MasterCode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project on Full Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-an6uteyx because the default path (/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n",
      "/.local/lib/python3.8/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import scipy.stats as sts\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "from collections import defaultdict\n",
    "from glob import glob\n",
    "import random as rd\n",
    "import pylab as pl\n",
    "import pandas as pd\n",
    "import re\n",
    "from sklearn import decomposition\n",
    "from sklearn import pipeline\n",
    "\n",
    "\n",
    "from PIL import Image\n",
    "import PIL.ImageOps\n",
    "\n",
    "#from skimage import io, color\n",
    "import matplotlib.image as mpimg\n",
    "from skimage.transform import resize\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.model_selection import train_test_split, cross_val_score,  GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn.metrics import classification_report, mean_squared_error\n",
    "\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "import pygeos\n",
    "import ntpath\n",
    "import geopandas as gpd\n",
    "#import geoplot\n",
    "from shapely.geometry import Point, Polygon\n",
    "from fiona.crs import from_epsg\n",
    "import math\n",
    "\n",
    "import tensorflow as tf\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\n",
    "os.environ['TORCH_HOME'] = '/workspace/cache'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
      "env: CUDA_VISIBLE_DEVICES=0,1,2,3\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
    "%env CUDA_VISIBLE_DEVICES= 0,1,2,3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the random seed for reproducibility\n",
    "rd.seed(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[LogicalDevice(name='/device:GPU:0', device_type='GPU'),\n",
       " LogicalDevice(name='/device:GPU:1', device_type='GPU'),\n",
       " LogicalDevice(name='/device:GPU:2', device_type='GPU'),\n",
       " LogicalDevice(name='/device:GPU:3', device_type='GPU')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.list_logical_devices('GPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_size = (256, 256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to resize the data and flatten images if necessary\n",
    "def transform(dataset, size = (256, 256)):\n",
    "    new_list = []\n",
    "    for i in range(len(dataset)):\n",
    "        #resize the image\n",
    "        temp2 = resize(dataset[i], size, Image.NEAREST)\n",
    "        \n",
    "        #flatten it\n",
    "        new_list.append(temp2.flatten())\n",
    "\n",
    "    return new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imagePreProcess(pathLink, outcomeLink, n_samples = 100, n_comps_filter = 30, size = (256,256)):\n",
    "    \"\"\"\n",
    "    func: Filteres and preprocesses the images for one country\n",
    "    \n",
    "    input:\n",
    "        -pathLink: path to image files\n",
    "        -outcomeLink: path to outcome file\n",
    "        -n_samples: no. of samples in PCA, default 100\n",
    "        -n_comps_filter: no. of components to filter, default 30 \n",
    "        \n",
    "    \n",
    "    output: \n",
    "        -indices for filtered array\n",
    "        -transformed images\n",
    "        -geoframe with matched images\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"Opening files from\", pathLink)\n",
    "    \n",
    "    #get all the pathlinks to the images\n",
    "    raw_images_links = glob(pathLink)\n",
    "    \n",
    "    #open the images\n",
    "    img_arrays = [np.array(Image.open(file_name)) for file_name in raw_images_links]\n",
    "    \n",
    "    #transform images\n",
    "    img_transform = transform(img_arrays, size)\n",
    "    \n",
    "    #print(\"Filtering Images\")\n",
    "    \n",
    "    #filter the images using a PCA\n",
    "    n_samples = 100 #set higher for more accuracy \n",
    "    n_comps_filter = 30 #set higher for more accuracy \n",
    "    \n",
    "    pca_filter = PCA(n_comps_filter)\n",
    "    pca_filter.fit(rd.sample(img_transform, n_samples))\n",
    "    print(f\"Completed training the PCA for filtering with {n_samples} samples and {n_comps_filter} components. \\nvar expl: {round(np.cumsum(pca_filter.explained_variance_ratio_)[n_comps_filter-1]*100)}%\")\n",
    "    img_pca_filter = pca_filter.transform(img_transform)\n",
    "    \n",
    "    #filter out the unique images\n",
    "    img_unique, unique_indices = np.unique(img_pca_filter, axis= 0, return_index= True)\n",
    "    \n",
    "    #print(\"Finding Location of images\")\n",
    "    # Get the location of the images \n",
    "    image_geodf = gpd.GeoDataFrame() # Create an empty geopandas GeoDataFrame\n",
    "    for i in unique_indices:\n",
    "        #extract an array of the geo points from the file name \n",
    "        point_array = [float(point) for point in str.split(ntpath.basename(raw_images_links[i][:-4]),\"_\")]\n",
    "\n",
    "        lat_i = 1\n",
    "        long_i = 0\n",
    "        #calculate the width of the image taken\n",
    "        #https://wiki.openstreetmap.org/wiki/Zoom_levels \n",
    "        meters = 40075016.686 * math.cos(math.radians(point_array[lat_i]))/(2**14)\n",
    "\n",
    "        #the tiles are x-meters wide. Let's find that in degrees: \n",
    "        # (https://stackoverflow.com/questions/25237356/convert-meters-to-decimal-degrees)\n",
    "        width_deg_half = (meters / (111.32 * 1000 * math.cos(point_array[lat_i] * (math.pi / 180))))/2\n",
    "\n",
    "        geo_point = [(point_array[lat_i] - width_deg_half, point_array[long_i]+ width_deg_half),\n",
    "                    (point_array[lat_i] + width_deg_half, point_array[long_i]+ width_deg_half),\n",
    "                    (point_array[lat_i] + width_deg_half, point_array[long_i] - width_deg_half),\n",
    "                    (point_array[lat_i] - width_deg_half, point_array[long_i] - width_deg_half)]\n",
    "\n",
    "        # Create a Shapely polygon from the coordinate-tuple list\n",
    "        image_geodf.loc[i, 'geometry'] = Polygon(geo_point)\n",
    "        # assign an image id. used for merging with actual images and the survey values\n",
    "        image_geodf.loc[i, 'image_id'] = i  \n",
    "\n",
    "        # Set the GeoDataFrame's coordinate system to WGS84 (i.e. epsg code 4326)\n",
    "    image_geodf.crs = from_epsg(4326)\n",
    "    \n",
    "    print(\"Loading Outcome\")\n",
    "    \n",
    "    #load outcome data\n",
    "    outcome_geodf = gpd.read_file(outcomeLink)\n",
    "    \n",
    "    \n",
    "    geo_df = gpd.sjoin(image_geodf, outcome_geodf, how=\"inner\", predicate = \"contains\")\n",
    "    \n",
    "    ## ASSEMBLE DATASET FOR COUNTRY\n",
    "    X_country = []\n",
    "    Y_country = []\n",
    "    Lon_C = []\n",
    "    Lat_C = []\n",
    "    for i in range(len(geo_df)):\n",
    "        obs = geo_df.iloc[i]\n",
    "        X_country.append(img_transform[int(obs['image_id'])])\n",
    "        Y_country.append(obs['wealth'])\n",
    "        Lon_C.append(obs['LONGNUM'])\n",
    "        Lat_C.append(obs['LATNUM'])\n",
    "    \n",
    "    return X_country, Y_country, Lon_C, Lat_C\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening files from image_data/malawi_archive/images/*\n",
      "Completed training the PCA for filtering with 100 samples and 30 components. \n",
      "var expl: 91%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johannes/opt/anaconda3/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Outcome\n",
      "Opening files from image_data/Mali_archive/images/*\n",
      "Completed training the PCA for filtering with 100 samples and 30 components. \n",
      "var expl: 95%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johannes/opt/anaconda3/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Outcome\n",
      "Opening files from image_data/nigeria_archive/images/*\n",
      "Completed training the PCA for filtering with 100 samples and 30 components. \n",
      "var expl: 97%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johannes/opt/anaconda3/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Outcome\n",
      "Opening files from image_data/ethiopia_archive/images/ethiopia_Images/*\n",
      "Completed training the PCA for filtering with 100 samples and 30 components. \n",
      "var expl: 89%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johannes/opt/anaconda3/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Outcome\n"
     ]
    }
   ],
   "source": [
    "X_mala, Y_mala, Lon_mala, Lat_mala =  imagePreProcess(\"image_data/malawi_archive/images/*\", \n",
    "                                  \"outcome_data/mlw_gdf.shp\", size = (128, 128))\n",
    "\n",
    "X_mali, Y_mali,Lon_mali, Lat_mali = imagePreProcess(\"image_data/Mali_archive/images/*\", \n",
    "                                \"outcome_data/mli_gdf.shp\", size = (128, 128))\n",
    "\n",
    "X_ni, Y_ni, Lon_ni, Lat_ni = imagePreProcess(\"image_data/nigeria_archive/images/*\", \n",
    "                                \"outcome_data/ngr_gdf.shp\", size = (128, 128))\n",
    "\n",
    "X_eth, Y_eth, Lon_eth, Lat_eth = imagePreProcess(\"image_data/ethiopia_archive/images/ethiopia_Images/*\", \n",
    "                                \"outcome_data/eth_gdf.shp\", size = (128, 128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gpd.read(\"outcome_data/mlw_gdf.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15691\n",
      "3460\n",
      "9432\n",
      "2797\n"
     ]
    }
   ],
   "source": [
    "print(len(X_mala))\n",
    "print(len(X_mali))\n",
    "print(len(X_ni))\n",
    "print(len(X_eth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31380\n"
     ]
    }
   ],
   "source": [
    "# List of X dataframes\n",
    "X_full = X_mala + X_mali + X_eth + X_ni\n",
    "\n",
    "# List of Y dataframes\n",
    "Y_full = Y_mala + Y_mali + Y_eth + Y_ni\n",
    "\n",
    "print(len(X_full))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split into train and test data set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_full, Y_full, test_size=0.15)\n",
    "\n",
    "n_obs = len(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9385585565359373"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find how many components we should use by just applying a PCA with a range of components\n",
    "\n",
    "#initialize the pca\n",
    "pca = PCA(whiten=True, copy=True, random_state= 10)\n",
    "#fit it\n",
    "pca.fit(rd.sample(X_train,100))\n",
    "\n",
    "#get the cumulative \n",
    "total = np.cumsum(pca.explained_variance_ratio_)\n",
    "total[26]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.71911407 0.04478373 0.03770406 0.02582641 0.01511354 0.00904438\n",
      " 0.00825402 0.00751007 0.0063645  0.00596967 0.00494405 0.00481715\n",
      " 0.00434626 0.00411239 0.00379129 0.00367015 0.00363519 0.00338955\n",
      " 0.00330121 0.00318081 0.00313058 0.00303407 0.00287182 0.00279678\n",
      " 0.002696  ]\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "comp_to_view = 25\n",
    "#let's plot it\n",
    "plot_xs = list(range(comp_to_view))\n",
    "\n",
    "\n",
    "plt.scatter(plot_xs, pca.explained_variance_ratio_[:comp_to_view], color='black', label='Explained variance proportion')\n",
    "plt.scatter(plot_xs, total[:comp_to_view],color='orange', label='Total')\n",
    "\n",
    "plt.legend(loc=2)\n",
    "plt.xlabel('Number of Component', size=16)\n",
    "plt.ylabel('Variance Explained (%)', size=16)\n",
    "#plt.hlines(0.,xmin=0, xmax=comp_to_view, colors = 'blue', linestyles='--')\n",
    "plt.grid('minor')\n",
    "plt.show()\n",
    "\n",
    "print(pca.explained_variance_ratio_[:comp_to_view])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=25, random_state=10, whiten=True)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#initialize the pca\n",
    "\n",
    "n_comp = 25\n",
    "pca = PCA(n_components = n_comp, whiten=True, copy=True, random_state= 10)\n",
    "\n",
    "#fit with random sample of _ observations\n",
    "pca.fit(rd.sample(X_train,10000))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "## batchwise transformation of the \n",
    "\n",
    "reduced = []\n",
    "batchsize = 2500\n",
    "# \n",
    "for i in range(0, len(X_train), batchsize):\n",
    "    if i == 0:\n",
    "        reduced = pca.transform(X_train[i:i+batchsize])\n",
    "        continue \n",
    "    \n",
    "    reduced_batch = pca.transform(X_train[i:i+batchsize])\n",
    "    reduced = np.concatenate((reduced, reduced_batch), axis =0)\n",
    "\n",
    "X_train_pca = np.asarray(reduced)\n",
    "n_obs = len(X_train_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "#function straight copied from\n",
    "def plot_gallery(title, images, n_col, n_row, cmap_p=plt.cm.gray):\n",
    "    plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n",
    "    plt.suptitle(title, size=16)\n",
    "    for i, comp in enumerate(images):\n",
    "        plt.subplot(n_row, n_col, i + 1)\n",
    "        vmax = max(comp.max(), -comp.min())\n",
    "        #print(vmax)\n",
    "        plt.imshow((comp*255).astype(np.uint8),\n",
    "                   interpolation='nearest',\n",
    "                   vmin=-vmax, vmax=vmax \n",
    "                   #cmap = cmap_p\n",
    "                   )\n",
    "        plt.xticks(())\n",
    "        plt.yticks(())\n",
    "    plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x813.6 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eigenimages = pca.components_.reshape(n_comp,128,128,4).copy()\n",
    "plot_gallery(\"PCA\", eigenimages[:,:,:,:3], n_col=5, n_row=5, cmap_p = plt.cm.viridis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_model = LinearRegression()\n",
    "lin_model.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.042648412844900396"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_model.score(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23150"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(X_train_pca))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist((y_train))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators= 100, bootstrap = True)\n",
    "rf.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6412984169913836"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Grid Search on Random Forest Regressor\n",
    "\n",
    "Source and documentation: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html\n",
    "\n",
    "Tuning: \n",
    "- n_estimators: number of trees in the forest\n",
    "- max_depth: maximum depth of the tree \n",
    "- max_features: number of features taken into account when splitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyper_n_list = [100]\n",
    "hyper_depth_list = [1,2,3,4,5, 10,16,18,20,22,24,26, 30, 40, 50, 100, 200]\n",
    "hyper_m_list = [\"auto\", \"sqrt\", \"log2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 51 candidates, totalling 204 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:   11.2s\n",
