djgagne_partial_dependence_plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Partial Dependence Plot Example\n",
    "David John Gagne\n",
    "\n",
    "The goal of this notebook is to show an example of a serial and potentially parallel partial dependence plot in order to figure out ways to scale this better with Pangeo tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "import matplotlib.pyplot as plt\n",
    "from dask.distributed import LocalCluster, Client\n",
    "import os\n",
    "from os.path import exists, join\n",
    "from urllib.request import urlretrieve\n",
    "import tarfile\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download CSV Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Get csv files\n",
      "Extract csv tar file\n"
     ]
    }
   ],
   "source": [
    "if not exists(\"tornado_data\"):\n",
    "    os.mkdir(\"tornado_data\")\n",
    "csv_tar_file = \"https://storage.googleapis.com/track_data_ncar_ams_3km_csv_small/track_data_ncar_ams_3km_csv_small.tar.gz\"\n",
    "print(\"Get csv files\")\n",
    "urlretrieve(csv_tar_file, join(\"tornado_data\", csv_tar_file.split(\"/\")[-1]))\n",
    "print(\"Extract csv tar file\")\n",
    "csv_tar = tarfile.open(join(\"tornado_data\", csv_tar_file.split(\"/\")[-1]))\n",
    "csv_tar.extractall(\"tornado_data/\")\n",
    "csv_tar.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load CSV data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20101024-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20101122-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110201-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110308-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110326-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110404-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110414-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110420-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110425-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110509-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110522-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110527-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110605-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110610-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110615-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110620-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110625-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110704-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20110712-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20111116-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120218-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120315-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120323-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120401-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120409-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120426-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120503-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120510-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120529-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120606-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120622-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120701-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120706-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20120715-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20121225-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130318-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130331-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130411-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130429-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130513-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130519-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130527-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130602-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130613-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130619-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130625-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130701-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130708-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20130715-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140220-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140328-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140407-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140425-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140508-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140514-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140526-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140604-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140609-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140617-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140622-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140628-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140705-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20140710-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20141123-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150331-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150416-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150422-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150505-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150510-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150523-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