0]),\n",
    "        },\n",
    "    'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
    "    }\n",
    "\n",
    "\n",
    "twls = {\n",
    "    'forecasted': {\n",
    "    'postintertidal_slope_hol86': df_from_csv('twl_postintertidal_slope_hol86.csv', index_col=[0,1]),\n",
    "    'postintertidal_slope_nie91': df_from_csv('twl_postintertidal_slope_nie91.csv', index_col=[0,1]),\n",
    "    'postintertidal_slope_pow18': df_from_csv('twl_postintertidal_slope_pow18.csv', index_col=[0,1]),\n",
    "    'postintertidal_slope_sto06': df_from_csv('twl_postintertidal_slope_sto06.csv', index_col=[0,1]),\n",
    "    'postmean_slope_hol86': df_from_csv('twl_postmean_slope_hol86.csv', index_col=[0,1]),\n",
    "    'postmean_slope_nie91': df_from_csv('twl_postmean_slope_nie91.csv', index_col=[0,1]),\n",
    "    'postmean_slope_pow18': df_from_csv('twl_postmean_slope_pow18.csv', index_col=[0,1]),\n",
    "    'postmean_slope_sto06': df_from_csv('twl_postmean_slope_sto06.csv', index_col=[0,1]),\n",
    "    'preintertidal_slope_hol86': df_from_csv('twl_preintertidal_slope_hol86.csv', index_col=[0,1]),\n",
    "    'preintertidal_slope_nie91': df_from_csv('twl_preintertidal_slope_nie91.csv', index_col=[0,1]),\n",
    "    'preintertidal_slope_pow18': df_from_csv('twl_preintertidal_slope_pow18.csv', index_col=[0,1]),\n",
    "    'preintertidal_slope_sto06': df_from_csv('twl_preintertidal_slope_sto06.csv', index_col=[0,1]),\n",
    "    'premean_slope_hol86': df_from_csv('twl_premean_slope_hol86.csv', index_col=[0,1]),\n",
    "    'premean_slope_nie91': df_from_csv('twl_premean_slope_nie91.csv', index_col=[0,1]),\n",
    "    'premean_slope_pow18': df_from_csv('twl_premean_slope_pow18.csv', index_col=[0,1]),\n",
    "    'premean_slope_sto06': df_from_csv('twl_premean_slope_sto06.csv', index_col=[0,1]),\n",
    "    }\n",
    "}\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate longshore plots for each beach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "beaches = list(\n",
    "    set([\n",
    "        x[:-4] for x in df_profiles.index.get_level_values('site_id').unique()\n",
    "    ]))\n",
    "\n",
    "for beach in beaches:\n",
    "   \n",
    "    df_obs_impacts = impacts['observed'].loc[impacts['observed'].index.str.\n",
    "                                             contains(beach)]\n",
    "\n",
    "    # Get index for each site on the beach\n",
    "    n = [x for x in range(len(df_obs_impacts))][::-1]\n",
    "    n_sites = [x for x in df_obs_impacts.index][::-1]\n",
    "\n",
    "    # Convert storm regimes to categorical datatype\n",
    "    cat_type = CategoricalDtype(\n",
    "        categories=['swash', 'collision', 'overwash', 'inundation'],\n",
    "        ordered=True)\n",
    "    df_obs_impacts.storm_regime = df_obs_impacts.storm_regime.astype(cat_type)\n",
    "\n",
    "    # Create figure\n",
    "    \n",
    "    # Determine the height of the figure, based on the number of sites.\n",
    "    fig_height = max(6, 0.18 * len(n_sites))\n",
    "    f, (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8) = plt.subplots(\n",
    "        1,\n",
    "        8,\n",
    "        sharey=True,\n",
    "        figsize=(18, fig_height),\n",
    "        gridspec_kw={'width_ratios': [4, 4, 2, 2, 2, 2, 2, 2]})\n",
    "\n",
    "    # ax1: Impacts\n",
    "\n",
    "    # Define colors for storm regime\n",
    "    cmap = {'swash': '#1a9850', 'collision': '#fee08b', 'overwash': '#d73027'}\n",
    "\n",
    "    # Common marker style\n",
    "    marker_style = {\n",
    "        's': 60,\n",
    "        'linewidths': 0.7,\n",
    "        'alpha': 1,\n",
    "        'edgecolors': 'k',\n",
    "        'marker': 'o',\n",
    "    }\n",
    "\n",
    "    # Plot observed impacts\n",
    "    colors = [cmap.get(x) for x in df_obs_impacts.storm_regime]\n",