      "[Parallel(n_jobs=-1)]: Done 176 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done 204 out of 204 | elapsed:  2.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, estimator=RandomForestRegressor(random_state=0), n_jobs=-1,\n",
       "             param_grid={'max_depth': [1, 2, 3, 4, 5, 10, 16, 18, 20, 22, 24,\n",
       "                                       26, 30, 40, 50, 100, 200],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'n_estimators': [100]},\n",
       "             scoring='r2', verbose=1)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = GridSearchCV(RandomForestRegressor(random_state=0),\n",
    "                           {\n",
    "                              'n_estimators':hyper_n_list,\n",
    "                              'max_depth': hyper_depth_list,\n",
    "                              'max_features': hyper_m_list,\n",
    "                            },cv=4, scoring=\"r2\",verbose=1,n_jobs=-1\n",
    "                           )\n",
    "grid_search.fit(X_train_pca,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_df = pd.DataFrame(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.949630</td>\n",
       "      <td>0.010419</td>\n",
       "      <td>0.035241</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>1</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 1, 'max_features': 'auto', 'n_es...</td>\n",
       "      <td>0.035889</td>\n",
       "      <td>0.042431</td>\n",
       "      <td>0.034537</td>\n",
       "      <td>0.029794</td>\n",
       "      <td>0.035663</td>\n",
       "      <td>0.004516</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.950918</td>\n",
       "      <td>0.024106</td>\n",
       "      <td>0.037006</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>1</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 1, 'max_features': 'sqrt', 'n_es...</td>\n",
       "      <td>0.029860</td>\n",
       "      <td>0.029885</td>\n",
       "      <td>0.029107</td>\n",
       "      <td>0.028934</td>\n",
       "      <td>0.029447</td>\n",
       "      <td>0.000430</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.924977</td>\n",
       "      <td>0.017522</td>\n",
       "      <td>0.040025</td>\n",
       "      <td>0.008130</td>\n",
       "      <td>1</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 1, 'max_features': 'log2', 'n_es...</td>\n",
       "      <td>0.028229</td>\n",
       "      <td>0.028112</td>\n",
       "      <td>0.027509</td>\n",
       "      <td>0.027549</td>\n",
       "      <td>0.027850</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.076172</td>\n",
       "      <td>0.050289</td>\n",
       "      <td>0.034936</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>2</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 2, 'max_features': 'auto', 'n_es...</td>\n",
       "      <td>0.074233</td>\n",
       "      <td>0.084475</td>\n",
       "      <td>0.077554</td>\n",
       "      <td>0.069787</td>\n",
       "      <td>0.076512</td>\n",
       "      <td>0.005360</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.452210</td>\n",
       "      <td>0.025175</td>\n",
       "      <td>0.041397</td>\n",
       "      <td>0.010637</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 2, 'max_features': 'sqrt', 'n_es...</td>\n",
       "      <td>0.065875</td>\n",
       "      <td>0.064620</td>\n",
       "      <td>0.064031</td>\n",
       "      <td>0.063997</td>\n",
       "      <td>0.064631</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.259746</td>\n",
       "      <td>0.034208</td>\n",
       "      <td>0.035465</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>2</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 2, 'max_features': 'log2', 'n_es...</td>\n",
       "      <td>0.060006</td>\n",
       "      <td>0.061758</td>\n",
       "      <td>0.059431</td>\n",
       "      <td>0.058995</td>\n",
       "      <td>0.060047</td>\n",
       "      <td>0.001051</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.874525</td>\n",
       "      <td>0.036400</td>\n",
       "      <td>0.040332</td>\n",
       "      <td>0.002655</td>\n",
       "      <td>3</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 3, 'max_features': 'auto', 'n_es...</td>\n",
       "      <td>0.120734</td>\n",
       "      <td>0.138761</td>\n",
       "      <td>0.127616</td>\n",
       "      <td>0.121517</td>\n",
       "      <td>0.127157</td>\n",
       "      <td>0.007210</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.890088</td>\n",
       "      <td>0.017734</td>\n",
       "      <td>0.042874</td>\n",
       "      <td>0.004119</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 3, 'max_features': 'sqrt', 'n_es...</td>\n",
       "      <td>0.101441</td>\n",
       "      <td>0.106160</td>\n",
       "      <td>0.102702</td>\n",
       "      <td>0.100553</td>\n",
       "      <td>0.102714</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.689491</td>\n",
       "      <td>0.025695</td>\n",
       "      <td>0.049755</td>\n",
       "      <td>0.007573</td>\n",
       "      <td>3</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 3, 'max_features': 'log2', 'n_es...</td>\n",
       "      <td>0.098142</td>\n",
       "      <td>0.098514</td>\n",
       "      <td>0.096964</td>\n",
       "      <td>0.093909</td>\n",
       "      <td>0.096882</td>\n",
       "      <td>0.001809</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.859294</td>\n",
       "      <td>0.037296</td>\n",
       "      <td>0.054370</td>\n",
       "      <td>0.004075</td>\n",
       "      <td>4</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 4, 'max_features': 'auto', 'n_es...</td>\n",
       "      <td>0.177366</td>\n",
       "      <td>0.194445</td>\n",
       "      <td>0.179205</td>\n",
       "      <td>0.173823</td>\n",
       "      <td>0.181210</td>\n",
       "      <td>0.007883</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.319708</td>\n",
       "      <td>0.030288</td>\n",
       "      <td>0.042889</td>\n",
       "      <td>0.002360</td>\n",
       "      <td>4</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 4, 'max_features': 'sqrt', 'n_es...</td>\n",
       "      <td>0.147325</td>\n",
       "      <td>0.153041</td>\n",
       "      <td>0.145699</td>\n",
       "      <td>0.147181</td>\n",
       "      <td>0.148311</td>\n",
       "      <td>0.002804</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.910839</td>\n",
       "      <td>0.015550</td>\n",
       "      <td>0.044003</td>\n",
       "      <td>0.003085</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 4, 'max_features': 'log2', 'n_es...</td>\n",
       "      <td>0.139479</td>\n",
       "      <td>0.142879</td>\n",
       "      <td>0.137327</td>\n",
       "      <td>0.139896</td>\n",
       "      <td>0.139895</td>\n",
       "      <td>0.001979</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.050984</td>\n",
       "      <td>0.125781</td>\n",
       "      <td>0.117736</td>\n",
       "      <td>0.077842</td>\n",
       "      <td>5</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 5, 'max_features': 'auto', 'n_es...</td>\n",
       "      <td>0.241463</td>\n",
       "      <td>0.259939</td>\n",
       "      <td>0.242287</td>\n",
       "      <td>0.236491</td>\n",
       "      <td>0.245045</td>\n",
       "      <td>0.008880</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.909659</td>\n",
       "      <td>0.030450</td>\n",
       "      <td>0.061095</td>\n",
       "      <td>0.005073</td>\n",
       "      <td>5</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 5, 'max_features': 'sqrt', 'n_es...</td>\n",
       "      <td>0.194249</td>\n",
       "      <td>0.205723</td>\n",
       "      <td>0.198348</td>\n",
       "      <td>0.193791</td>\n",
       "      <td>0.198028</td>\n",
       "      <td>0.004784</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.456041</td>\n",
       "      <td>0.015242</td>\n",
       "      <td>0.060846</td>\n",
       "      <td>0.002849</td>\n",
       "      <td>5</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 5, 'max_features': 'log2', 'n_es...</td>\n",
       "      <td>0.187063</td>\n",
       "      <td>0.194742</td>\n",
       "      <td>0.192525</td>\n",
       "      <td>0.185750</td>\n",
       "      <td>0.190020</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17.226959</td>\n",
       "      <td>0.075029</td>\n",
       "      <td>0.101144</td>\n",
       "      <td>0.012348</td>\n",
       "      <td>10</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 10, 'max_features': 'auto', 'n_e...</td>\n",
       "      <td>0.528379</td>\n",
       "      <td>0.531232</td>\n",
       "      <td>0.497118</td>\n",
       "      <td>0.514094</td>\n",
       "      <td>0.517706</td>\n",
       "      <td>0.013544</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.860513</td>\n",
       "      <td>0.054498</td>\n",
       "      <td>0.080335</td>\n",
       "      <td>0.005175</td>\n",
       "      <td>10</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 10, 'max_features': 'sqrt', 'n_e...</td>\n",
       "      <td>0.463147</td>\n",
       "      <td>0.466680</td>\n",
       "      <td>0.459342</td>\n",
       "      <td>0.447898</td>\n",
       "      <td>0.459267</td>\n",
       "      <td>0.007058</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.932518</td>\n",
       "      <td>0.049030</td>\n",
       "      <td>0.093097</td>\n",
       "      <td>0.018033</td>\n",
       "      <td>10</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 10, 'max_features': 'log2', 'n_e...</td>\n",
       "      <td>0.456089</td>\n",
       "      <td>0.445671</td>\n",
       "      <td>0.448635</td>\n",
       "      <td>0.447150</td>\n",
       "      <td>0.449386</td>\n",
       "      <td>0.004009</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.353306</td>\n",
       "      <td>0.129950</td>\n",
       "      <td>0.133675</td>\n",
       "      <td>0.005039</td>\n",
       "      <td>16</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 16, 'max_features': 'auto', 'n_e...</td>\n",
       "      <td>0.614038</td>\n",
       "      <td>0.615929</td>\n",
       "      <td>0.601415</td>\n",
       "      <td>0.611517</td>\n",
       "      <td>0.610725</td>\n",
       "      <td>0.005598</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.083833</td>\n",
       "      <td>0.010201</td>\n",
       "      <td>0.122113</td>\n",
       "      <td>0.008024</td>\n",
       "      <td>16</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 16, 'max_features': 'sqrt', 'n_e...</td>\n",
       "      <td>0.608006</td>\n",
       "      <td>0.607410</td>\n",
       "      <td>0.590449</td>\n",
       "      <td>0.603196</td>\n",
       "      <td>0.602265</td>\n",
       "      <td>0.007070</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.609204</td>\n",
       "      <td>0.027646</td>\n",
       "      <td>0.129255</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>16</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 16, 'max_features': 'log2', 'n_e...</td>\n",
       "      <td>0.606324</td>\n",
       "      <td>0.603241</td>\n",
       "      <td>0.587117</td>\n",
       "      <td>0.597159</td>\n",
       "      <td>0.598460</td>\n",
       "      <td>0.007332</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20.997469</td>\n",
       "      <td>0.135794</td>\n",
       "      <td>0.144909</td>\n",
       "      <td>0.017379</td>\n",
       "      <td>18</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 18, 'max_features': 'auto', 'n_e...</td>\n",
       "      <td>0.614896</td>\n",
       "      <td>0.618700</td>\n",
       "      <td>0.603795</td>\n",
       "      <td>0.613288</td>\n",
       "      <td>0.612670</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.550301</td>\n",
       "      <td>0.068188</td>\n",
       "      <td>0.135642</td>\n",
       "      <td>0.011052</td>\n",
       "      <td>18</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 18, 'max_features': 'sqrt', 'n_e...</td>\n",
       "      <td>0.614297</td>\n",
       "      <td>0.616492</td>\n",
       "      <td>0.600811</td>\n",
       "      <td>0.610816</td>\n",
       "      <td>0.610604</td>\n",
       "      <td>0.006006</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.724996</td>\n",
       "      <td>0.053743</td>\n",
       "      <td>0.138137</td>\n",
       "      <td>0.013023</td>\n",
       "      <td>18</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 18, 'max_features': 'log2', 'n_e...</td>\n",
       "      <td>0.614018</td>\n",
       "      <td>0.614798</td>\n",
       "      <td>0.599487</td>\n",
       "      <td>0.608827</td>\n",
       "      <td>0.609282</td>\n",
       "      <td>0.006104</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.737890</td>\n",
       "      <td>0.184439</td>\n",
       "      <td>0.157404</td>\n",
       "      <td>0.012918</td>\n",
       "      <td>20</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 20, 'max_features': 'auto', 'n_e...</td>\n",
       "      <td>0.614778</td>\n",
       "      <td>0.619487</td>\n",
       "      <td>0.603312</td>\n",
       "      <td>0.613235</td>\n",
       "      <td>0.612703</td>\n",
       "      <td>0.005891</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.773333</td>\n",
       "      <td>0.031220</td>\n",
       "      <td>0.149456</td>\n",
       "      <td>0.010552</td>\n",
       "      <td>20</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 20, 'max_features': 'sqrt', 'n_e...</td>\n",
       "      <td>0.615000</td>\n",
       "      <td>0.618286</td>\n",
       "      <td>0.602600</td>\n",
       "      <td>0.612833</td>\n",
       "      <td>0.612180</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6.159119</td>\n",
       "      <td>0.192521</td>\n",
       "      <td>0.195156</td>\n",
       "      <td>0.033195</td>\n",
       "      <td>20</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 20, 'max_features': 'log2', 'n_e...</td>\n",
       "      <td>0.615082</td>\n",
       "      <td>0.618092</td>\n",
       "      <td>0.602924</td>\n",
       "      <td>0.612173</td>\n",
       "      <td>0.612068</td>\n",
       "      <td>0.005679</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>24.296207</td>\n",
       "      <td>0.113232</td>\n",
       "      <td>0.146911</td>\n",
       "      <td>0.006003</td>\n",
       "      <td>22</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 22, 'max_features': 'auto', 'n_e...</td>\n",
       "      <td>0.614563</td>\n",
       "      <td>0.619733</td>\n",
       "      <td>0.602956</td>\n",
       "      <td>0.613062</td>\n",
       "      <td>0.612579</td>\n",
       "      <td>0.006082</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7.028628</td>\n",
       "      <td>0.097687</td>\n",
       "      <td>0.161094</td>\n",
       "      <td>0.006424</td>\n",
       "      <td>22</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 22, 'max_features': 'sqrt', 'n_e...</td>\n",
       "      <td>0.614741</td>\n",
       "      <td>0.619581</td>\n",
       "      <td>0.603166</td>\n",
       "      <td>0.613134</td>\n",
       "      <td>0.612655</td>\n",
       "      <td>0.005970</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5.357378</td>\n",