150528-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150605-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150612-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150620-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150625-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150630-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150706-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20150712-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20151031-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20151227-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160224-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160323-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160401-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160415-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160429-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160505-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160511-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160522-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160528-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160608-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160616-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160622-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160628-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160707-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20160712-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20161129-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20170121-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20170228-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20170323-0000.csv', 'tornado_data/track_data_ncar_ams_3km_csv_small/track_step_NCARSTORM_d01_20170329-0000.csv']\n"
     ]
    }
   ],
   "source": [
    "path = \"tornado_data/track_data_ncar_ams_3km_csv_small/\"\n",
    "files = sorted(glob.glob(path+\"/*.csv\"))\n",
    "print(files)\n",
    "df = pd.concat([pd.read_csv(f, parse_dates=[\"Run_Date\", \"Valid_Date\"]) for f in files], ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step_ID\n",
      "Track_ID\n",
      "Ensemble_Name\n",
      "Ensemble_Member\n",
      "Run_Date\n",
      "Valid_Date\n",
      "Forecast_Hour\n",
      "Valid_Hour_UTC\n",
      "Duration\n",
      "Centroid_Lon\n",
      "Centroid_Lat\n",
      "Centroid_X\n",
      "Centroid_Y\n",
      "Storm_Motion_U\n",
      "Storm_Motion_V\n",
      "REFL_COM_mean\n",
      "REFL_COM_max\n",
      "REFL_COM_min\n",
      "REFL_COM_std\n",
      "REFL_COM_percentile_10\n",
      "REFL_COM_percentile_25\n",
      "REFL_COM_percentile_50\n",
      "REFL_COM_percentile_75\n",
      "REFL_COM_percentile_90\n",
      "U10_mean\n",
      "U10_max\n",
      "U10_min\n",
      "U10_std\n",
      "U10_percentile_10\n",
      "U10_percentile_25\n",
      "U10_percentile_50\n",
      "U10_percentile_75\n",
      "U10_percentile_90\n",
      "V10_mean\n",
      "V10_max\n",
      "V10_min\n",
      "V10_std\n",
      "V10_percentile_10\n",
      "V10_percentile_25\n",
      "V10_percentile_50\n",
      "V10_percentile_75\n",
      "V10_percentile_90\n",
      "T2_mean\n",
      "T2_max\n",
      "T2_min\n",
      "T2_std\n",
      "T2_percentile_10\n",
      "T2_percentile_25\n",
      "T2_percentile_50\n",
      "T2_percentile_75\n",
      "T2_percentile_90\n",
      "RVORT1_MAX-future_mean\n",
      "RVORT1_MAX-future_max\n",
      "RVORT1_MAX-future_min\n",
      "RVORT1_MAX-future_std\n",
      "RVORT1_MAX-future_percentile_10\n",
      "RVORT1_MAX-future_percentile_25\n",
      "RVORT1_MAX-future_percentile_50\n",
      "RVORT1_MAX-future_percentile_75\n",
      "RVORT1_MAX-future_percentile_90\n",
      "HAIL_MAXK1-future_mean\n",
      "HAIL_MAXK1-future_max\n",
      "HAIL_MAXK1-future_min\n",
      "HAIL_MAXK1-future_std\n",
      "HAIL_MAXK1-future_percentile_10\n",
      "HAIL_MAXK1-future_percentile_25\n",
      "HAIL_MAXK1-future_percentile_50\n",
      "HAIL_MAXK1-future_percentile_75\n",
      "HAIL_MAXK1-future_percentile_90\n",
      "area\n",
      "eccentricity\n",
      "major_axis_length\n",
      "minor_axis_length\n",
      "orientation\n",
      "Matched\n",
      "Max_Hail_Size\n",
      "Num_Matches\n",
      "Shape\n",
      "Location\n",
      "Scale\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are using reflectivity, u-wind, v-wind, and 2 m temperature to predict vorticity for a given storm in a dataset of storms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_cols = [\"REFL_COM_mean\", \"U10_mean\", \"V10_mean\", \"T2_mean\"]\n",
    "output_col = [\"RVORT1_MAX-future_max\"]\n",
    "split_date = pd.Timestamp(\"2015-01-01\")\n",
    "train_in = df.loc[df[\"Run_Date\"] < split_date, input_cols]\n",
    "train_out = df.loc[df[\"Run_Date\"] < split_date, output_col]\n",
    "test_in = df.loc[df[\"Run_Date\"] >= split_date, input_cols]\n",
    "test_out = df.loc[df[\"Run_Date\"]>= split_date, output_col]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76377, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_out.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=50, max_depth=8)\n",
    "rf.fit(train_in, train_out.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to interpret the input sensitivities for the random forest. We will use a technique called partial dependence plots, which changes the inputs in a way that can affect the mean output and reveal sensitivities of the model to certain ranges of inputs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def partial_dependence_1d(x, model, var_index, var_vals):\n",