    "    colors = ['#aaaaaa' if c is None else c for c in colors]\n",
    "    ax1.scatter([0 for x in n], n, color=colors, **marker_style)\n",
    "\n",
    "    # Plot model impacts\n",
    "    for i, model in enumerate(impacts['forecasted']):\n",
    "\n",
    "        # Only get model results for this beach\n",
    "        df_model = impacts['forecasted'][model].loc[\n",
    "            impacts['forecasted'][model].index.str.contains(beach)]\n",
    "\n",
    "        # Recast storm regimes as categorical data\n",
    "        df_model.storm_regime = df_model.storm_regime.astype(cat_type)\n",
    "\n",
    "        # Assign colors\n",
    "        colors = [cmap.get(x) for x in df_model.storm_regime]\n",
    "        colors = ['#aaaaaa' if c is None else c for c in colors]\n",
    "\n",
    "        # Only plot markers which are different to the observed storm regime. \n",
    "        # This makes it easier to find where model predictions differ\n",
    "        y_coords = []\n",
    "        for obs_impact, for_impact in zip(df_model.storm_regime,\n",
    "                                          df_obs_impacts.storm_regime):\n",
    "            if obs_impact == for_impact:\n",
    "                y_coords.append(None)\n",
    "            else:\n",
    "                y_coords.append(i + 1)\n",
    "\n",
    "        ax1.scatter(y_coords, n, color=colors, **marker_style)\n",
    "\n",
    "    # Add model names to each impact on x axis\n",
    "    ax1.set_xticks(range(len(impacts['forecasted']) + 1))\n",
    "    ax1.set_xticklabels(['observed'] +\n",
    "                        [x.replace('_', '\\_') for x in impacts['forecasted']])\n",
    "    ax1.xaxis.set_tick_params(rotation=90)\n",
    "\n",
    "    # Add title\n",
    "    ax1.set_title('Storm regime')\n",
    "\n",
    "    # Create custom legend\n",
    "    legend_elements = [\n",
    "        Line2D([0], [0],\n",
    "               marker='o',\n",
    "               color='w',\n",
    "               label='Swash',\n",
    "               markerfacecolor='#1a9850',\n",
    "               markersize=8,\n",
    "               markeredgewidth=1.0,\n",
    "               markeredgecolor='k'),\n",
    "        Line2D([0], [0],\n",
    "               marker='o',\n",
    "               color='w',\n",
    "               label='Collision',\n",
    "               markerfacecolor='#fee08b',\n",
    "               markersize=8,\n",
    "               markeredgewidth=1.0,\n",
    "               markeredgecolor='k'),\n",
    "        Line2D([0], [0],\n",
    "               marker='o',\n",
    "               color='w',\n",
    "               label='Overwash',\n",
    "               markerfacecolor='#d73027',\n",
    "               markersize=8,\n",
    "               markeredgewidth=1.0,\n",
    "               markeredgecolor='k'),\n",
    "    ]\n",
    "    ax1.legend(\n",
    "        handles=legend_elements, loc='lower center', bbox_to_anchor=(0.5, 1.1))\n",
    "\n",
    "    # Replace axis ticks with names of site ids\n",
    "    ytick_labels = ax1.get_yticks().tolist()\n",
    "    yticks = [\n",
    "        n_sites[int(y)] if all([y >= 0, y < len(n_sites)]) else ''\n",
    "        for y in ytick_labels\n",
    "    ]\n",
    "    yticks = [x.replace('_', '\\_') for x in yticks]\n",
    "    ax1.set_yticklabels(yticks)\n",
    "\n",
    "    # ax2: elevations\n",
    "\n",
    "    # Dune elevations\n",
    "    df_feats = df_profile_features_crest_toes.xs(['prestorm'],\n",
    "                                                 level=['profile_type'])\n",
    "    df_feats = df_feats.loc[df_feats.index.str.contains(beach)]\n",
    "\n",
    "    ax2.plot(df_feats.dune_crest_z, n, color='#fdae61')\n",
    "    ax2.plot(df_feats.dune_toe_z, n, color='#fdae61')\n",
    "    ax2.fill_betweenx(\n",
    "        n,\n",
    "        df_feats.dune_toe_z,\n",