       "      <td>0.055816</td>\n",
       "      <td>0.155600</td>\n",
       "      <td>0.012181</td>\n",
       "      <td>22</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 22, 'max_features': 'log2', 'n_e...</td>\n",
       "      <td>0.615115</td>\n",
       "      <td>0.619369</td>\n",
       "      <td>0.603130</td>\n",
       "      <td>0.613101</td>\n",
       "      <td>0.612679</td>\n",
       "      <td>0.005959</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>22.601592</td>\n",
       "      <td>0.183297</td>\n",
       "      <td>0.153152</td>\n",
       "      <td>0.008735</td>\n",
       "      <td>24</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 24, 'max_features': 'auto', 'n_e...</td>\n",
       "      <td>0.614480</td>\n",
       "      <td>0.619777</td>\n",
       "      <td>0.602783</td>\n",
       "      <td>0.612927</td>\n",
       "      <td>0.612492</td>\n",
       "      <td>0.006154</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>6.041053</td>\n",
       "      <td>0.021642</td>\n",
       "      <td>0.149994</td>\n",
       "      <td>0.004958</td>\n",
       "      <td>24</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 24, 'max_features': 'sqrt', 'n_e...</td>\n",
       "      <td>0.614622</td>\n",
       "      <td>0.619805</td>\n",
       "      <td>0.602938</td>\n",
       "      <td>0.613050</td>\n",
       "      <td>0.612604</td>\n",
       "      <td>0.006115</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.095674</td>\n",
       "      <td>0.065887</td>\n",
       "      <td>0.144513</td>\n",
       "      <td>0.007165</td>\n",
       "      <td>24</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 24, 'max_features': 'log2', 'n_e...</td>\n",
       "      <td>0.614640</td>\n",
       "      <td>0.619783</td>\n",
       "      <td>0.602977</td>\n",
       "      <td>0.613028</td>\n",
       "      <td>0.612607</td>\n",
       "      <td>0.006094</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>23.208893</td>\n",
       "      <td>0.254004</td>\n",
       "      <td>0.161419</td>\n",
       "      <td>0.019743</td>\n",
       "      <td>26</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 26, 'max_features': 'auto', 'n_e...</td>\n",
       "      <td>0.614405</td>\n",
       "      <td>0.619808</td>\n",
       "      <td>0.602707</td>\n",
       "      <td>0.612913</td>\n",
       "      <td>0.612458</td>\n",
       "      <td>0.006186</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>6.417097</td>\n",
       "      <td>0.098585</td>\n",
       "      <td>0.156179</td>\n",
       "      <td>0.002011</td>\n",
       "      <td>26</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 26, 'max_features': 'sqrt', 'n_e...</td>\n",
       "      <td>0.614437</td>\n",
       "      <td>0.619760</td>\n",
       "      <td>0.602803</td>\n",
       "      <td>0.612928</td>\n",
       "      <td>0.612482</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.461129</td>\n",
       "      <td>0.065127</td>\n",
       "      <td>0.164718</td>\n",
       "      <td>0.007452</td>\n",
       "      <td>26</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 26, 'max_features': 'log2', 'n_e...</td>\n",
       "      <td>0.614545</td>\n",
       "      <td>0.619749</td>\n",
       "      <td>0.602846</td>\n",
       "      <td>0.612946</td>\n",
       "      <td>0.612522</td>\n",
       "      <td>0.006126</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>23.255589</td>\n",
       "      <td>0.120674</td>\n",
       "      <td>0.141747</td>\n",
       "      <td>0.007491</td>\n",
       "      <td>30</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 30, 'max_features': 'auto', 'n_e...</td>\n",
       "      <td>0.614395</td>\n",
       "      <td>0.619823</td>\n",
       "      <td>0.602668</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006206</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>6.524611</td>\n",
       "      <td>0.092915</td>\n",
       "      <td>0.165455</td>\n",
       "      <td>0.012324</td>\n",
       "      <td>30</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 30, 'max_features': 'sqrt', 'n_e...</td>\n",
       "      <td>0.614403</td>\n",
       "      <td>0.619816</td>\n",
       "      <td>0.602679</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612449</td>\n",
       "      <td>0.006200</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5.621003</td>\n",
       "      <td>0.090340</td>\n",
       "      <td>0.152849</td>\n",
       "      <td>0.022378</td>\n",
       "      <td>30</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 30, 'max_features': 'log2', 'n_e...</td>\n",
       "      <td>0.614399</td>\n",
       "      <td>0.619826</td>\n",
       "      <td>0.602672</td>\n",
       "      <td>0.612906</td>\n",
       "      <td>0.612451</td>\n",
       "      <td>0.006205</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>22.092839</td>\n",
       "      <td>0.118031</td>\n",
       "      <td>0.140231</td>\n",
       "      <td>0.006584</td>\n",
       "      <td>40</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 40, 'max_features': 'auto', 'n_e...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>6.114252</td>\n",
       "      <td>0.013006</td>\n",
       "      <td>0.148962</td>\n",
       "      <td>0.005820</td>\n",
       "      <td>40</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 40, 'max_features': 'sqrt', 'n_e...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>5.023711</td>\n",
       "      <td>0.068505</td>\n",
       "      <td>0.144494</td>\n",
       "      <td>0.009624</td>\n",
       "      <td>40</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 40, 'max_features': 'log2', 'n_e...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>21.526620</td>\n",
       "      <td>0.163161</td>\n",
       "      <td>0.144430</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>50</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 50, 'max_features': 'auto', 'n_e...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.833962</td>\n",
       "      <td>0.050045</td>\n",
       "      <td>0.149756</td>\n",
       "      <td>0.006095</td>\n",
       "      <td>50</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 50, 'max_features': 'sqrt', 'n_e...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4.957751</td>\n",
       "      <td>0.067461</td>\n",
       "      <td>0.146209</td>\n",
       "      <td>0.006499</td>\n",
       "      <td>50</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 50, 'max_features': 'log2', 'n_e...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>23.439360</td>\n",
       "      <td>0.185782</td>\n",
       "      <td>0.150526</td>\n",
       "      <td>0.007994</td>\n",
       "      <td>100</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 100, 'max_features': 'auto', 'n_...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6.076990</td>\n",
       "      <td>0.073657</td>\n",
       "      <td>0.158154</td>\n",
       "      <td>0.014393</td>\n",
       "      <td>100</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 100, 'max_features': 'sqrt', 'n_...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5.350747</td>\n",
       "      <td>0.139145</td>\n",
       "      <td>0.194366</td>\n",
       "      <td>0.053834</td>\n",
       "      <td>100</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 100, 'max_features': 'log2', 'n_...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>19.557530</td>\n",
       "      <td>0.084090</td>\n",
       "      <td>0.084666</td>\n",
       "      <td>0.001443</td>\n",
       "      <td>200</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 200, 'max_features': 'auto', 'n_...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>7.408737</td>\n",
       "      <td>0.085529</td>\n",
       "      <td>0.165851</td>\n",
       "      <td>0.017063</td>\n",
       "      <td>200</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 200, 'max_features': 'sqrt', 'n_...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4.346060</td>\n",
       "      <td>0.126134</td>\n",
       "      <td>0.109299</td>\n",
       "      <td>0.007460</td>\n",
       "      <td>200</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 200, 'max_features': 'log2', 'n_...</td>\n",
       "      <td>0.614394</td>\n",
       "      <td>0.619821</td>\n",
       "      <td>0.602670</td>\n",
       "      <td>0.612900</td>\n",
       "      <td>0.612446</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        2.949630      0.010419         0.035241        0.000689   \n",
       "1        0.950918      0.024106         0.037006        0.004184   \n",
       "2        0.924977      0.017522         0.040025        0.008130   \n",
       "3        5.076172      0.050289         0.034936        0.001593   \n",
       "4        1.452210      0.025175         0.041397        0.010637   \n",
       "5        1.259746      0.034208         0.035465        0.001244   \n",
       "6        6.874525      0.036400         0.040332        0.002655   \n",
       "7        1.890088      0.017734         0.042874        0.004119   \n",
       "8        1.689491      0.025695         0.049755        0.007573   \n",
       "9        8.859294      0.037296         0.054370        0.004075   \n",
       "10       2.319708      0.030288         0.042889        0.002360   \n",
       "11       1.910839      0.015550         0.044003        0.003085   \n",
       "12      11.050984      0.125781         0.117736        0.077842   \n",
       "13       2.909659      0.030450         0.061095        0.005073   \n",
       "14       2.456041      0.015242         0.060846        0.002849   \n",
       "15      17.226959      0.075029         0.101144        0.012348   \n",
       "16       4.860513      0.054498         0.080335        0.005175   \n",
       "17       3.932518      0.049030         0.093097        0.018033   \n",
       "18      20.353306      0.129950         0.133675        0.005039   \n",
       "19       5.083833      0.010201         0.122113        0.008024   \n",
       "20       4.609204      0.027646         0.129255        0.010526   \n",
       "21      20.997469      0.135794         0.144909        0.017379   \n",
       "22       5.550301      0.068188         0.135642        0.011052   \n",
       "23       4.724996      0.053743         0.138137        0.013023   \n",
       "24      23.737890      0.184439         0.157404        0.012918   \n",
       "25       5.773333      0.031220         0.149456        0.010552   \n",
       "26       6.159119      0.192521         0.195156        0.033195   \n",
       "27      24.296207      0.113232         0.146911        0.006003   \n",
       "28       7.028628      0.097687         0.161094        0.006424   \n",
       "29       5.357378      0.055816         0.155600        0.012181   \n",
       "30      22.601592      0.183297         0.153152        0.008735   \n",
       "31       6.041053      0.021642         0.149994        0.004958   \n",
       "32       5.095674      0.065887         0.144513        0.007165   \n",
       "33      23.208893      0.254004         0.161419        0.019743   \n",
       "34       6.417097      0.098585         0.156179        0.002011   \n",
       "35       5.461129      0.065127         0.164718        0.007452   \n",
       "36      23.255589      0.120674         0.141747        0.007491   \n",
       "37       6.524611      0.092915         0.165455        0.012324   \n",
       "38       5.621003      0.090340         0.152849        0.022378   \n",
       "39      22.092839      0.118031         0.140231        0.006584   \n",
       "40       6.114252      0.013006         0.148962        0.005820   \n",
       "41       5.023711      0.068505         0.144494        0.009624   \n",
       "42      21.526620      0.163161         0.144430        0.005274   \n",
       "43       5.833962      0.050045         0.149756        0.006095   \n",
       "44       4.957751      0.067461         0.146209        0.006499   \n",
       "45      23.439360      0.185782         0.150526        0.007994   \n",
       "46       6.076990      0.073657         0.158154        0.014393   \n",
       "47       5.350747      0.139145         0.194366        0.053834   \n",
       "48      19.557530      0.084090         0.084666        0.001443   \n",
       "49       7.408737      0.085529         0.165851        0.017063   \n",
       "50       4.346060      0.126134         0.109299        0.007460   \n",
       "\n",
       "   param_max_depth param_max_features param_n_estimators  \\\n",
       "0                1               auto                100   \n",
       "1                1               sqrt                100   \n",
       "2                1               log2                100   \n",
       "3                2               auto                100   \n",
       "4                2               sqrt                100   \n",
       "5                2               log2                100   \n",
       "6                3               auto                100   \n",
       "7                3               sqrt                100   \n",
       "8                3               log2                100   \n",
       "9                4               auto                100   \n",
       "10               4               sqrt                100   \n",
       "11               4               log2                100   \n",
       "12               5               auto                100   \n",
       "13               5               sqrt                100   \n",
       "14               5               log2                100   \n",
       "15              10               auto                100   \n",
       "16              10               sqrt                100   \n",
       "17              10               log2                100   \n",
       "18              16               auto                100   \n",
       "19              16               sqrt                100   \n",
       "20              16               log2                100   \n",
       "21              18               auto                100   \n",
       "22              18               sqrt                100   \n",
       "23              18               log2                100   \n",
       "24              20               auto                100   \n",
       "25              20               sqrt                100   \n",
       "26              20               log2                100   \n",
       "27              22               auto                100   \n",
       "28              22               sqrt                100   \n",
       "29              22               log2                100   \n",
       "30              24               auto                100   \n",
       "31              24               sqrt                100   \n",
       "32              24               log2                100   \n",
       "33              26               auto                100   \n",
       "34              26               sqrt                100   \n",