    "    \"\"\"\n",
    "    Calculate how the mean prediction of an ML model varies if one variable's value is fixed across all input\n",
    "    examples.\n",
    "\n",
    "    Args:\n",
    "        x: array of input variables\n",
    "        model: scikit-learn style model object\n",
    "        var_index: column index of the variable being investigated\n",
    "        var_vals: values of the input variable that are fixed.\n",
    "\n",
    "    Returns:\n",
    "        Array of partial dependence values.\n",
    "    \"\"\"\n",
    "    partial_dependence = np.zeros(var_vals.shape)\n",
    "    x_copy = np.copy(x)\n",
    "    for v, var_val in enumerate(var_vals):\n",
    "        x_copy[:, var_index] = var_val\n",
    "        partial_dependence[v] = model.predict(x_copy).mean()\n",
    "    return partial_dependence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REFL_COM_mean</th>\n",
       "      <th>U10_mean</th>\n",
       "      <th>V10_mean</th>\n",
       "      <th>T2_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>76377.000000</td>\n",
       "      <td>76377.000000</td>\n",
       "      <td>76377.000000</td>\n",
       "      <td>76377.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.846515</td>\n",
       "      <td>0.449749</td>\n",
       "      <td>0.542538</td>\n",
       "      <td>289.408528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.984091</td>\n",
       "      <td>4.347625</td>\n",
       "      <td>4.461936</td>\n",
       "      <td>6.931555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.082820</td>\n",
       "      <td>-18.653700</td>\n",
       "      <td>-20.561280</td>\n",
       "      <td>262.921600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>43.755930</td>\n",
       "      <td>-2.366580</td>\n",
       "      <td>-2.301950</td>\n",
       "      <td>285.114260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46.170650</td>\n",
       "      <td>0.396810</td>\n",
       "      <td>0.659590</td>\n",
       "      <td>290.625850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.128190</td>\n",
       "      <td>3.271980</td>\n",
       "      <td>3.496990</td>\n",
       "      <td>294.398190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>68.743160</td>\n",
       "      <td>19.686640</td>\n",
       "      <td>18.616930</td>\n",
       "      <td>312.149230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       REFL_COM_mean      U10_mean      V10_mean       T2_mean\n",
       "count   76377.000000  76377.000000  76377.000000  76377.000000\n",
       "mean       46.846515      0.449749      0.542538    289.408528\n",
       "std         3.984091      4.347625      4.461936      6.931555\n",
       "min        40.082820    -18.653700    -20.561280    262.921600\n",
       "25%        43.755930     -2.366580     -2.301950    285.114260\n",
       "50%        46.170650      0.396810      0.659590    290.625850\n",
       "75%        49.128190      3.271980      3.496990    294.398190\n",
       "max        68.743160     19.686640     18.616930    312.149230"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_in.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example for a single variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_count = 50\n",
    "index = 0\n",
    "var_vals = np.linspace(train_in.iloc[:, 0].min(), train_in.iloc[:, 0].max(), pd_count)\n",
    "pd_vals = partial_dependence_1d(train_in, rf, index, var_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.99 s ± 236 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd_vals = partial_dependence_1d(train_in, rf, index, var_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'REFL_COM_mean')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(var_vals, pd_vals, 'ko-')\n",
    "plt.xlabel(input_cols[index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How well can Dask parallelize the task across all the input variables?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster = LocalCluster(n_workers=4)\n",
    "client = Client(cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Client</h3>\n",
       "<ul>\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:51668\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Cluster</h3>\n",
       "<ul>\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>8</li>\n",
       "  <li><b>Memory: </b>17.18 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: scheduler='tcp://127.0.0.1:51668' processes=4 cores=8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_count = 50\n",
    "var_vals = np.zeros((len(input_cols), pd_count))\n",
    "futures = []\n",
    "train_future = client.scatter(train_in.values)\n",
    "for i, input_col in enumerate(input_cols):\n",
    "    var_vals[i] = np.linspace(train_in[input_col].min(), train_in[input_col].max(), pd_count)\n",
    "    futures.append(client.submit(partial_dependence_1d, train_future, rf, i, var_vals[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = client.gather(futures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n",
    "axef = axes.ravel()\n",
    "for r, res in enumerate(results):\n",
    "    axef[r].plot(var_vals[r], res, 'ko-')\n",
    "    axef[r].set_xlabel(input_cols[r])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that in this case, dask already does quite well with paralellizing the task? \n",
    "\n",
    "Is there a way we can make dask struggle or fail? At what dataset size do we see problems?\n",
    "\n",
    "Is there a more concise way to paralellize the task?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}