    "        df_feats.dune_crest_z,\n",
    "        alpha=0.2,\n",
    "        color='#fdae61',\n",
    "        label='$D_{low}$ to $D_{high}$')\n",
    "\n",
    "    model_colors = [\n",
    "        '#1f78b4',\n",
    "        '#33a02c',\n",
    "        '#e31a1c',\n",
    "        '#6a3d9a',\n",
    "        '#a6cee3',\n",
    "        '#b2df8a',\n",
    "        '#fb9a99',\n",
    "        '#cab2d6',\n",
    "        '#ffff99',\n",
    "    ]\n",
    "\n",
    "    # Define colors to cycle through for our R_high\n",
    "    ax2.set_prop_cycle(cycler('color', model_colors))\n",
    "\n",
    "    # For TWL elevations, Rhigh-Dlow and R2 axis, only plot a few models\n",
    "    models_to_plot = [\n",
    "        'premean_slope_hol86',\n",
    "        'premean_slope_sto06',\n",
    "        'preintertidal_slope_hol86',\n",
    "        'preintertidal_slope_sto06',\n",
    "    ]\n",
    "    models_linewidth = 0.8\n",
    "\n",
    "    # Plot R_high values\n",
    "    for model in models_to_plot:\n",
    "\n",
    "        # Only get model results for this beach\n",
    "        df_model = impacts['forecasted'][model].loc[\n",
    "            impacts['forecasted'][model].index.str.contains(beach)]\n",
    "\n",
    "        # Recast storm regimes as categorical data\n",
    "        ax2.plot(\n",
    "            df_model.R_high,\n",
    "            n,\n",
    "            label=model.replace('_', '\\_'),\n",
    "            linewidth=models_linewidth)\n",
    "\n",
    "    # Set title, legend and labels\n",
    "    ax2.set_title('TWL \\& dune\\nelevations')\n",
    "    ax2.legend(loc='lower center', bbox_to_anchor=(0.5, 1.1))\n",
    "    ax2.set_xlabel('Elevation (m AHD)')\n",
    "#     ax2.set_xlim([0, max(df_feats.dune_crest_z)])\n",
    "\n",
    "    # ax3: Plot R_high - D_low\n",
    "\n",
    "    # Define colors to cycle through for our R_high\n",
    "    ax3.set_prop_cycle(cycler('color', model_colors))\n",
    "\n",
    "    # Plot R_high values\n",
    "    for model in models_to_plot:\n",
    "\n",
    "        df_model = impacts['forecasted'][model].loc[\n",
    "            impacts['forecasted'][model].index.str.contains(beach)]\n",
    "        # R_high - D_low\n",
    "        ax3.plot(\n",
    "            df_model.R_high - df_feats.dune_toe_z,\n",
    "            n,\n",
    "            label=model.replace('_', '\\_'),\n",
    "            linewidth=models_linewidth)\n",
    "\n",
    "    ax3.axvline(x=0, color='black', linestyle=':')\n",
    "    ax3.set_title('$R_{high}$ - $D_{low}$')\n",
    "    ax3.set_xlabel('Height (m)')\n",
    "#     ax3.set_xlim([-2, 2])\n",
    "\n",
    "    # Define colors to cycle through for our R2\n",
    "    ax4.set_prop_cycle(cycler('color', model_colors))\n",
    "\n",
    "    # R_high - D_low\n",
    "    for model in models_to_plot:\n",
    "        df_R2 = impacts['forecasted'][model].merge(\n",
    "            twls['forecasted'][model], on=['site_id', 'datetime'], how='left')\n",
    "        df_R2 = df_R2.loc[df_R2.index.str.contains(beach)]\n",
    "        ax4.plot(\n",
    "            df_R2.R2,\n",
    "            n,\n",
    "            label=model.replace('_', '\\_'),\n",
    "            linewidth=models_linewidth)\n",
    "\n",
    "    ax4.set_title(r'$R_{2\\%}$')\n",
    "    ax4.set_xlabel('Height (m)')\n",
    "#     ax4.set_xlim([0, 10])\n",
    "\n",
    "    # Beach slope\n",
    "    slope_colors = [\n",
    "        '#bebada',\n",
    "        '#bc80bd',\n",
    "        '#ffed6f',\n",
    "        '#fdb462',\n",
    "    ]\n",
    "    ax5.set_prop_cycle(cycler('color', slope_colors))\n",
    "    slope_models = {\n",
    "        'prestorm mean': 'premean_slope_sto06',\n",
    "        'poststorm mean': 'postmean_slope_sto06',\n",
    "        'prestorm intertidal': 'preintertidal_slope_sto06',\n",
    "        'poststorm intertidal': 'postintertidal_slope_sto06',\n",