       "35              26               log2                100   \n",
       "36              30               auto                100   \n",
       "37              30               sqrt                100   \n",
       "38              30               log2                100   \n",
       "39              40               auto                100   \n",
       "40              40               sqrt                100   \n",
       "41              40               log2                100   \n",
       "42              50               auto                100   \n",
       "43              50               sqrt                100   \n",
       "44              50               log2                100   \n",
       "45             100               auto                100   \n",
       "46             100               sqrt                100   \n",
       "47             100               log2                100   \n",
       "48             200               auto                100   \n",
       "49             200               sqrt                100   \n",
       "50             200               log2                100   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'max_depth': 1, 'max_features': 'auto', 'n_es...           0.035889   \n",
       "1   {'max_depth': 1, 'max_features': 'sqrt', 'n_es...           0.029860   \n",
       "2   {'max_depth': 1, 'max_features': 'log2', 'n_es...           0.028229   \n",
       "3   {'max_depth': 2, 'max_features': 'auto', 'n_es...           0.074233   \n",
       "4   {'max_depth': 2, 'max_features': 'sqrt', 'n_es...           0.065875   \n",
       "5   {'max_depth': 2, 'max_features': 'log2', 'n_es...           0.060006   \n",
       "6   {'max_depth': 3, 'max_features': 'auto', 'n_es...           0.120734   \n",
       "7   {'max_depth': 3, 'max_features': 'sqrt', 'n_es...           0.101441   \n",
       "8   {'max_depth': 3, 'max_features': 'log2', 'n_es...           0.098142   \n",
       "9   {'max_depth': 4, 'max_features': 'auto', 'n_es...           0.177366   \n",
       "10  {'max_depth': 4, 'max_features': 'sqrt', 'n_es...           0.147325   \n",
       "11  {'max_depth': 4, 'max_features': 'log2', 'n_es...           0.139479   \n",
       "12  {'max_depth': 5, 'max_features': 'auto', 'n_es...           0.241463   \n",
       "13  {'max_depth': 5, 'max_features': 'sqrt', 'n_es...           0.194249   \n",
       "14  {'max_depth': 5, 'max_features': 'log2', 'n_es...           0.187063   \n",
       "15  {'max_depth': 10, 'max_features': 'auto', 'n_e...           0.528379   \n",
       "16  {'max_depth': 10, 'max_features': 'sqrt', 'n_e...           0.463147   \n",
       "17  {'max_depth': 10, 'max_features': 'log2', 'n_e...           0.456089   \n",
       "18  {'max_depth': 16, 'max_features': 'auto', 'n_e...           0.614038   \n",
       "19  {'max_depth': 16, 'max_features': 'sqrt', 'n_e...           0.608006   \n",
       "20  {'max_depth': 16, 'max_features': 'log2', 'n_e...           0.606324   \n",
       "21  {'max_depth': 18, 'max_features': 'auto', 'n_e...           0.614896   \n",
       "22  {'max_depth': 18, 'max_features': 'sqrt', 'n_e...           0.614297   \n",
       "23  {'max_depth': 18, 'max_features': 'log2', 'n_e...           0.614018   \n",
       "24  {'max_depth': 20, 'max_features': 'auto', 'n_e...           0.614778   \n",
       "25  {'max_depth': 20, 'max_features': 'sqrt', 'n_e...           0.615000   \n",
       "26  {'max_depth': 20, 'max_features': 'log2', 'n_e...           0.615082   \n",
       "27  {'max_depth': 22, 'max_features': 'auto', 'n_e...           0.614563   \n",
       "28  {'max_depth': 22, 'max_features': 'sqrt', 'n_e...           0.614741   \n",
       "29  {'max_depth': 22, 'max_features': 'log2', 'n_e...           0.615115   \n",
       "30  {'max_depth': 24, 'max_features': 'auto', 'n_e...           0.614480   \n",
       "31  {'max_depth': 24, 'max_features': 'sqrt', 'n_e...           0.614622   \n",
       "32  {'max_depth': 24, 'max_features': 'log2', 'n_e...           0.614640   \n",
       "33  {'max_depth': 26, 'max_features': 'auto', 'n_e...           0.614405   \n",
       "34  {'max_depth': 26, 'max_features': 'sqrt', 'n_e...           0.614437   \n",
       "35  {'max_depth': 26, 'max_features': 'log2', 'n_e...           0.614545   \n",
       "36  {'max_depth': 30, 'max_features': 'auto', 'n_e...           0.614395   \n",
       "37  {'max_depth': 30, 'max_features': 'sqrt', 'n_e...           0.614403   \n",
       "38  {'max_depth': 30, 'max_features': 'log2', 'n_e...           0.614399   \n",
       "39  {'max_depth': 40, 'max_features': 'auto', 'n_e...           0.614394   \n",
       "40  {'max_depth': 40, 'max_features': 'sqrt', 'n_e...           0.614394   \n",
       "41  {'max_depth': 40, 'max_features': 'log2', 'n_e...           0.614394   \n",
       "42  {'max_depth': 50, 'max_features': 'auto', 'n_e...           0.614394   \n",
       "43  {'max_depth': 50, 'max_features': 'sqrt', 'n_e...           0.614394   \n",
       "44  {'max_depth': 50, 'max_features': 'log2', 'n_e...           0.614394   \n",
       "45  {'max_depth': 100, 'max_features': 'auto', 'n_...           0.614394   \n",
       "46  {'max_depth': 100, 'max_features': 'sqrt', 'n_...           0.614394   \n",
       "47  {'max_depth': 100, 'max_features': 'log2', 'n_...           0.614394   \n",
       "48  {'max_depth': 200, 'max_features': 'auto', 'n_...           0.614394   \n",
       "49  {'max_depth': 200, 'max_features': 'sqrt', 'n_...           0.614394   \n",
       "50  {'max_depth': 200, 'max_features': 'log2', 'n_...           0.614394   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  mean_test_score  \\\n",
       "0            0.042431           0.034537           0.029794         0.035663   \n",
       "1            0.029885           0.029107           0.028934         0.029447   \n",
       "2            0.028112           0.027509           0.027549         0.027850   \n",
       "3            0.084475           0.077554           0.069787         0.076512   \n",
       "4            0.064620           0.064031           0.063997         0.064631   \n",
       "5            0.061758           0.059431           0.058995         0.060047   \n",
       "6            0.138761           0.127616           0.121517         0.127157   \n",
       "7            0.106160           0.102702           0.100553         0.102714   \n",
       "8            0.098514           0.096964           0.093909         0.096882   \n",
       "9            0.194445           0.179205           0.173823         0.181210   \n",
       "10           0.153041           0.145699           0.147181         0.148311   \n",
       "11           0.142879           0.137327           0.139896         0.139895   \n",
       "12           0.259939           0.242287           0.236491         0.245045   \n",
       "13           0.205723           0.198348           0.193791         0.198028   \n",
       "14           0.194742           0.192525           0.185750         0.190020   \n",
       "15           0.531232           0.497118           0.514094         0.517706   \n",
       "16           0.466680           0.459342           0.447898         0.459267   \n",
       "17           0.445671           0.448635           0.447150         0.449386   \n",
       "18           0.615929           0.601415           0.611517         0.610725   \n",
       "19           0.607410           0.590449           0.603196         0.602265   \n",
       "20           0.603241           0.587117           0.597159         0.598460   \n",
       "21           0.618700           0.603795           0.613288         0.612670   \n",
       "22           0.616492           0.600811           0.610816         0.610604   \n",
       "23           0.614798           0.599487           0.608827         0.609282   \n",
       "24           0.619487           0.603312           0.613235         0.612703   \n",
       "25           0.618286           0.602600           0.612833         0.612180   \n",
       "26           0.618092           0.602924           0.612173         0.612068   \n",
       "27           0.619733           0.602956           0.613062         0.612579   \n",
       "28           0.619581           0.603166           0.613134         0.612655   \n",
       "29           0.619369           0.603130           0.613101         0.612679   \n",
       "30           0.619777           0.602783           0.612927         0.612492   \n",
       "31           0.619805           0.602938           0.613050         0.612604   \n",
       "32           0.619783           0.602977           0.613028         0.612607   \n",
       "33           0.619808           0.602707           0.612913         0.612458   \n",
       "34           0.619760           0.602803           0.612928         0.612482   \n",
       "35           0.619749           0.602846           0.612946         0.612522   \n",
       "36           0.619823           0.602668           0.612900         0.612446   \n",
       "37           0.619816           0.602679           0.612900         0.612449   \n",
       "38           0.619826           0.602672           0.612906         0.612451   \n",
       "39           0.619821           0.602670           0.612900         0.612446   \n",
       "40           0.619821           0.602670           0.612900         0.612446   \n",
       "41           0.619821           0.602670           0.612900         0.612446   \n",
       "42           0.619821           0.602670           0.612900         0.612446   \n",
       "43           0.619821           0.602670           0.612900         0.612446   \n",
       "44           0.619821           0.602670           0.612900         0.612446   \n",
       "45           0.619821           0.602670           0.612900         0.612446   \n",
       "46           0.619821           0.602670           0.612900         0.612446   \n",
       "47           0.619821           0.602670           0.612900         0.612446   \n",
       "48           0.619821           0.602670           0.612900         0.612446   \n",
       "49           0.619821           0.602670           0.612900         0.612446   \n",
       "50           0.619821           0.602670           0.612900         0.612446   \n",
       "\n",
       "    std_test_score  rank_test_score  \n",
       "0         0.004516               49  \n",
       "1         0.000430               50  \n",
       "2         0.000324               51  \n",
       "3         0.005360               46  \n",
       "4         0.000760               47  \n",
       "5         0.001051               48  \n",
       "6         0.007210               43  \n",
       "7         0.002131               44  \n",
       "8         0.001809               45  \n",
       "9         0.007883               40  \n",
       "10        0.002804               41  \n",
       "11        0.001979               42  \n",
       "12        0.008880               37  \n",
       "13        0.004784               38  \n",
       "14        0.003726               39  \n",
       "15        0.013544               34  \n",
       "16        0.007058               35  \n",
       "17        0.004009               36  \n",
       "18        0.005598               29  \n",
       "19        0.007070               32  \n",
       "20        0.007332               33  \n",
       "21        0.005488                3  \n",
       "22        0.006006               30  \n",
       "23        0.006104               31  \n",
       "24        0.005891                1  \n",
       "25        0.005862               27  \n",
       "26        0.005679               28  \n",
       "27        0.006082                7  \n",
       "28        0.005970                4  \n",
       "29        0.005959                2  \n",
       "30        0.006154                9  \n",
       "31        0.006115                6  \n",
       "32        0.006094                5  \n",
       "33        0.006186               11  \n",
       "34        0.006138               10  \n",
       "35        0.006126                8  \n",
       "36        0.006206               14  \n",
       "37        0.006200               13  \n",
       "38        0.006205               12  \n",
       "39        0.006204               15  \n",
       "40        0.006204               15  \n",
       "41        0.006204               15  \n",
       "42        0.006204               15  \n",
       "43        0.006204               15  \n",
       "44        0.006204               15  \n",
       "45        0.006204               15  \n",
       "46        0.006204               15  \n",
       "47        0.006204               15  \n",
       "48        0.006204               15  \n",
       "49        0.006204               15  \n",
       "50        0.006204               15  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hyperparam_plot2d(df, param, outcome, group_var = None):\n",
    "\n",
    "    '''\n",
    "    func: function to plot the performance of a parameter\n",
    "    - df (dataframe): cross-validation grid search dataframe\n",
    "\n",
    "    params have to be column names in DF\n",
    "    - param (str): which parameter to asses\n",
    "    - outcome (str): which outcome to find the max for\n",
    "    - group_var (str): grouping variable (default None)\n",
    "    '''\n",
    "\n",
    "    group_var_list = np.unique(np.array(df[group_var]))\n",
    "    plt.figure()\n",
    "    #print(np.argmax(np.asarray(df[outcome])))\n",
    "    best_score = np.max(np.asarray(df[outcome]))\n",
    "    best_score_param = np.asarray(df[param])[np.argmax(np.asarray(df[outcome]))]\n",
    "    if group_var != None:\n",
    "        best_score_group = np.asarray(df[group_var])[np.argmax(np.asarray(df[outcome]))]\n",
    "        #print(best_score_group)\n",
    "\n",
    "    if group_var == None:\n",
    "        plt.plot(param,outcome,data = df)\n",
    "        plt.xlabel(param)\n",
    "        plt.ylabel(outcome)\n",
    "        #plt.grid()\n",
    "        plt.vlines(best_score_param, 0, np.max(np.asarray(df[outcome])+.05))\n",
    "        plt.text(best_score_param,best_score - (best_score*.2),  f'Best parameter: {best_score_param} with score: {best_score}', size = 12)\n",
    "        plt.legend()\n",
    "    else:\n",
    "        for inst in group_var_list:\n",
    "            plt.plot(param,outcome,label = inst, data = df[df[group_var]== inst])\n",
    "            plt.xlabel(param)\n",
    "            plt.ylabel(outcome)\n",
    "            #plt.grid()\n",
    "            plt.text(best_score_param,best_score - (best_score*.25),  f'Best parameter: {best_score_param} \\n with score: {round(best_score,3)} \\nin group:{best_score_group}', size = 12)\n",
    "            plt.vlines(best_score_param, 0, best_score+.05)\n",
    "            plt.legend(loc = 4)\n",
    "            plt.ylim(bottom =0 )\n",
    "    plt.grid()\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hyperparam_plot2d(cv_df, 'param_max_depth', 'mean_test_score', 'param_max_features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hyperparam_plot2d(cv_df, 'param_max_features' ,'mean_test_score','param_max_depth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_forest = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_forest.predict(X_train_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Score on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_pca = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6189953504072113"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_forest.score(X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03769696694123925"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_model.score(X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import gc\n",
    "from keras.models import Sequential,Input,Model\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from tensorflow.keras import applications\n",
    "from tensorflow.keras import callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_cnn = np.asarray(X_train).reshape(n_obs, 128, 128, 4)\n",
    "#X_train_cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_cnn = X_train_cnn[:,:,:,:3].astype('float32') # Taking out the fourth channel as ghe information is the same everywhere\n",
    "y_train_cnn = np.asarray(y_train).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train1_cnn, X_val_cnn, y_train1_cnn, y_val_cnn = train_test_split(X_train_cnn, y_train_cnn, test_size=0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "462"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(y_train1_cnn, color = 'b')\n",
    "sns.kdeplot(y_val_cnn, color = 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "## prep the dataset with to be passed to resnet\n",
    "X_cnn_res50 = applications.resnet_v2.preprocess_input(np.copy(X_train1_cnn))\n",
    "\n",
    "X_cnn_res50_val = applications.resnet_v2.preprocess_input(np.copy(X_val_cnn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         [ 0.22352946,  0.15294123, -0.0745098 ],\n",
       "         ...,\n",
       "         [ 0.27058828,  0.16078436, -0.04313725],\n",
       "         [ 0.28627455,  0.17647064, -0.02745098],\n",
       "         [ 0.28627455,  0.17647064, -0.02745098]],\n",
       "\n",
       "        [[ 0.254902  ,  0.18431377, -0.04313725],\n",
       "         [ 0.20000005,  0.12941182, -0.09803921],\n",
       "         [ 0.28627455,  0.21568632, -0.01176471],\n",
       "         ...,\n",
       "         [ 0.2941177 ,  0.18431377, -0.01960784],\n",
       "         [ 0.28627455,  0.17647064, -0.02745098],\n",
       "         [ 0.28627455,  0.17647064, -0.02745098]],\n",
       "\n",
       "        [[ 0.1686275 ,  0.09803927, -0.12941176],\n",
       "         [ 0.13725495,  0.06666672, -0.1607843 ],\n",
       "         [ 0.2941177 ,  0.22352946, -0.00392157],\n",
       "         ...,\n",
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       "         [ 0.30980396,  0.20000005, -0.00392157],\n",
       "         [ 0.2941177 ,  0.18431377, -0.01960784]]],\n",
       "\n",
       "\n",
       "       [[[-0.44313723, -0.4352941 , -0.4823529 ],\n",
       "         [-0.34117645, -0.3333333 , -0.38039213],\n",
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       "         [ 0.01176476, -0.05098039, -0.15294117]],\n",
       "\n",
       "        [[-0.46666664, -0.4588235 , -0.5058824 ],\n",
       "         [-0.3098039 , -0.30196077, -0.3490196 ],\n",
       "         [-0.40392154, -0.3960784 , -0.44313723],\n",
       "         ...,\n",
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       "         [-0.05882353, -0.1607843 , -0.26274508],\n",
       "         [-0.03529412, -0.12941176, -0.23137254]],\n",
       "\n",
       "        [[-0.38039213, -0.36470586, -0.41960782],\n",
       "         [-0.3490196 , -0.34117645, -0.3960784 ],\n",
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       "         ...,\n",
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       "\n",
       "        ...,\n",
       "\n",
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       "         [-0.12156862, -0.1607843 , -0.2862745 ],\n",
       "         [-0.15294117, -0.19215685, -0.3333333 ],\n",
       "         ...,\n",
       "         [ 0.26274514,  0.16078436,  0.02745104],\n",
       "         [ 0.30980396,  0.19215691,  0.05882359],\n",
       "         [ 0.38823533,  0.22352946,  0.09019613]],\n",
       "\n",
       "        [[-0.25490195, -0.24705881, -0.3490196 ],\n",
       "         [-0.2235294 , -0.23921567, -0.3490196 ],\n",
       "         [-0.1372549 , -0.15294117, -0.2862745 ],\n",
       "         ...,\n",
       "         [ 0.03529418, -0.0745098 , -0.18431371],\n",
       "         [ 0.32549024,  0.16078436,  0.02745104],\n",
       "         [ 0.27058828,  0.05882359, -0.06666666]],\n",
       "\n",
       "        [[-0.24705881, -0.23921567, -0.34117645],\n",
       "         [-0.29411763, -0.29411763, -0.41176468],\n",
       "         [-0.15294117, -0.17647058, -0.29411763],\n",
       "         ...,\n",
       "         [-0.14509803, -0.20784312, -0.2862745 ],\n",
       "         [ 0.21568632,  0.09019613, -0.05098039],\n",
       "         [ 0.37254906,  0.17647064,  0.00392163]]],\n",
       "\n",
       "\n",
       "       [[[ 0.11372554, -0.01176471, -0.10588235],\n",
       "         [ 0.0196079 , -0.09019607, -0.18431371],\n",
       "         [ 0.02745104, -0.05098039, -0.1607843 ],\n",
       "         ...,\n",
       "         [ 0.27058828,  0.10588241, -0.00392157],\n",
       "         [ 0.24705887,  0.082353  , -0.02745098],\n",
       "         [ 0.22352946,  0.05098045, -0.05882353]],\n",
       "\n",
       "        [[ 0.13725495, -0.00392157, -0.08235294],\n",
       "         [ 0.09019613, -0.03529412, -0.11372548],\n",
       "         [ 0.05882359, -0.04313725, -0.12941176],\n",
       "         ...,\n",
       "         [ 0.254902  ,  0.10588241, -0.00392157],\n",
       "         [ 0.21568632,  0.06666672, -0.04313725],\n",
       "         [ 0.15294123,  0.00392163, -0.10588235]],\n",
       "\n",
       "        [[ 0.16078436,  0.00392163, -0.0745098 ],\n",
       "         [ 0.1686275 ,  0.02745104, -0.05882353],\n",
       "         [ 0.02745104, -0.09019607, -0.18431371],\n",
       "         ...,\n",
       "         [ 0.20000005,  0.05098045, -0.05882353],\n",
       "         [ 0.20000005,  0.05098045, -0.05882353],\n",
       "         [ 0.22352946,  0.082353  , -0.02745098]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 0.15294123,  0.0196079 , -0.0745098 ],\n",
       "         [ 0.06666672, -0.05882353, -0.15294117],\n",
       "         [ 0.07450986, -0.05882353, -0.15294117],\n",
       "         ...,\n",
       "         [ 0.09019613, -0.01960784, -0.09019607],\n",
       "         [ 0.15294123,  0.0196079 , -0.05882353],\n",
       "         [ 0.23921573,  0.07450986, -0.02745098]],\n",
       "\n",
       "        [[ 0.20784318,  0.07450986, -0.03529412],\n",
       "         [ 0.16078436,  0.02745104, -0.08235294],\n",
       "         [ 0.18431377,  0.05098045, -0.05098039],\n",
       "         ...,\n",
       "         [-0.03529412, -0.14509803, -0.21568626],\n",
       "         [-0.05882353, -0.18431371, -0.26274508],\n",
       "         [ 0.20000005,  0.05098045, -0.05098039]],\n",
       "\n",
       "        [[ 0.20784318,  0.06666672, -0.05098039],\n",
       "         [ 0.21568632,  0.07450986, -0.03529412],\n",
       "         [ 0.1686275 ,  0.0196079 , -0.09019607],\n",
       "         ...,\n",
       "         [-0.02745098, -0.14509803, -0.21568626],\n",
       "         [ 0.06666672, -0.05882353, -0.1372549 ],\n",
       "         [ 0.11372554, -0.03529412, -0.12156862]]]], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cnn_res50_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://jmlb.github.io/ml/2017/03/20/CoeffDetermination_CustomMetric4Keras/\n",
    "from keras import backend as K\n",
    "\n",
    "def coeff_determination(y_true, y_pred):\n",
    "    SS_res =  K.sum(K.square( y_true-y_pred )) \n",
    "    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) ) \n",
    "    return ( 1 - SS_res/(SS_tot + K.epsilon()) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.environ['CUDA_LAUNCH_BLOCKING'] = \"0\"\n",
    "#os.environ['TORCH_HOME'] = '/workspace/cache'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n"
     ]
    }
   ],
   "source": [
    "tf.debugging.set_log_device_placement(True)\n",
    "gpus = tf.config.list_logical_devices('GPU')\n",
    "strategy = tf.distribute.MirroredStrategy(devices=[\"/gpu:0\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2940"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cnn_satellite = Sequential()\n",
    "cnn_satellite.add(Conv2D(32, kernel_size=(16, 16),activation='linear',input_shape=(128,128,3),padding='same'))\n",
    "cnn_satellite.add(LeakyReLU(alpha=0.1))\n",
    "cnn_satellite.add(MaxPooling2D((2, 2),padding='same'))\n",
    "cnn_satellite.add(Conv2D(64, (3, 3), activation='linear',padding='same'))\n",
    "cnn_satellite.add(LeakyReLU(alpha=0.1))\n",
    "cnn_satellite.add(MaxPooling2D(pool_size=(2, 2),padding='same'))\n",
    "cnn_satellite.add(Conv2D(128, (3, 3), activation='linear',padding='same'))\n",
    "cnn_satellite.add(LeakyReLU(alpha=0.1))                  \n",
    "cnn_satellite.add(MaxPooling2D(pool_size=(2, 2),padding='same'))\n",
    "cnn_satellite.add(Flatten())\n",
    "cnn_satellite.add(Dense(32, activation='linear'))\n",
    "cnn_satellite.add(LeakyReLU(alpha=0.1))                  \n",
    "cnn_satellite.add(Dense(1, activation='linear'))\n",
    "cnn_satellite.compile(loss=keras.losses.mean_squared_error, \n",
    "                  optimizer=keras.optimizers.Adam(1e-4),\n",
    "                  metrics=[coeff_determination])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_6 (Conv2D)           (None, 128, 128, 32)      24608     \n",
      "                                                                 \n",
      " leaky_re_lu_8 (LeakyReLU)   (None, 128, 128, 32)      0         \n",
      "                                                                 \n",
      " max_pooling2d_6 (MaxPooling  (None, 64, 64, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 64, 64, 64)        18496     \n",
      "                                                                 \n",
      " leaky_re_lu_9 (LeakyReLU)   (None, 64, 64, 64)        0         \n",
      "                                                                 \n",
      " max_pooling2d_7 (MaxPooling  (None, 32, 32, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 32, 32, 128)       73856     \n",
      "                                                                 \n",
      " leaky_re_lu_10 (LeakyReLU)  (None, 32, 32, 128)       0         \n",
      "                                                                 \n",
      " max_pooling2d_8 (MaxPooling  (None, 16, 16, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 32768)             0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 32)                1048608   \n",
      "                                                                 \n",
      " leaky_re_lu_11 (LeakyReLU)  (None, 32)                0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,165,601\n",
      "Trainable params: 1,165,601\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn_satellite.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyperparameters\n",
    "\n",
    "epochs = 50\n",
    "batchsize = 75\n",
    "\n",
    "callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "341/341 [==============================] - 9s 25ms/step - loss: 0.8606 - coeff_determination: 0.1912 - val_loss: 0.7216 - val_coeff_determination: 0.2885\n",
      "Epoch 2/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.6856 - coeff_determination: 0.3556 - val_loss: 0.5920 - val_coeff_determination: 0.4181\n",
      "Epoch 3/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.5899 - coeff_determination: 0.4429 - val_loss: 0.5379 - val_coeff_determination: 0.4717\n",
      "Epoch 4/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.5363 - coeff_determination: 0.4943 - val_loss: 0.5013 - val_coeff_determination: 0.5067\n",
      "Epoch 5/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.5061 - coeff_determination: 0.5223 - val_loss: 0.4724 - val_coeff_determination: 0.5345\n",
      "Epoch 6/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4859 - coeff_determination: 0.5409 - val_loss: 0.4541 - val_coeff_determination: 0.5525\n",
      "Epoch 7/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4770 - coeff_determination: 0.5488 - val_loss: 0.4583 - val_coeff_determination: 0.5476\n",
      "Epoch 8/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4652 - coeff_determination: 0.5603 - val_loss: 0.4615 - val_coeff_determination: 0.5462\n",
      "Epoch 9/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4586 - coeff_determination: 0.5661 - val_loss: 0.4710 - val_coeff_determination: 0.5345\n",
      "Epoch 10/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4555 - coeff_determination: 0.5687 - val_loss: 0.4665 - val_coeff_determination: 0.5383\n",
      "Epoch 11/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4488 - coeff_determination: 0.5756 - val_loss: 0.4710 - val_coeff_determination: 0.5349\n",
      "Epoch 12/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4498 - coeff_determination: 0.5755 - val_loss: 0.4321 - val_coeff_determination: 0.5752\n",
      "Epoch 13/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4447 - coeff_determination: 0.5804 - val_loss: 0.4272 - val_coeff_determination: 0.5810\n",
      "Epoch 14/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4479 - coeff_determination: 0.5772 - val_loss: 0.4309 - val_coeff_determination: 0.5759\n",
      "Epoch 15/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4398 - coeff_determination: 0.5849 - val_loss: 0.4408 - val_coeff_determination: 0.5669\n",
      "Epoch 16/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4383 - coeff_determination: 0.5860 - val_loss: 0.4451 - val_coeff_determination: 0.5627\n",
      "Epoch 17/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4390 - coeff_determination: 0.5855 - val_loss: 0.4387 - val_coeff_determination: 0.5700\n",
      "Epoch 18/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4398 - coeff_determination: 0.5846 - val_loss: 0.4418 - val_coeff_determination: 0.5655\n",
      "Epoch 19/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4376 - coeff_determination: 0.5852 - val_loss: 0.4245 - val_coeff_determination: 0.5829\n",
      "Epoch 20/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4341 - coeff_determination: 0.5900 - val_loss: 0.4332 - val_coeff_determination: 0.5742\n",
      "Epoch 21/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4342 - coeff_determination: 0.5892 - val_loss: 0.4229 - val_coeff_determination: 0.5846\n",
      "Epoch 22/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4347 - coeff_determination: 0.5889 - val_loss: 0.4330 - val_coeff_determination: 0.5742\n",
      "Epoch 23/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4326 - coeff_determination: 0.5925 - val_loss: 0.4252 - val_coeff_determination: 0.5816\n",
      "Epoch 24/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4307 - coeff_determination: 0.5935 - val_loss: 0.4333 - val_coeff_determination: 0.5745\n",