    "    }\n",
    "\n",
    "    for label in slope_models:\n",
    "        model = slope_models[label]\n",
    "        df_beta = impacts['forecasted'][model].merge(\n",
    "            twls['forecasted'][model], on=['site_id', 'datetime'], how='left')\n",
    "        df_beta = df_beta.loc[df_beta.index.str.contains(beach)]\n",
    "        ax5.plot(df_beta.beta, n, label=label, linewidth=models_linewidth)\n",
    "\n",
    "    ax5.set_title(r'$\\beta$')\n",
    "    ax5.set_xlabel('Beach slope')\n",
    "    ax5.legend(loc='lower center', bbox_to_anchor=(0.5, 1.1))\n",
    "    # ax5.set_xlim([0, 0.15])\n",
    "\n",
    "    # Need to chose a model to extract environmental parameters at maximum R_high time\n",
    "    model = 'premean_slope_sto06'\n",
    "    df_beach = impacts['forecasted'][model].merge(\n",
    "        twls['forecasted'][model], on=['site_id', 'datetime'], how='left')\n",
    "    df_beach = df_beach.loc[df_beach.index.str.contains(beach)]\n",
    "\n",
    "    # Wave height, wave period\n",
    "    ax6.plot(df_beach.Hs0, n, color='#999999')\n",
    "    ax6.set_title('$H_{s0}$')\n",
    "    ax6.set_xlabel('Sig. wave height (m)')\n",
    "    ax6.set_xlim([3, 5])\n",
    "\n",
    "    ax7.plot(df_beach.Tp, n, color='#999999')\n",
    "    ax7.set_title('$T_{p}$')\n",
    "    ax7.set_xlabel('Peak wave period (s)')\n",
    "    ax7.set_xlim([8, 14])\n",
    "\n",
    "    ax8.plot(df_beach.tide, n, color='#999999')\n",
    "    ax8.set_title('Tide \\& surge')\n",
    "    ax8.set_xlabel('Elevation (m AHD)')\n",
    "    ax8.set_xlim([0, 2])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    f.subplots_adjust(top=0.88)\n",
    "    f.suptitle(beach.replace('_', '\\_'))\n",
    "\n",
    "    # Set minor axis ticks on each plot\n",
    "    ax1.yaxis.set_minor_locator(MultipleLocator(1))\n",
    "    ax1.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
    "    ax2.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
    "    ax3.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
    "    ax4.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
    "    ax5.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
    "    ax6.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
    "    ax7.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
    "    ax8.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
    "\n",
    "    # # Print to figure\n",
    "    plt.savefig('07_{}.png'.format(beach), dpi=600, bbox_inches='tight')\n",
    "\n",
    "#     plt.show()\n",
    "    plt.close()\n",
    "    print('Done: {}'.format(beach))\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate classification reports for each model\n",
    "Use sklearn metrics to generate classification reports for each forecasting model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.metrics\n",
    "\n",
    "# Get observed impacts\n",
    "df_obs = impacts['observed']\n",
    "\n",
    "# Convert storm regimes to categorical datatype\n",
    "cat_type = CategoricalDtype(\n",
    "    categories=['swash', 'collision', 'overwash', 'inundation'], ordered=True)\n",
    "df_obs.storm_regime = df_obs.storm_regime.astype(cat_type)\n",
    "\n",
    "for model in impacts['forecasted']:\n",
    "    df_for = impacts['forecasted'][model]\n",
    "    df_for.storm_regime = df_for.storm_regime.astype(cat_type)\n",
    "\n",
    "    m = sklearn.metrics.classification_report(\n",
    "            df_obs.storm_regime.astype(cat_type).cat.codes.values,\n",
    "            df_for.storm_regime.astype(cat_type).cat.codes.values,\n",
    "            labels=[0, 1, 2, 3],\n",
    "            target_names=['swash', 'collision', 'overwash', 'inundation'])\n",
    "    print(model)\n",
    "    print(m)\n",
    "    print()\n",
    "    "
   ]
  }
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