      "Epoch 25/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4319 - coeff_determination: 0.5925 - val_loss: 0.4364 - val_coeff_determination: 0.5701\n",
      "Epoch 26/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4321 - coeff_determination: 0.5919 - val_loss: 0.4320 - val_coeff_determination: 0.5760\n",
      "Epoch 27/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4326 - coeff_determination: 0.5911 - val_loss: 0.4188 - val_coeff_determination: 0.5887\n",
      "Epoch 28/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4303 - coeff_determination: 0.5930 - val_loss: 0.4222 - val_coeff_determination: 0.5852\n",
      "Epoch 29/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4310 - coeff_determination: 0.5933 - val_loss: 0.4314 - val_coeff_determination: 0.5768\n",
      "Epoch 30/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4299 - coeff_determination: 0.5941 - val_loss: 0.4243 - val_coeff_determination: 0.5836\n",
      "Epoch 31/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4297 - coeff_determination: 0.5940 - val_loss: 0.4274 - val_coeff_determination: 0.5792\n",
      "Epoch 32/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4302 - coeff_determination: 0.5946 - val_loss: 0.4244 - val_coeff_determination: 0.5838\n",
      "Epoch 33/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4293 - coeff_determination: 0.5933 - val_loss: 0.4249 - val_coeff_determination: 0.5824\n",
      "Epoch 34/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4273 - coeff_determination: 0.5955 - val_loss: 0.4255 - val_coeff_determination: 0.5828\n",
      "Epoch 35/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4291 - coeff_determination: 0.5940 - val_loss: 0.4226 - val_coeff_determination: 0.5846\n",
      "Epoch 36/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4276 - coeff_determination: 0.5958 - val_loss: 0.4260 - val_coeff_determination: 0.5816\n",
      "Epoch 37/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4280 - coeff_determination: 0.5957 - val_loss: 0.4256 - val_coeff_determination: 0.5816\n",
      "Epoch 38/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4274 - coeff_determination: 0.5951 - val_loss: 0.4312 - val_coeff_determination: 0.5769\n",
      "Epoch 39/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4272 - coeff_determination: 0.5972 - val_loss: 0.4200 - val_coeff_determination: 0.5874\n",
      "Epoch 40/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4256 - coeff_determination: 0.5968 - val_loss: 0.4194 - val_coeff_determination: 0.5877\n",
      "Epoch 41/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4273 - coeff_determination: 0.5963 - val_loss: 0.4194 - val_coeff_determination: 0.5881\n",
      "Epoch 42/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4257 - coeff_determination: 0.5987 - val_loss: 0.4170 - val_coeff_determination: 0.5901\n",
      "Epoch 43/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4265 - coeff_determination: 0.5970 - val_loss: 0.4238 - val_coeff_determination: 0.5844\n",
      "Epoch 44/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4274 - coeff_determination: 0.5966 - val_loss: 0.4260 - val_coeff_determination: 0.5803\n",
      "Epoch 45/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4263 - coeff_determination: 0.5963 - val_loss: 0.4268 - val_coeff_determination: 0.5794\n",
      "Epoch 46/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4265 - coeff_determination: 0.5969 - val_loss: 0.4193 - val_coeff_determination: 0.5879\n",
      "Epoch 47/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4259 - coeff_determination: 0.5975 - val_loss: 0.4243 - val_coeff_determination: 0.5825\n",
      "Epoch 48/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4252 - coeff_determination: 0.5972 - val_loss: 0.4275 - val_coeff_determination: 0.5789\n",
      "Epoch 49/50\n",
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4254 - coeff_determination: 0.5992 - val_loss: 0.4251 - val_coeff_determination: 0.5828\n",
      "Epoch 50/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "341/341 [==============================] - 8s 23ms/step - loss: 0.4249 - coeff_determination: 0.5983 - val_loss: 0.4224 - val_coeff_determination: 0.5848\n"
     ]
    }
   ],
   "source": [
    "#fit and record the history \n",
    "cnn_history = cnn_satellite.fit(X_cnn_res50, \n",
    "                  y_train1_cnn, \n",
    "                  batch_size=batchsize, \n",
    "                  epochs=epochs, \n",
    "                  verbose=1,\n",
    "                  validation_data=(X_cnn_res50_val, y_val_cnn)\n",
    "                                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 1s 6ms/step - loss: 0.4224 - coeff_determination: 0.5705\n"
     ]
    }
   ],
   "source": [
    "test_eval = cnn_satellite.evaluate(X_cnn_res50_val, y_val_cnn, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cnn(e, history_obj): \n",
    "    plt.plot([i for i in range(e)], history_obj.history['coeff_determination'], color = 'blue', label = \"Train\")\n",
    "    plt.scatter([i for i in range(e)], history_obj.history['coeff_determination'], color = 'blue', s = 0.5)\n",
    "    plt.plot([i for i in range(e)], history_obj.history['val_coeff_determination'], color = 'green', label = 'Validation')\n",
    "    plt.scatter([i for i in range(e)], history_obj.history['val_coeff_determination'], color = 'green', s = 0.5)\n",
    "    plt.grid()\n",
    "    plt.legend()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('$R^2$')\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot([i for i in range(e)], history_obj.history['loss'], color = 'blue', label = \"Train\")\n",
    "    plt.scatter([i for i in range(e)], history_obj.history['loss'], color = 'blue', s = 0.5)\n",
    "    plt.plot([i for i in range(e)], history_obj.history['val_loss'], color = 'green', label = 'Validation')\n",
    "    plt.scatter([i for i in range(e)], history_obj.history['val_loss'], color = 'green', s = 0.5)\n",
    "    plt.grid()\n",
    "    plt.legend()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cnn(epochs, cnn_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: cnn_fullData/assets\n"
     ]
    }
   ],
   "source": [
    "cnn_satellite.save('cnn_fullData')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mala_cnn = np.asarray(X_mala).reshape(len(X_mala), 128, 128, 4)\n",
    "\n",
    "X_mala_cnn = X_mala_cnn[:,:,:,:3].astype('float32') # Taking out the fourth channel as ghe information is the same everywhere\n",
    "\n",
    "X_mala_cnn_p = applications.resnet_v2.preprocess_input(np.copy(X_mala_cnn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_mala_cnn = cnn_satellite.predict(X_mala_cnn_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15691, 1)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_mala_cnn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_tag_predictions = pd.DataFrame({'lon': Lon_mala,'lat': Lat_mala,'cnn': y_pred_mala_cnn.flatten()})\n",
    "\n",
    "geo_tag_predictions.to_csv('predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johannes/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.kdeplot(y_pred_mala_cnn.flatten(), color = 'orange', label = \"Prediction\")\n",
    "sns.kdeplot(Y_mala, color = 'b', label = \"Truth\").legend()\n",
    "plt.xlabel(\"Wealth Index\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ResNet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import applications\n",
    "\n",
    "resnet = tf.keras.applications.ResNet50V2(\n",
    "    include_top=False,\n",
    "    weights=\"imagenet\",\n",
    "    input_tensor=None,\n",
    "    input_shape=None,\n",
    "    pooling=None\n",
    ")\n",
    "resnet.trainable = False\n",
    "#resnet.summary()\n",
    "\n",
    "gc.collect()\n",
    "\n",
    "\n",
    "inputs = keras.Input(shape=(128, 128, 3))\n",
    "\n",
    "# We make sure that the base_model is running in inference mode here,\n",
    "# by passing `training=False`. This is important for fine-tuning, as you will\n",
    "# learn in a few paragraphs.\n",
    "x = resnet(inputs, training=False)\n",
    "\n",
    "# Convert features of shape `base_model.output_shape[1:]` to vectors\n",
    "x = keras.layers.GlobalAveragePooling2D()(x)\n",
    "#x = keras.layers.Flatten()(x)\n",
    "x = keras.layers.Dense(32, activation='linear')(x)\n",
    "#x = keras.layers.Dropout(.2)(x)\n",
    "#x = keras.layers.LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "# A Dense classifier with a single unit (binary classification)\n",
    "outputs = keras.layers.Dense(1, activation = 'linear')(x)\n",
    "\n",
    "model = keras.Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "341/341 [==============================] - 14s 34ms/step - loss: 0.8965 - coeff_determination: 0.1575 - val_loss: 0.7014 - val_coeff_determination: 0.3069\n",
      "Epoch 2/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.6367 - coeff_determination: 0.3972 - val_loss: 0.5591 - val_coeff_determination: 0.4487\n",
      "Epoch 3/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.5853 - coeff_determination: 0.4481 - val_loss: 0.5318 - val_coeff_determination: 0.4752\n",
      "Epoch 4/50\n",
      "341/341 [==============================] - 10s 31ms/step - loss: 0.5399 - coeff_determination: 0.4908 - val_loss: 0.5206 - val_coeff_determination: 0.4870\n",
      "Epoch 5/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.5344 - coeff_determination: 0.4959 - val_loss: 0.5259 - val_coeff_determination: 0.4809\n",
      "Epoch 6/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.5293 - coeff_determination: 0.4984 - val_loss: 0.4949 - val_coeff_determination: 0.5126\n",
      "Epoch 7/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.5131 - coeff_determination: 0.5156 - val_loss: 0.4888 - val_coeff_determination: 0.5187\n",
      "Epoch 8/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.5135 - coeff_determination: 0.5162 - val_loss: 0.4955 - val_coeff_determination: 0.5132\n",
      "Epoch 9/50\n",
      "341/341 [==============================] - 10s 29ms/step - loss: 0.5066 - coeff_determination: 0.5218 - val_loss: 0.4858 - val_coeff_determination: 0.5249\n",
      "Epoch 10/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4949 - coeff_determination: 0.5326 - val_loss: 0.5116 - val_coeff_determination: 0.4945\n",
      "Epoch 11/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.5044 - coeff_determination: 0.5226 - val_loss: 0.5033 - val_coeff_determination: 0.5029\n",
      "Epoch 12/50\n",
      "341/341 [==============================] - 10s 31ms/step - loss: 0.4978 - coeff_determination: 0.5301 - val_loss: 0.4913 - val_coeff_determination: 0.5143\n",
      "Epoch 13/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4876 - coeff_determination: 0.5406 - val_loss: 0.4935 - val_coeff_determination: 0.5128\n",
      "Epoch 14/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4875 - coeff_determination: 0.5387 - val_loss: 0.4786 - val_coeff_determination: 0.5299\n",
      "Epoch 15/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4845 - coeff_determination: 0.5416 - val_loss: 0.5021 - val_coeff_determination: 0.5065\n",
      "Epoch 16/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4884 - coeff_determination: 0.5398 - val_loss: 0.4598 - val_coeff_determination: 0.5477\n",
      "Epoch 17/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4779 - coeff_determination: 0.5484 - val_loss: 0.4718 - val_coeff_determination: 0.5357\n",
      "Epoch 18/50\n",
      "341/341 [==============================] - 10s 31ms/step - loss: 0.4784 - coeff_determination: 0.5489 - val_loss: 0.4819 - val_coeff_determination: 0.5248\n",
      "Epoch 19/50\n",
      "341/341 [==============================] - 11s 31ms/step - loss: 0.4813 - coeff_determination: 0.5448 - val_loss: 0.4721 - val_coeff_determination: 0.5342\n",
      "Epoch 20/50\n",
      "341/341 [==============================] - 10s 31ms/step - loss: 0.4784 - coeff_determination: 0.5468 - val_loss: 0.4605 - val_coeff_determination: 0.5469\n",
      "Epoch 21/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4699 - coeff_determination: 0.5580 - val_loss: 0.4853 - val_coeff_determination: 0.5205\n",
      "Epoch 22/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4750 - coeff_determination: 0.5513 - val_loss: 0.4955 - val_coeff_determination: 0.5133\n",
      "Epoch 23/50\n",
      "341/341 [==============================] - 10s 31ms/step - loss: 0.4735 - coeff_determination: 0.5529 - val_loss: 0.4857 - val_coeff_determination: 0.5239\n",
      "Epoch 24/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4691 - coeff_determination: 0.5569 - val_loss: 0.4705 - val_coeff_determination: 0.5380\n",
      "Epoch 25/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4720 - coeff_determination: 0.5535 - val_loss: 0.4635 - val_coeff_determination: 0.5455\n",
      "Epoch 26/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4670 - coeff_determination: 0.5592 - val_loss: 0.4695 - val_coeff_determination: 0.5378\n",
      "Epoch 27/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4671 - coeff_determination: 0.5576 - val_loss: 0.4619 - val_coeff_determination: 0.5466\n",
      "Epoch 28/50\n",
      "341/341 [==============================] - 10s 29ms/step - loss: 0.4667 - coeff_determination: 0.5581 - val_loss: 0.4706 - val_coeff_determination: 0.5386\n",
      "Epoch 29/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4644 - coeff_determination: 0.5619 - val_loss: 0.4783 - val_coeff_determination: 0.5308\n",
      "Epoch 30/50\n",
      "341/341 [==============================] - 10s 29ms/step - loss: 0.4640 - coeff_determination: 0.5598 - val_loss: 0.4666 - val_coeff_determination: 0.5405\n",
      "Epoch 31/50\n",
      "341/341 [==============================] - 10s 31ms/step - loss: 0.4609 - coeff_determination: 0.5638 - val_loss: 0.4640 - val_coeff_determination: 0.5422\n",
      "Epoch 32/50\n",
      "341/341 [==============================] - 10s 31ms/step - loss: 0.4606 - coeff_determination: 0.5633 - val_loss: 0.4730 - val_coeff_determination: 0.5363\n",
      "Epoch 33/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4631 - coeff_determination: 0.5634 - val_loss: 0.4837 - val_coeff_determination: 0.5211\n",
      "Epoch 34/50\n",
      "341/341 [==============================] - 10s 31ms/step - loss: 0.4617 - coeff_determination: 0.5625 - val_loss: 0.4545 - val_coeff_determination: 0.5538\n",
      "Epoch 35/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4560 - coeff_determination: 0.5699 - val_loss: 0.4471 - val_coeff_determination: 0.5594\n",
      "Epoch 36/50\n",
      "341/341 [==============================] - 11s 31ms/step - loss: 0.4584 - coeff_determination: 0.5679 - val_loss: 0.4523 - val_coeff_determination: 0.5557\n",
      "Epoch 37/50\n",
      "341/341 [==============================] - 11s 31ms/step - loss: 0.4558 - coeff_determination: 0.5692 - val_loss: 0.4534 - val_coeff_determination: 0.5530\n",
      "Epoch 38/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4548 - coeff_determination: 0.5703 - val_loss: 0.4568 - val_coeff_determination: 0.5508\n",
      "Epoch 39/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4570 - coeff_determination: 0.5687 - val_loss: 0.4711 - val_coeff_determination: 0.5381\n",
      "Epoch 40/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4557 - coeff_determination: 0.5688 - val_loss: 0.4510 - val_coeff_determination: 0.5575\n",
      "Epoch 41/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4528 - coeff_determination: 0.5707 - val_loss: 0.4436 - val_coeff_determination: 0.5649\n",
      "Epoch 42/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4519 - coeff_determination: 0.5727 - val_loss: 0.4444 - val_coeff_determination: 0.5611\n",
      "Epoch 43/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4528 - coeff_determination: 0.5715 - val_loss: 0.4447 - val_coeff_determination: 0.5633\n",
      "Epoch 44/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4549 - coeff_determination: 0.5709 - val_loss: 0.4575 - val_coeff_determination: 0.5511\n",
      "Epoch 45/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4525 - coeff_determination: 0.5719 - val_loss: 0.4488 - val_coeff_determination: 0.5578\n",
      "Epoch 46/50\n",
      "341/341 [==============================] - 11s 31ms/step - loss: 0.4496 - coeff_determination: 0.5752 - val_loss: 0.4457 - val_coeff_determination: 0.5603\n",
      "Epoch 47/50\n",
      "341/341 [==============================] - 11s 31ms/step - loss: 0.4518 - coeff_determination: 0.5735 - val_loss: 0.4516 - val_coeff_determination: 0.5570\n",
      "Epoch 48/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4489 - coeff_determination: 0.5771 - val_loss: 0.4493 - val_coeff_determination: 0.5590\n",
      "Epoch 49/50\n",
      "341/341 [==============================] - 10s 30ms/step - loss: 0.4503 - coeff_determination: 0.5749 - val_loss: 0.4527 - val_coeff_determination: 0.5538\n",
      "Epoch 50/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "341/341 [==============================] - 10s 31ms/step - loss: 0.4465 - coeff_determination: 0.5784 - val_loss: 0.4610 - val_coeff_determination: 0.5449\n"
     ]
    }
   ],
   "source": [
    "model = keras.Model(inputs, outputs)\n",
    "model.compile(loss=keras.losses.mean_squared_error, \n",
    "            optimizer=keras.optimizers.Adam(),\n",
    "            metrics=[coeff_determination])\n",
    "\n",
    "ep = 50\n",
    "resnet_history = model.fit(X_cnn_res50, \n",
    "          y_train1_cnn, \n",
    "          epochs=ep,\n",
    "          batch_size = 75, \n",
    "          verbose = 1,\n",
    "          validation_data= (X_cnn_res50_val, y_val_cnn)\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: resNet_fullData/assets\n"
     ]
    }
   ],
   "source": [
    "model.save('resNet_fullData')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cnn(ep, resnet_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 128, 128, 3)]     0         \n",
      "                                                                 \n",
      " resnet50v2 (Functional)     (None, None, None, 2048)  23564800  \n",
      "                                                                 \n",
      " global_average_pooling2d (G  (None, 2048)             0         \n",
      " lobalAveragePooling2D)                                          \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 32)                65568     \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,630,401\n",
      "Trainable params: 23,584,961\n",
      "Non-trainable params: 45,440\n",
      "_________________________________________________________________\n",
      "Epoch 1/10\n",
      "341/341 [==============================] - 34s 89ms/step - loss: 0.5744 - coeff_determination: 0.4582 - val_loss: 0.4746 - val_coeff_determination: 0.5324\n",
      "Epoch 2/10\n",
      "341/341 [==============================] - 29s 85ms/step - loss: 0.4832 - coeff_determination: 0.5428 - val_loss: 0.4407 - val_coeff_determination: 0.5648\n",
      "Epoch 3/10\n",
      "341/341 [==============================] - 29s 85ms/step - loss: 0.4654 - coeff_determination: 0.5614 - val_loss: 0.4445 - val_coeff_determination: 0.5633\n",
      "Epoch 4/10\n",
      "341/341 [==============================] - 29s 84ms/step - loss: 0.4580 - coeff_determination: 0.5676 - val_loss: 0.4406 - val_coeff_determination: 0.5668\n",
      "Epoch 5/10\n",
      "341/341 [==============================] - 29s 85ms/step - loss: 0.4548 - coeff_determination: 0.5709 - val_loss: 0.4510 - val_coeff_determination: 0.5564\n",
      "Epoch 6/10\n",
      "341/341 [==============================] - 29s 85ms/step - loss: 0.4522 - coeff_determination: 0.5729 - val_loss: 0.4404 - val_coeff_determination: 0.5682\n",
      "Epoch 7/10\n",
      "341/341 [==============================] - 29s 85ms/step - loss: 0.4480 - coeff_determination: 0.5772 - val_loss: 0.4405 - val_coeff_determination: 0.5668\n",
      "Epoch 8/10\n",
      "341/341 [==============================] - 29s 84ms/step - loss: 0.4478 - coeff_determination: 0.5763 - val_loss: 0.4442 - val_coeff_determination: 0.5653\n",
      "Epoch 9/10\n",
      "341/341 [==============================] - 29s 85ms/step - loss: 0.4435 - coeff_determination: 0.5801 - val_loss: 0.4407 - val_coeff_determination: 0.5660\n",
      "Epoch 10/10\n",
      "341/341 [==============================] - 29s 84ms/step - loss: 0.4424 - coeff_determination: 0.5824 - val_loss: 0.4335 - val_coeff_determination: 0.5756\n"
     ]
    }
   ],
   "source": [
    "### Unfreeze and train with small stepsize \n",
    "\n",
    "resnet.trainable = True\n",
    "model.summary()\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(1e-5),  # Low learning rate\n",
    "    loss=keras.losses.mean_squared_error,            \n",
    "            metrics=[coeff_determination]\n",
    ")\n",
    "\n",
    "ep_unfrozen = 10\n",
    "resnet_unfrozen = model.fit(X_cnn_res50, \n",
    "          y_train1_cnn, \n",
    "          epochs=ep_unfrozen,\n",
    "          batch_size = 75, \n",
    "          verbose = 1,\n",
    "          validation_data= (X_cnn_res50_val, y_val_cnn)\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: resNet_unfroze_fullData/assets\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QGoD+/psToxIREVCvXj2Eh4cjKioKt2/fxujRowEAzZs3x549e5CQkIC0tDS88cYbGv1pKhMWFoZWrVph1KhRSE5OxsGDBzFz5kyNbVq2bInr169j8+bNuHTpEj7//HNs2bJFYxt3d3dcuXIFSUlJyMnJQVFRUZn3Gj58OMzMzDBq1CicOXMGf/31F9555x2MGDFCfflLTAxAOqBFC6BRI2Fi1JMnxa6GiIh0weuvv467d++iV69e6j47U6ZMQdu2bdGrVy9069YNjo6OGDRoUJX3KZVKsWXLFjx48ABBQUEYN24cFi5cqLHNgAED8N577yEiIgL+/v5ISEjArFmzNLZ56aWX0Lt3b3Tv3h2NGjUq91Z8CwsL7Nq1C3fu3EH79u0xZMgQPP/881i5cmX1vxg1QKKq6uAEBiQ3NxfW1ta4f/9+tTt9VUYul2PHjh3o27evxnXPF18EfvsN+PhjYNo0rb4lPUFF7UHiYHvoFn1uj4cPH+LKlSto2rQpzMzMxC5HK5RKJXJzc2FlZQWp1HDPXzypbavz99twv4I6hv2AiIiIao/oAWjVqlVwd3eHmZkZgoODcfz48Qq3Xb9+vXrUydKlvGSflpaGAQMGwNraGpaWlmjfvj2uX79ek4fxzDgxKhERUe0RNQDFxsYiMjISc+bMQWJiIvz8/NCrVy9kZWVV+BorKyvcvn1bvTzeM/3SpUvo1KkTPD09sW/fPpw+fRqzZs3S+VOg7doBpqZATo4wKCIRERHVHFEHQly6dCnGjx+PMWPGAABiYmKwfft2rFu3DtOnTy/3NRKJBI6OjhXuc+bMmejbty8++eQT9brmzZs/sY6ioiKNHuy5ubkAhOvf2p6PpHR/j+9XKgUCA41w+LAUBw6UoFkzngaqDRW1B4mD7aFb9Lk9SkpKoFKpoFAoamVMmdpQ2mVXpVLVmWN6GgqFAiqVCiUlJWW+N6vzvSpaACouLsbJkycRFRWlXieVShEWFoYjR45U+Lr8/Hy4ublBqVSiXbt2WLRoEXx8fAAIHcS2b9+OadOmoVevXjh16hSaNm2KqKioJ/aSX7x4MaKjo8us3717d43NoFve5HMODt4AWiI29ibs7ZNq5H2pfOW1B4mH7aFb9LE9JBIJnJyccOfOHdSvX1/scrQqLy9P7BJElZeXh4KCAuzdu7fMJLOFhYVV3o9od4HdunULLi4uSEhI0BiGe9q0adi/fz+OHTtW5jVHjhzBhQsX4Ovri/v372PJkiU4cOAAUlNT0bhxY2RkZMDJyQkWFhZYsGABunfvjp07d2LGjBn466+/0LVr13JrKe8MkKurK3JycmrkLrC4uDj06NGjzF0V27ZJMHiwMVq1UuHMmRKtvi+V70ntQbWP7aFb9L09MjMzkZubi0aNGsHCwqLaozHrGpVKhYKCAlhaVn9k6bpApVKhsLAQ2dnZsLKyKncsodzcXNjZ2VXpLjC9mgssJCREIyyFhobCy8sLa9aswfz589WnBAcOHIj33nsPANRjGMTExFQYgExNTWFqalpmvUwmq7Ef+vL23aWL8PH8eQnu35fBzq5G3prKUZNtTdXH9tAt+toeLi4uMDIyQk5OjtilaIVKpcKDBw9gbm5ukAGolI2NDRwdHcv9GlTn+1S0AGRnZwcjI6MyI1hmZmY+sY/Po2QyGdq2bYuLFy+q92lsbAxvb2+N7by8vHDo0CHtFF6DbG0BT0/g3DnhbrABA8SuiIhIf5VeBrO3t9fLfkyPk8vlOHDgALp06aKXgVQbZDKZxsSpz0K0AGRiYoKAgADEx8er++colUrEx8cjIiKiSvtQKBRISUlB37591fts37490tPTNbY7f/483NzctFp/TenYkQGIiEibjIyMtPZHU0xGRkYoKSmBmZmZwQYgbRL1ElhkZCRGjRqFwMBABAUFYfny5SgoKFDfFTZy5Ei4uLhg8eLFAIB58+ahQ4cOaNGiBe7du4dPP/0U165dw7hx49T7nDp1KsLDw9GlSxd1H6A//vgD+/btE+MQq61jR+DrrzkgIhERUU0SNQCFh4cjOzsbs2fPRkZGBvz9/bFz5051x6br169rDPd99+5djB8/HhkZGbCxsUFAQAASEhI0Lnm9+OKLiImJweLFizFp0iR4eHjgl19+QadOnWr9+J5G6YCIJ04ARUXC2EBERESkXaJ3go6IiKjwktfjZ22WLVuGZcuWVbrPsWPHYuzYsdoor9a1bClMjJqdDSQmAo/0+SYiIiItEX0qDNIkkQChocLnvAxGRERUMxiAdBAnRiUiIqpZDEA66NEAxIlRiYiItI8BSAcFBAidn7Ozgf8f4oiIiIi0iAFIB5maAoGBwue8DEZERKR9DEA6iv2AiIiIag4DkI5iACIiIqo5DEA6qvRW+LQ04M4dcWshIiKqaxiAdJSdHeDhIXyekCBuLURERHUNA5AO42UwIiKimsEApMMYgIiIiGoGA5AOe3Ri1OJicWshIiKqSxiAdFirVkJfoIcPhYlRiYiISDsYgHQYJ0YlIiKqGQxAOo79gIiIiLSPAUjHcWJUIiIi7WMA0nEBAYCJCZCVBVy6JHY1REREdQMDkI4zM+PEqERERNrGAKQH2A+IiIhIuxiA9AADEBERkXYxAOmB0lvhz57lxKhERETawACkBxo1EgZFBIAjR8SthYiIqC5gANITvAxGRESkPQxAeoIBiIiISHsYgPREaQA6fpwToxIRET0rBiA94eEB2NoKE6OeOiV2NURERPqNAUhPcGJUIiIi7WEA0iPsB0RERKQdDEB6hBOjEhERaQcDkB4JDBQmRs3MBC5fFrsaIiIi/cUApEfMzITZ4QFeBiMiInoWDEB6hv2AiIiInh0DkJ5hACIiInp2DEB6pvRW+NRU4O5dcWshIiLSVwxAesbeHmjZUvicE6MSERE9HQYgPcTLYERERM9GJwLQqlWr4O7uDjMzMwQHB+P48eMVbrt+/XpIJBKNxczMrMLt33zzTUgkEixfvrwGKhcHAxAREdGzET0AxcbGIjIyEnPmzEFiYiL8/PzQq1cvZGVlVfgaKysr3L59W71cu3at3O22bNmCo0ePwtnZuabKF8WjE6PK5eLWQkREpI9ED0BLly7F+PHjMWbMGHh7eyMmJgYWFhZYt25dha+RSCRwdHRULw4ODmW2uXnzJt555x1s3LgRMpmsJg+h1nl4AA0bAg8ecGJUIiKip2Es5psXFxfj5MmTiIqKUq+TSqUICwvDkSf08M3Pz4ebmxuUSiXatWuHRYsWwcfHR/28UqnEiBEjMHXqVI31FSkqKkJRUZH6cW5uLgBALpdDruVTLKX7e9b9duhghB07pDhwQIG2bZXaKM0gaas9SDvYHrqF7aFb2B6Vq87XRtQAlJOTA4VCUeYMjoODA86dO1fuazw8PLBu3Tr4+vri/v37WLJkCUJDQ5GamorGjRsDAD7++GMYGxtj0qRJVapj8eLFiI6OLrN+9+7dsLCwqOZRVU1cXNwzvd7WtiUAb/z6ayZatjyhnaIM2LO2B2kX20O3sD10C9ujYoWFhVXeVtQA9DRCQkIQEhKifhwaGgovLy+sWbMG8+fPx8mTJ/HZZ58hMTEREomkSvuMiopCZGSk+nFubi5cXV3Rs2dPWFlZabV+uVyOuLg49OjR45kuzVlZSfDdd8CVK07o06cvqnio9BhttQdpB9tDt7A9dAvbo3KlV3CqQtQAZGdnByMjI2RmZmqsz8zMhKOjY5X2IZPJ0LZtW1y8eBEAcPDgQWRlZaFJkybqbRQKBd5//30sX74cV69eLbMPU1NTmJqalrvvmvome9Z9d+gAyGRARoYE//wjQ7NmWizOANVkW1P1sT10C9tDt7A9Kladr4uonaBNTEwQEBCA+Ph49TqlUon4+HiNszxPolAokJKSAicnJwDAiBEjcPr0aSQlJakXZ2dnTJ06Fbt27aqR4xCDuTnQrp3wOW+HJyIiqh7RL4FFRkZi1KhRCAwMRFBQEJYvX46CggKMGTMGADBy5Ei4uLhg8eLFAIB58+ahQ4cOaNGiBe7du4dPP/0U165dw7hx4wAAtra2sLW11XgPmUwGR0dHeHh41O7B1bCOHYFjx4QANGKE2NUQERHpD9EDUHh4OLKzszF79mxkZGTA398fO3fuVHeMvn79OqTS/05U3b17F+PHj0dGRgZsbGwQEBCAhIQEeHt7i3UIounYEVi6lGeAiIiIqkv0AAQAERERiIiIKPe5ffv2aTxetmwZli1bVq39l9fvpy4oHRAxNRW4dw9o0EDMaoiIiPSH6AMh0tNzcACaNwdUKk6MSkREVB0MQHqO84IRERFVHwOQnmMAIiIiqj4GID1XGoCOHePEqERERFXFAKTnvLyEzs8PHgBJSWJXQ0REpB8YgPScVAqEhgqf8zIYERFR1TAA1QHsB0RERFQ9DEB1wKMBSKUStxYiIiJ9wABUB7RvDxgbA7dvA3V0zEciIiKtYgCqAywsODEqERFRdTAA1RHsB0RERFR1DEB1BAMQERFR1TEA1RGlAejMGWFiVCIiIqoYA1Ad4egINGsm3AV29KjY1RAREek2BqA6hJfBiIiIqoYBqA5hACIiIqoaBqA6hBOjEhERVQ0DUB3i7S1MjFpYCCQni10NERGR7mIAqkOkUiAkRPicl8GIiIgqxgBUx7AfEBERUeUYgOoYToxKRERUOQagOiYoSJgY9dYt4No1sashIiLSTQxAdYyFBdC2rfA5L4MRERGVjwGoDmI/ICIioidjAKqDGICIiIiejAGoDioNQCkpwP374tZCRESkixiA6iAnJ6BpU06MSkREVBEGoDqKl8GIiIgqxgBURzEAERERVYwBqI56dGLUkhJxayEiItI1DEB1lI8PYG0NFBRwYlQiIqLHMQDVUZwYlYiIqGIMQHUY+wERERGVjwGoDuPEqEREROVjAKrDgoIAIyPg5k3g+nWxqyEiItIdDEB1mKUlJ0YlIiIqDwNQHcd+QERERGXpRABatWoV3N3dYWZmhuDgYBw/frzCbdevXw+JRKKxmJmZqZ+Xy+X44IMP0KZNG1haWsLZ2RkjR47ErVu3auNQdA4DEBERUVmiB6DY2FhERkZizpw5SExMhJ+fH3r16oWsrKwKX2NlZYXbt2+rl2vXrqmfKywsRGJiImbNmoXExET8+uuvSE9Px4ABA2rjcHTOoxOj5uaKWwsREZGuMBa7gKVLl2L8+PEYM2YMACAmJgbbt2/HunXrMH369HJfI5FI4OjoWO5z1tbWiIuL01i3cuVKBAUF4fr162jSpIl2D0DHOTsD7u7A1avCxKg9e4pdERERkfhEDUDFxcU4efIkoqKi1OukUinCwsJw5MiRCl+Xn58PNzc3KJVKtGvXDosWLYKPj0+F29+/fx8SiQQNGjQo9/mioiIUFRWpH+f+/6kSuVwOuVxezaN6stL9aXu/TxISYoSrV6U4cECB7t2Vtfa++kCM9qCKsT10C9tDt7A9Kledr42oASgnJwcKhQIODg4a6x0cHHDu3LlyX+Ph4YF169bB19cX9+/fx5IlSxAaGorU1FQ0bty4zPYPHz7EBx98gFdeeQVWVlbl7nPx4sWIjo4us3737t2wsLB4iiOr3ONnqWqSlZU7AD/88ccdtG+fUGvvq09qsz2ocmwP3cL20C1sj4oVFhZWeVuJSiXeEHm3bt2Ci4sLEhISEFI6bwOAadOmYf/+/Th27Fil+5DL5fDy8sIrr7yC+fPnl3nupZdewj///IN9+/ZVGIDKOwPk6uqKnJycCl/ztORyOeLi4tCjRw/IZDKt7rsip08DgYEyWFqqkJ1dAmPRL3zqDjHagyrG9tAtbA/dwvaoXG5uLuzs7HD//v1K/36L+qfQzs4ORkZGyMzM1FifmZlZYR+fx8lkMrRt2xYXL17UWC+XyzF06FBcu3YNe/fufeIXwtTUFKampuXuu6a+yWpy34/z9wesrIDcXAnS0mRo165W3lav1GZ7UOXYHrqF7aFb2B4Vq87XRdS7wExMTBAQEID4+Hj1OqVSifj4eI0zQk+iUCiQkpICJycn9brS8HPhwgXs2bMHtra2Wq9dnxgZcWJUIiKiR4l+G3xkZCTWrl2Lb7/9FmlpaXjrrbdQUFCgvits5MiRGp2k582bh927d+Py5ctITEzEa6+9hmvXrmHcuHEAhPAzZMgQ/P3339i4cSMUCgUyMjKQkZGB4uJiUY5RF3A8ICIiov+I3hskPDwc2dnZmD17NjIyMuDv74+dO3eqO0Zfv34dUul/Oe3u3bsYP348MjIyYGNjg4CAACQkJMDb2xsAcPPmTWzduhUA4O/vr/Fef/31F7p161Yrx6VrGICIiIj+I3oAAoCIiAhERESU+9y+ffs0Hi9btgzLli2rcF/u7u4QsV+3zgoOFi6F/fOPMDGqgQ2HREREpEH0S2BUOywthc7QAM8CERERMQAZEF4GIyIiEjAAGRAGICIiIgEDkAEpDUCnTwN5eeLWQkREJCYGIAPi4gK4uQFKpTAxKhERkaFiADIwvAxGRETEAGRwGICIiIgYgAxOaQA6ehQoKRG3FiIiIrEwABmY1q2FiVHz84GUFLGrISIiEgcDkIExMgI6dBA+52UwIiIyVAxABqj0MlhCgrh1EBERiYUByACxIzQRERk6BiADVDox6vXrwuSoREREhoYByADVqwf4+Qmf8ywQEREZIgYgA8XLYEREZMgYgAwUAxARERkyBiADVRqAkpOFMYGIiIgMCQOQgWrcGGjSBFAogGPHxK6GiIiodjEAGTBeBiMiIkPFAGTAGICIiMhQPVUAunHjBv55ZACZ48ePY/Lkyfjyyy+1VhjVvNIAdOSIcCmMiIjIUDxVAHr11Vfx119/AQAyMjLQo0cPHD9+HDNnzsS8efO0WiDVnDZtgPr1gbw84MwZsashIiKqPU8VgM6cOYOgoCAAwI8//ojWrVsjISEBGzduxPr167VZH9UgToxKRESG6qkCkFwuh6mpKQBgz549GDBgAADA09MTt2/f1l51VOPYD4iIiAzRUwUgHx8fxMTE4ODBg4iLi0Pv3r0BALdu3YKtra1WC6SaxQBERESG6KkC0Mcff4w1a9agW7dueOWVV+D3/xNLbd26VX1pjPRDcDAglQLXrgE3b4pdDRERUe0wfpoXdevWDTk5OcjNzYWNjY16/YQJE2BhYaG14qjm1a8vTIx66pRwFmjoULErIiIiqnlPdQbowYMHKCoqUoefa9euYfny5UhPT4e9vb1WC6Sax8tgRERkaJ4qAA0cOBAbNmwAANy7dw/BwcH43//+h0GDBmH16tVaLZBqHgMQEREZmqcKQImJiejcuTMA4Oeff4aDgwOuXbuGDRs24PPPP9dqgVTzSgNQUhInRiUiIsPwVAGosLAQ9evXBwDs3r0bgwcPhlQqRYcOHXDt2jWtFkg1z9VVWBQK4PhxsashIiKqeU8VgFq0aIHffvsNN27cwK5du9CzZ08AQFZWFqysrLRaINUOXgYjIiJD8lQBaPbs2ZgyZQrc3d0RFBSEkJAQAMLZoLZt22q1QKodDEBERGRInuo2+CFDhqBTp064ffu2egwgAHj++efx4osvaq04qj2PT4xqZCRuPURERDXpqQIQADg6OsLR0VE9K3zjxo05CKIea9MGqFcPyM0FUlMBX1+xKyIiIqo5T3UJTKlUYt68ebC2toabmxvc3NzQoEEDzJ8/H0qlUts1Ui0wNubEqEREZDieKgDNnDkTK1euxEcffYRTp07h1KlTWLRoEVasWIFZs2Zpu0aqJewHREREhuKpAtC3336Lr776Cm+99RZ8fX3h6+uLt99+G2vXrsX69eurvb9Vq1bB3d0dZmZmCA4OxvEn3Iu9fv16SCQSjcXMzExjG5VKhdmzZ8PJyQnm5uYICwvDhQsXql2XoWEAIiIiQ/FUAejOnTvw9PQss97T0xN37typ1r5iY2MRGRmJOXPmIDExEX5+fujVqxeysrIqfI2VlRVu376tXh4fe+iTTz7B559/jpiYGBw7dgyWlpbo1asXHj58WK3aDE2HDsLEqFevArduiV0NERFRzXmqTtB+fn5YuXJlmVGfV65cCd9q9p5dunQpxo8fjzFjxgAAYmJisH37dqxbtw7Tp08v9zUSiQSOjo7lPqdSqbB8+XJ8+OGHGDhwIABgw4YNcHBwwG+//YZhw4aVeU1RURGKiorUj3NzcwEAcrkccrm8WsdTmdL9aXu/2mBmBrRpY4zkZAn27y/BkCEqsUuqcbrcHoaI7aFb2B66he1Ruep8bZ4qAH3yySfo168f9uzZox4D6MiRI7hx4wZ27NhR5f0UFxfj5MmTiIqKUq+TSqUICwvDkSNHKnxdfn4+3NzcoFQq0a5dOyxatAg+Pj4AgCtXriAjIwNhYWHq7a2trREcHIwjR46UG4AWL16M6OjoMut3795dY7Pbx8XF1ch+n5WLSxskJzfDpk3XYGFxRuxyao2utoehYnvoFraHbmF7VKywsLDK2z5VAOratSvOnz+PVatW4dy5cwCAwYMHY8KECViwYIF6nrDK5OTkQKFQwMHBQWO9g4ODer+P8/DwwLp16+Dr64v79+9jyZIlCA0NRWpqKho3boyMjAz1Ph7fZ+lzj4uKikJkZKT6cW5uLlxdXdGzZ0+tj2wtl8sRFxeHHj16QCaTaXXf2pCbK8GOHcDt203Rt28TscupcbreHoaG7aFb2B66he1RudIrOFXx1OMAOTs7Y+HChRrrkpOT8fXXX+PLL7982t1WKiQkRH3WCQBCQ0Ph5eWFNWvWYP78+U+1T1NTU5iampZZL5PJauybrCb3/Sy6dhU+JiVJUVwshaWluPXUFl1tD0PF9tAtbA/dwvaoWHW+Lk/VCVpb7OzsYGRkhMzMTI31mZmZFfbxeZxMJkPbtm1x8eJFAFC/7ln2aciaNAEaN+bEqEREVLeJGoBMTEwQEBCA+Ph49TqlUon4+HiNszxPolAokJKSAicnJwBA06ZN4ejoqLHP3NxcHDt2rMr7NHS8HZ6IiOo6UQMQAERGRmLt2rX49ttvkZaWhrfeegsFBQXqu8JGjhyp0Ul63rx52L17Ny5fvozExES89tpruHbtGsaNGwdAuENs8uTJWLBgAbZu3YqUlBSMHDkSzs7OGDRokBiHqHcYgIiIqK6rVh+gwYMHP/H5e/fuVbuA8PBwZGdnY/bs2cjIyIC/vz927typ7sR8/fp1SKX/5bS7d+9i/PjxyMjIgI2NDQICApCQkABvb2/1NtOmTUNBQQEmTJiAe/fuoVOnTti5c2eZAROpfI9OjKpUCmMDERER1SXVCkDW1taVPj9y5MhqFxEREYGIiIhyn9u3b5/G42XLlmHZsmVP3J9EIsG8efMwb968atdCwkSolpbA/fvCxKht2ohdERERkXZVKwB98803NVUH6ZDSiVHj44XLYAxARERU1/DiBpWL/YCIiKguYwCicjEAERFRXcYAROUqnRj1yhXg9m2xqyEiItIuBiAql5XVf31/eBaIiIjqGgYgqhAvgxERUV3FAEQVYgAiIqK6igGIKlQagE6dAgoLxa2FiIhImxiAqEJNmgAuLkBJCSdGJSKiuoUBiCokkfAyGBER1U0MQPREDEBERFQXMQDREz0+MSoREVFdwABET+TnJ0yMeu8ecPas2NUQERFpBwMQPZGxMRAcLHzOy2BERFRXMABRpdgPiIiI6hoGIKoUAxAREdU1DEBUqQ4dhFviL18GMjLEroaIiOjZMQBRpaytOTEqERHVLQxAVCW8DEZERHUJAxBVCQMQERHVJQxAVCWhocLHxEROjEpERPqPAYiqxN0dcHISJkY9cULsaoiIiJ4NAxBVCSdGJSKiuoQBiKqMAYiIiOoKBiCqstIAlJDAiVGJiEi/MQBRlfn7AxYWwsSoaWliV0NERPT0GICoymQyIChI+JyXwYiISJ8xAFG1sB8QERHVBQxAVC0MQEREVBcwAFG1hIQIt8RfugRkZopdDRER0dNhAKJqadAA8PERPudZICIi0lcMQFRtpZfBoqKAv/8WtxYiIqKnwQBE1RYWJnw8fx4YNw5QqcSth4iIqLoYgKjaBg8GwsOFz5OTgTFjgKIicWsiIiKqDgYgqjapFNi8GVi1CjAyAr79FujRA8jJEbsyIiKiqmEAoqf29tvA9u2AlRVw8CDQoQOQni52VURERJUTPQCtWrUK7u7uMDMzQ3BwMI4fP16l123evBkSiQSDBg3SWJ+fn4+IiAg0btwY5ubm8Pb2RkxMTA1UTgDQq5cwN5i7u3BrfIcOwN69YldFRET0ZKIGoNjYWERGRmLOnDlITEyEn58fevXqhaysrCe+7urVq5gyZQo6d+5c5rnIyEjs3LkT33//PdLS0jB58mRERERg69atNXUYBs/HBzh2TBgj6N49IRR99ZXYVREREVVM1AC0dOlSjB8/HmPGjFGfqbGwsMC6desqfI1CocDw4cMRHR2NZs2alXk+ISEBo0aNQrdu3eDu7o4JEybAz8+vymeW6OnY2wtnfl55BSgpAcaPB6ZOBRQKsSsjIiIqy1isNy4uLsbJkycRFRWlXieVShEWFoYjR45U+Lp58+bB3t4er7/+Og4ePFjm+dDQUGzduhVjx46Fs7Mz9u3bh/Pnz2PZsmUV7rOoqAhFj9zGlJubCwCQy+WQy+VPc3gVKt2ftverC4yMgPXrgebNpViwwAhLlgDp6Ups2KCApaXY1ZWvLreHPmJ76Ba2h25he1SuOl8b0QJQTk4OFAoFHBwcNNY7ODjg3Llz5b7m0KFD+Prrr5GUlFThflesWIEJEyagcePGMDY2hlQqxdq1a9GlS5cKX7N48WJER0eXWb97925YWFhU7YCqKS4urkb2qwsCA4H33nPBihVt8ccfRggIyMXMmcdga/tQ7NIqVJfbQx+xPXQL20O3sD0qVlhYWOVtRQtA1ZWXl4cRI0Zg7dq1sLOzq3C7FStW4OjRo9i6dSvc3Nxw4MABTJw4Ec7OzggrHcHvMVFRUYiMjFQ/zs3NhaurK3r27AkrKyutHodcLkdcXBx69OgBmUym1X3rkr59gUGDVBgyRIXLlxtg1qye2LKlBG3bil2ZJkNpD33B9tAtbA/dwvaoXOkVnKoQLQDZ2dnByMgImY/NqJmZmQlHR8cy21+6dAlXr15F//791euUSiUAwNjYGOnp6XB2dsaMGTOwZcsW9OvXDwDg6+uLpKQkLFmypMIAZGpqClNT0zLrZTJZjX2T1eS+dUWXLkLn6BdeAM6elaB7dxk2bQIGDhS7srIMoT30CdtDt7A9dAvbo2LV+bqI1gnaxMQEAQEBiI+PV69TKpWIj49HSEhIme09PT2RkpKCpKQk9TJgwAB0794dSUlJcHV1VffZkUo1D8vIyEgdlqh2NW0q3CbfsydQWAi8+CKwZAmnzyAiInGJegksMjISo0aNQmBgIIKCgrB8+XIUFBRgzJgxAICRI0fCxcUFixcvhpmZGVq3bq3x+gYNGgCAer2JiQm6du2KqVOnwtzcHG5ubti/fz82bNiApUuX1uqx0X+srYUBEydNAlavFu4OS08HvvgC4D8xREQkBlEDUHh4OLKzszF79mxkZGTA398fO3fuVHeMvn79epmzOZXZvHkzoqKiMHz4cNy5cwdubm5YuHAh3nzzzZo4BKoiY2Nh6gwPDyAyUhgn6PJl4OefARsbsasjIiJDI3on6IiICERERJT73L59+5742vXr15dZ5+joiG+++UYLlZG2SSTAu+8CLVoAw4YJ4waFhADbtgnriIiIaovoU2GQ4enXDzh8GHB1FS6FBQcDBw6IXRURERkSBiASha+vcIdY+/bAnTtAWBiwYYPYVRERkaFgACLRODkB+/YBQ4YAcjkwahQwcybAG/aIiKimMQCRqCwsgNhYYMYM4fGiRUB4uHDLPBERUU1hAKple6/sxQPFA7HL0ClSKbBwoTCPmEwm3BnWrRuQkSF2ZUREVFcxANWiaXHT0PuH3ojNiMWpjFNil6NzRo0C9uwBGjYETpwAgoKA06fFroqIiOoiBqBa1NWtKwBga/ZWLDiwQORqdFPp9BmtWgE3bgAdOwqDKBIREWkTA1At6teqH7q7d4cSSly5fwVKFXv7lqdFC+DoUeC554D8fGDAAOCzzzh9BhERaQ8DUC1b138dzKXmOJN9BmtPrhW7HJ1lYwPs3AmMGyfcFTZ5MjBxIlBSInZlRERUFzAA1TKX+i4Y7jQcADA9fjoy8zNFrkh3yWTAl18Cn34qjCK9erUwiOL9+2JXRkRE+o4BSAR97PqgrWNb3Ht4D+/vfl/scnSaRAJMmQL8+qtwy/zu3UBoKHDlitiVERGRPmMAEoGRxAhf9PkCUokUG1M2Ys/lPWKXpPMGDQIOHgScnYGzZ4XpMxISxK6KiIj0FQOQSAKcAjCx/UQAwNvb38bDkociV6T72rUDjh8H2rYFsrOFTtKbNoldFRER6SMGIBHN7z4fTvWccOHOBXx06COxy9ELLi7CmaBBg4CiImD4cGDuXN4hRkRE1cMAJCJrM2t81vszAMDiQ4uRnpMuckX6wdIS+OUXYOpU4XF0tBCEHvIkGhERVREDkMiGeA9BnxZ9UKwoxts73oaKpzKqRCoFPvkEWLsWMDYGfvhBuCSWlSV2ZUREpA8YgEQmkUiwsu9KmBmbYe+VvdiYslHskvTKuHHArl1AgwbAkSNC5+jUVLGrIiIiXccApAOa2TTD7C6zAQCRuyJx58EdkSvSL889J4Sf5s2Bq1eF2+R37RK7KiIi0mUMQDri/dD34d3IG9mF2Zi+Z7rY5egdT09h+ozOnYHcXGHAxC++ELsqIiLSVQxAOsLEyAQx/WIAAGsT1+Lw9cMiV6R/7OyAuDhg5EhAoRCmzpg8WficiIjoUQxAOqSzW2eM9R8LAHhz+5uQK+QiV6R/TE2B9euBhQuFx599BgwcCOTliVoWERHpGAYgHfNJj09gZ2GHM1lnsOzoMrHL0UsSCTBjBvDjj4CZGbB9O9CpE3D9utiVERGRrmAA0jG2FrZY0mMJAGDuvrm4eu+quAXpsZdfBvbvBxwcgNOngaAgYSRpIiIiBiAdNNJvJLq6dcWDkgeI2BHBsYGeQWnoadMGyMwEunYFfv5Z7KqIiEhsDEA6SCKRIOaFGMikMmy/sB1bzm0RuyS91qQJcPiwcGfYw4fCmaFFizh9BhGRIWMA0lGedp74oOMHAIBJf05CXhF78T6L+vWB338H3n1XeDxzJjBmjDCfGBERGR4GIB02o/MMNLdpjpt5NzHrr1lil6P3jIyA5cuF8YGMjIBvvwX69DHCvXsmYpdGRES1jAFIh5nLzPFFP2E0vxXHVyDxdqLIFdUNb70l3BlmZQUcOiTFuHE90bOnEX7+GXjwQOzqiIioNjAA6biezXtiWOthUKqUeGPbG1AoOaqfNvTqBSQkAA0bqlBSYoR9+6R4+WXhjrHRo4Hdu4GSErGrJCKimsIApAeW9VoGa1Nr/H3rb6z+e7XY5dQZPj7An3+WYPnyvRg5UoEmTYQBE7/9VghILi7ApEnCFBvsME1EVLcwAOkBx3qOWPT8IgDAjPgZuJV3S+SK6o62bQF39zx89ZUSV64Ahw4Bb78tTKuRlQWsWAGEhAgTrX74IXD2rNgVExGRNjAA6Yk3At5AkEsQ8orz8N6u98Qup06SSoGOHYFVq4Bbt4AdO4DXXgMsLYErV4TpNXx8AH9/4JNPOLI0EZE+YwDSE0ZSI6x5YQ2kEil+TP0ROy/uFLukOk0mA/r0Ab77ThhA8YcfgP79AWNjIDkZ+OADwM0N6NIFiIkBcnLErpiIiKqDAUiP+Dv6491gYSCbt7e/jUJ5ocgVGQZLS2DYMGDrViAjA1izRhhRGgAOHhTuKnNyEgLSDz8ABQXi1ktERJVjANIz87rPQ2Orxrhy7woWHlgodjkGx9YWmDAB2LdPuAT26adCP6KSEmDbNuDVVwF7e2D4cOFWe7lc7IqJiKg8DEB6pp5JPazoswIA8GnCpzibzV65YnF1BaZMARIThc7Rs2YJnaULC4FNm4AXXhDODL31lnCmSKkUu2IiIirFAKSHBnkOwgCPAZAr5Xhz25tQqviXVWxeXsC8ecCFC8CxY8KUGw4OwL//Cn2EunQB3N2FvkPJybytnohIbKIHoFWrVsHd3R1mZmYIDg7G8ePHq/S6zZs3QyKRYNCgQWWeS0tLw4ABA2BtbQ1LS0u0b98e1+vYLTuf9/4cFjILHLx+EN8mfSt2OfT/JBJhBvrly4F//gHi4oQ5x6ysgBs3hLvH/P2B1q2Fu8ouXxa7YiIiwyRqAIqNjUVkZCTmzJmDxMRE+Pn5oVevXsjKynri665evYopU6agc+fOZZ67dOkSOnXqBE9PT+zbtw+nT5/GrFmzYGZmVlOHIQq3Bm6I7hYNAJgaNxU5hbwNSdcYGwNhYcC6dcKdZD//DAweDJiaCpfMPvxQuGQWEiKMN5SZKXbFRESGQ9QAtHTpUowfPx5jxoyBt7c3YmJiYGFhgXXr1lX4GoVCgeHDhyM6OhrNmjUr8/zMmTPRt29ffPLJJ2jbti2aN2+OAQMGwN7eviYPRRTvBr8LXwdf/PvgX0yNmyp2OfQEZmbASy8Bv/wi3Em2bp0QjqRSYaTpSZMAZ2dhBOpvvwVyc8WumIiobjMW642Li4tx8uRJREVFqddJpVKEhYXhyJEjFb5u3rx5sLe3x+uvv46DBw9qPKdUKrF9+3ZMmzYNvXr1wqlTp9C0aVNERUWVe6msVFFREYqKitSPc///r49cLodcy7fxlO5PW/td2Wslum7oivVJ6/Gaz2vo4tZFK/s1FNpuj6qwtBQGWHztNeD2beDnn6XYvFmCEyek2L1bmIfszTdV6NtXhWHDlOjTRwVT01orT1RitAdVjO2hW9gelavO10aiUonTHfPWrVtwcXFBQkICQkJC1OunTZuG/fv349ixY2Vec+jQIQwbNgxJSUmws7PD6NGjce/ePfz2228AgIyMDDg5OcHCwgILFixA9+7dsXPnTsyYMQN//fUXupYO3vKYuXPnIjo6usz6TZs2wcLCQjsHXINW31iNXf/uQmPTxljmsQwyqUzskugp3L5tiQMHXHDgQGPcvFlfvd7CQo7Q0Fvo0uUf+PjkwMhIxCKJiHRYYWEhXn31Vdy/fx9WVlZP3Fa0M0DVlZeXhxEjRmDt2rWws7Mrdxvl/99nPHDgQLz3njBdhL+/PxISEhATE1NhAIqKikJkZKT6cW5uLlxdXdGzZ89Kv4DVJZfLERcXhx49ekAm005QCXkQgjZr2uCfwn9w1uYsojpGVf4iAlAz7fEsXn9duEMsKUmO2FgpYmOluHlThj173LBnjxucnFQYOlSJYcNUaNdOBYlE7Iq1S9faw9CxPXQL26NyudXoPyBaALKzs4ORkREyH+v5mZmZCUdHxzLbX7p0CVevXkX//v3V60oDj7GxMdLT0+Hq6gpjY2N4e3trvNbLywuHDh2qsBZTU1OYlnONQSaT1dg3mTb3bS+zx7LeyzD81+FYfHgxhvsOR/OGzbWyb0NRk239NIKChOXTT4UxhDZtAn76Cbh9W4LPPjPCZ58BLVsKAy++8grg4SF2xdqla+1h6NgeuoXtUbHqfF1E6wRtYmKCgIAAxMfHq9cplUrEx8drXBIr5enpiZSUFCQlJamXAQMGoHv37khKSoKrqytMTEzQvn17pKena7z2/PnzcHNzq/FjEtMrrV9BWLMwPCx5iLd3vA2RrmySlkmlwrQba9YI/YV+/x0IDwfMzYUxh6KjAU9PwNtbmKh17lyhU/Xdu2JXTkSk20S9BBYZGYlRo0YhMDAQQUFBWL58OQoKCjBmzBgAwMiRI+Hi4oLFixfDzMwMrVu31nh9gwYNAEBj/dSpUxEeHo4uXbqo+wD98ccf2LdvX20dligkEgm+6PsF2qxug92XduPH1B8R3jpc7LJIi0xNgQEDhCUvTwhDmzYJnabT0oRtoqOFBQAaNRLODD2+NGsmTPZKRGTIRA1A4eHhyM7OxuzZs5GRkQF/f3/s3LkTDg4OAIDr169DKq3eSaoXX3wRMTExWLx4MSZNmgQPDw/88ssv6NSpU00cgk5padsSMzrPwJx9czB512T0atELDcwaiF0W1YD69f+7kyw7G1i2TLhE1qABcOuWsGRnC8vjV3+NjYUQVF44atQIda5fERFReUS7C0yX5ebmwtraukq9yKtLLpdjx44d6Nu3b41cwy0qKYJvjC/O/3seE9tPxMq+K7X+HnVJTbeHWPLygPPngfR0zeX8eWGusoo0aFB+MGrRQhjLqKbV1fbQV2wP3cL2qFx1/n7rzV1gVDWmxqaI6ReD5zY8hy9OfIGRfiMR5BIkdllUy+rXBwIChOVRSiVw82bZYJSeLsxuf++eMJfZ46NQSKWAm1v54cjZmWeNiEj/MADVQd2bdscI3xH47vR3eGPbGzgx/gSMpWxqEoKMq6uwhIVpPvfggdCxurxwlJsLXLkiLDt3ar6uXj2gVauywahVK2HQRyIiXcS/inXUkp5LsO38NiRlJGHFsRV4L+Q9sUsiHWduDvj6CsujVCphnrLygtGVK0B+PpCYKCyPa9y4/LNGTZoIYYyISCwMQHWUvaU9Pg77GBO2TcCsv2ZhiPcQuFq7il0W6SGJBHB0FJbHxxItLgYuXSrbzyg9HcjJAf75R1geGe0CgNCfqGXL8u9QIyKqDQxAddjr7V7Ht8nf4vCNw3h357v4NfxXsUuiOsbEBPDyEpbH3blT/lmjixeBhw+BlBRh0SSDlVVvuLgYwdtb6Hfk7q750dq65o+LiOo+BqA6TCqRYnW/1Wj3ZTtsObcFf6T/gf4e/St/IZEWNGwIhIQIy6NKSoBr18oPR7dvA7m5psjN/W9so8c1aCAEofLCkbu78L7slE1ElWEAquPaOLRBZIdIfJLwCSL+jMBzTZ+DpQl7ppJ4jI2B5s2FpW9fzef27pXj9OlDKCnpDBMTY1y7JoSlq1eFjzk5wp1q9+4Bycnl79/SsvxwVPq5gwMDEhExABmE2V1nIzY1FtfuX0P0/mh80uMTsUsiKlfnzkBeXi769lWVO1p1fr5wu35pIHo0HF29CmRkAAUFwNmzwlIeU9OyZ5Ae/dzZGTAyqrFDJCIdwQBkACxNLLGq7yq88MMLWHpkKV7zfQ2+Dr6Vv5BIx9SrJ8x79th8x2oPHwoBqbxwdO2aMAZSUZHQUfv8+fL3YWwsDBNQ0SW2xo05lQhRXcAAZCD6teqHwV6D8Wvar3hj2xs4PPYwpBLeh0x1i5mZMP5Qq1blPy+XC3elVXQG6cYNoY9S6ZhH5ZFKhbNE5YUjNzfhFv/aGDWbiJ4NA5AB+az3Z9h9aTeO/nMUXyV+hQkBE8QuiahWyWRA06bCUh6FQphH7dFg9PjnRUX/3d7/+DxrpWxthaAUFgb06yeMyN2qFcc+ItIlDEAGpLFVYyzovgCTd03GB3s+wECPgXCo5yB2WUQ6w8jov5Gyy5s/WakEsrIqPoN07ZrQB+nff4Xtf/hBWABhepK2bYHAQCEQBQYKc6wxFBGJgwHIwEwMmohvk7/FqYxTeH/3+/h+8Pdil0SkN6TS/waF7NCh7PMqlRB+/vwT+OIL4ZLYjRvAqVPCBLUHDghLKSsroF07zVDUvDnvUiOqDQxABsZYaow1L6xB8FfB2JiyEWP8x+D5Zs+LXRYZOJVKhW3nt2He/nkoyS/B9ZPXEREcAYmeJQGJBLCzA0aMEJZSJSXAuXPA338Ly8mTQFKSMMfavn3CUsra+r+JbEuDUbNmDEVE2sYAZIDau7THxPYTsfLESry1/S2cfus0zIzZa5Nqn1KlxO/nfse8A/OQlJGkXj9p1yTEno1FdLdoPNf0Ob0LQo8zNgZatxaW0aOFdXK5MNhjaSD6+29hbKP794G9e4WllI1N2VDk7s5QRPQsGIAM1ILnFuCXtF9w4c4FfHToI8ztNlfsksiAKFVK/Jr2K+YfmI/TmacBAPVM6mGwx2DcuX0Hu+/uxuEbhxH2XRg6N+mMud3mort7d70PQo+Syf6bfHbsWGGdXA6kpmqGotOngbt3gT17hKVUw4b/BaLSUNSkCUMRUVUxABkoazNrLO+9HOE/h2PxocV4pfUr8LDzELssquMUSgV+Pvsz5h+Yj9TsVABAfZP6mBQ8Ce91eA9WMivs2LEDK19Zif8d+x++PPklDl4/iOc3PI8ubl0Q3S0a3dy7iXsQNUgmA/z9hWXcOGFdcTFw5sx/gejkSSEU3bkDxMUJSyk7O82zRIGBwrhFDEVEZTEAGbCXvV/GNy2+wc6LO/H2jrexZ8SeOvUfNukOhVKB2NRYLDiwAGk5wiRf1qbWeDf4XUzuMBk25jYAALlcDgBwru+Mz/t8jg86foCPDn2ELxO/xIFrB9D92+7o5t4N0d2i0cWti2jHU5tMTISO0u3aAePHC+uKioRQ9GifopQUYaqQXbuEpVSjRpqBKCAAcHFhKCJiADJgEokEq/qugs8XPth7ZS82pmzEa76viV0W1SElyhJsStmEhQcX4vy/wtDLNmY2eK/De3gn+B00MGvwxNe7WLlgRd8V+KDTB1h8cDG+OvUV9l3dh67ru+K5ps9hbte56OzWuRaORLeYmv7XJ+iNN4R1Dx8KIejRy2dnzgDZ2cJdaX/++d/rHRzKhiJnZ3GOhUgsDEAGrplNM8zqMgsz985E5K5I9G3ZFw3NG4pdFuk5uUKO709/j4UHF+LS3UsAgIbmDfF+yPuICIqAlalVtfbX2KoxVvVbhemdpmPRwUX4+tTX2HtlL/Ze2Yvnmz6P6G7R6NikY00cit4wMwPatxeWUg8eCJfLHg1FZ88CmZnA9u3CUsrJqWyfIlvb2j8OotrCAESYEjoFG1M24mz2WUTticKa/mvELon0VLGiGBuSN2DRwUW4ck+YS8LOwg5TQqbg7fZvo75p/Wfav6u1K1a/sBrTO03H4kOLse7UOsRfiUf8lXj0aNYDc7vNRahrqDYOpU4wNweCg4WlVGGhcLdZaSD6+2/hbrTbt4Ft24SlVKNGxnB0DMVHHxmhYUNhoEh9XerV46U/0sQARDAxMkFMvxh0Wd8FXyZ+iVH+o/hHhKqlqKQI65PWY/Ghxbh2/xoAwN7SHlNDp+KtwLdgaWKp1fdza+CGmBdiENUpCgsPLsQ3Sd8g7nIc4i7HoWfznojuFo0OjcsZqZBgYQGEhAhLqYICYVyiRztap6UB2dkSZGc3Eq1WbbO2FsLgo5f+eOec4WIAIgBAZ7fOGOs/FuuS1uGNbW8gcUIiZEac8pqe7GHJQ6w7tQ4fHfoIN3JvAAAc6zliWug0vBH4BixkFjX6/m4N3PBl/y8R1SkKiw4uwvrk9dh9aTd2X9qN3i16Y27XuQhuHFz5jgycpSXQsaOwlMrPBzZuLEF6ejKsrPzg5mYMhQJ6ueTmCnfT3b8P7N4tLKVK75x7NBS5ujIUGQIGIFL7uMfH+D39d5zJOoNlR5dhWsdpYpdEOuqB/AG+SvwKHx3+CLfybgEQ7tz6oOMHGN9uPMxl5rVaT1Obplg7YC2iOkdh4YGF+Db5W+y8uBM7L+5EnxZ9MLfbXAS5BNVqTfquXj1g7FgVduz4B337+kKmx/8P/f03sGQJ0LevcAnw5Mkn3zlnZ1e2kziHE6h7GIBIzc7CDkt6LsGY38dg7r65GOozFO4N3MUui3RIobwQa/5eg08SPkFGfgYAoYPy9I7T8Xq710UfUbyZTTN8PfBrzOg8AwsOLsB3yd/hz4t/4s+Lf6Jfy36Y220uAp0DRa2Ral9gILB5c9n1j985d/KkcOdcTg6wc6ewlHp0OIHSYMQ+RfqNAYg0jPIbhfVJ67H/2n688+c72DpsK8cGIhQUF2D136vxacKnyCrIAgA0sW6CGZ1mYLT/aJgam4pcoabmDZvjm4HfYGbnmVhwYAG+O/0dtl/Yju0XtqN/q/6Y03UOApwDxC6TRFbenXMPH2reOVcaisobTsDevmwocnZmKNIXDECkQSKRYHW/1fCL8cO289uw5dwWDPYaLHZZJJK8ojx8ceILLDmyBDmFOQAA9wbumNl5Jkb6jYSJkYnIFT5Zi4YtsH7QeszsPBPzD8zHxpSN+OP8H/jj/B8Y4DEAc7vORVuntmKXSTrEzAwIChKWUqXDCTzaSTw1FcjKAnbsEJZSHGNJfzAAURlejbwwreM0LDy4EJP+nIQezXo88+3LpF9yi3Kx8vhK/O/I/3DnwR0AQHOb5pjZeSZe831N7zrIt7RtiQ0vblAHoR/O/ICt6VuxNX0rBnkOwpyuc+Dv6C92maSjyhtO4MEDzeEESkNReWMsOTqWDUVOTrV/HKSJAYjKNbPzTPxw5gdcvnsZs/+ajWW9l4ldktZl5mfi9O3TuFx4GUUlRZDpcy9PLbn38B5WHFuBZUeX4e7DuwCAlg1b4sMuH+LVNq/CWKrfvzI87Dzw/eDv8WGXD4UglPIDfjv3G3479xte9HwRc7vNha+Dr9hlkh4wNwc6dBCWUo+PsXTypDDwZEZG2TGWnJzKXj5zdKz94zBkEpVKpRK7CF2Tm5sLa2tr3L9/H1ZW1RuxtjJyuRw7duxA3759df4P7q6Lu9B7Y29IJVKcGH8C7ZzaiV3SU3lY8hBp2Wk4nXlaWLKEj6V9WQDASGIE70be8HXwhZ+DH/wc/eDr4AvHeobxG+nug7tYfnQ5Pjv2Ge4X3QcAeNp5YlaXWQj3CYeR1KhW6qjtn4+07DTMOzAPsWdioYLwq/Alr5cwp+sctHFoU+Pvr+v06ffV4+48uIOUzBSkZKUg/ko8/r71N55r+hzCmobBx94HXnZetXK3YkFB2VCUlgYolWW3dXYuG4ocHP57Xp/bo7ZU5+83A1A5GID+M+znYYhNjUV75/Y48vqRWvtD+DRUKhVu5N7A6czTSMlMUQed9Jx0KFSKMttLIEE9k3pQKpQoUBSUu097S3v4Ofipg5Gvgy+8GnnpfN+Xqvq38F8sO7oMnx/7HHnFeQAAn0Y+mNVlFoZ4D6n19hbr5yM1KxXzD8zHj6k/qoPQEO8hmNN1Dlrbt661OnSNPvy+KlYUIz0nXfi5z0pR/6NzM+/mE18nlUjRzKYZWtu3hk8jH/g08kFr+9ZoZduqxjv1VzTwZHl/jV1c/gtDfn4lyMmJx8svP4d69WTsbF0OBqBnxAD0n9t5t+G5ylPoE9JnJSYGTRS7JABAfnE+zmSd+e+szv8vpWcvHtfQvCF8HXzha+8rfHTwhY+9D85knMGtk7dg5GEEiZEEyRnJOJ11GskZybhw5wKUqrL/psmkMng18vrvbNH/ByOHeg7lvLNuyi7IxtIjS7HyxErkF+cDANrYt8HsrrMx2GswpBKpKHWJ/fNxJusM5u2fh5/O/gRACMkv+7yMOV3nwLuRd63XIzax2+NRKpUKN/NuavyDk5KZgrScNJQoS8p9jZu1G3wdfNHIohGSM5Ph3sAd2YXZOJN1Rt237XFGEiO0tG1ZJhi1aNiiRvu+5eeXDUXnzpUfigDA2BiwsgLq1xc+Pvr54x8re86kbvw/B4AB6JkxAGn64sQXmLhjIqxMrZA2MQ3O9WvvlgalSolLdy6V+e+udILNxxlLjeFl56UOOaWLUz2ncm/nf1J7FMoLkZqViuTMZJzOPI3kzGQkZyRXGLIcLB2ES2f2vvBzFIKRh52HTp0tyszPxP+O/A9fnPgCBXLhrJe/oz9md5mNgZ4DRQs+pXTl5yMlMwXR+6PxS9ovAIQgFN46HLO7zIZXIy/R6qptYrVHXlEezmSdUf/Ml3689/BeudtbmVrB18EXbezbqD+2tm8NazPrcrdXqVTILMhEalYqUrNTcSbrjPpjblFuua+RSWXwtPOEj/1/ocinkQ+a2TSrsTOleXmaoWjbNhVycwGVSrunfkxMni44lfec2H/WGICeEQOQJoVSgZCvQ3Di1gkM9RmK2CGxNfI+pdfsH+2rcybrDArlheVu71TPqUzQ8bTzrFbgqG57lF5mS85I1ghGF/69oL508iiZVFZu3yJ7S/sq16gNGfkZ+OTwJ4j5OwYPSh4AAAKcAjC762z0b9VfZ8Z60rWfj+SMZMw7MA+/pv0KQAhCw1oPw+yus+Fp5ylydTWvpttDoVTg4p2LZf7BKZ1I93FGEiN42nmijUMb+Nr7Ch8dfOFq5aqV7+HSs0ypWZqh6Gz2WfU/DI8zMzaDl50XfOx90LpRa3VAcmvgpvV/KI4fl+Off3agQYO+aNVKhtxcISTl5kLj84o+Pr6usPxfrc/EzKzqwSkoSHNOOm1gAHpGDEBlnbp9CoFrA6FUKfHn8D/Ru0Xvp96XXCFH+r/pZfrq/JP7T7nbmxmbobV9a/XlqzYObdDGvg0aWT77JI3aao+C4gKkZqeWCUYV/TfpWM9Ro2+Rn6MfPGw9tH6K/VbeLXx86GN8mfglHpY8BAAEuQRhTtc56NOij84En1K6+vORlJGE6P3R+O3cbwCE/iOvtH4Fs7rMgoedh7jF1SBttkdWQVaZy1ep2anq78vHOdd31jijU/oPjhiDbipVSly/f10IRY+cNUrLSauwfkuZJbwbeWsEo9b2reFS3+Wpf+60/fNRUiJceqssQFXluYflfxmeaORI4Ntvn/kwNFTn77d+39NKtaatU1u8G/wulh1dhok7JuLMW2cqvYOi9DTz4/100nLSUKwoLvc17g3cy/TVadGwhU53vgYASxNLBLkEacw3pVKpcP3+dfWls9K+RRfvXERGfgYy8jOw69J/ExCZGJnAu5F3mU7XTxP0/sn9Bx8d+ghfJX6FIkURACCkcQjmdJ2Dns176lzw0XX+jv7YEr4Fp26fwtz9c7E1fSs2pmzED2d+wKttXsWsLrPQyraV2GXqhAfyBzibfbbM5atH77p8lIXMAq3tW6tDTmngsbWwreXKKyaVSOHewB3uDdzxQqsX1OsVSgUu372M1OxU4axRthCQzuWcQ4G8ACduncCJWyc09mVlaqVxCa00GDlYOtT6z6WxMdCggbA8K7m86sHpt9+Ay5eBS+X3ZKg1DEBUZdHdovHT2Z9w+e5lLDiwAAufX6h+rvSX3uOnsrMLs8vdV32T+hqXriq7Zq+PJBIJ3Bq4wa2BGwZ4DFCvLyguwJmsMxrB6HTmaeQW5SIpIwlJGUka+3Gq51Smb1Er21blni26fv86Fh9cjHVJ69Qhs1OTTpjTdQ6eb/o8g88zauvUFr8P+x0nb51E9P5o/HH+D3x/+ntsStmE13xfw4edP0RL25Zil1krlColrt27pvEzn5KVgvP/ni/35gEJJGjRsEWZy1fNbJqJ3vfsaRlJhQ7TLW1bYpDnIPV6uUKOi3cuavQvSs1Kxfl/zyO3KBdH/jmCI/8c0dhXQ/OGZTpe+9j7wM7CrpaP6unIZEDDhsJSmWHDgGXLgPfeq/m6nkQnLoGtWrUKn376KTIyMuDn54cVK1YgKKjymZs3b96MV155BQMHDsRvv/1W7jZvvvkm1qxZg2XLlmHy5MlVqoeXwCq2JW0LBv84GDKpDB90/ADn75zH6czTFf7Sk0qkaNmwZZm+Om7Wbjrxx1hX2kOlUuHa/WtlLqFdunOp3L5FJkYm8Gnkow5Gnnae2HJuC9YnrYdcKQcAdHPvhtldZqObezed+FpXha60R1X9fetvzN03F9svCMP+GkmM8Jrva5jVZRaaN2wucnXPrrQ9QrqH4Nzdc+o+eilZwvg6pXcQPs7W3FbjnxtfB194N/KGpYllLR+BbikqKcL5f8+X6Xhd0c85IAzFURqMPBt6Iv9SPia9NAlmpuJOPKyr9KoPUGxsLEaOHImYmBgEBwdj+fLl+Omnn5Ceng57+4o7il69ehWdOnVCs2bN0LBhw3ID0JYtWxAdHY3s7GxMnTqVAUgLVCoVBm4eiD/O/1HmOVtzW/g5+mmcyvZu5A0LmYUIlVaNrrdH6e3+jwaj05mn1WP2lOf5ps9jdtfZ6OLWpRYr1Q5db4+KHL95HNH7o7HjgjAplJHECHYWdmjRsAWsTIXfISqoUPrrtvSP3ZMe19S2pY+rsm1hcSH+zfsX/8r/Lfe4Sy/bPt5Xx7Geo96Ebl3wQP4A53LOaYSi1OxUXL13tdztbc1t8WqbVzGs9TB0aNxBb8+g1QS9CkDBwcFo3749Vq5cCQBQKpVwdXXFO++8g+nTp5f7GoVCgS5dumDs2LE4ePAg7t27VyYA3bx5E8HBwdi1axf69euHyZMnVxiAioqKUFRUpH6cm5sLV1dX5OTk1EgAiouLQ48ePfTqF/yjbubdRJdvu+B23m34OvhiXrd5aGPfBo6W+vdLTx/bQ6lS4tr9a+o75VKyUrD36l4UFBegR9Me2Dpsq9glPjV9bI9HHb95HPMPzseuy7sq31jPuFm7waeRj3C5upHQZ6dlw5Z6Ny+cPskvzkdaThrOZp/F2Zyz+DH1R9x7cE9j4FZXK1cM8RqCod5D0c6xnd79Dta23Nxc2NnZ6X4AKi4uhoWFBX7++WcMGjRIvX7UqFG4d+8efv/993JfN2fOHJw+fRpbtmzB6NGjywQgpVKJsLAwDBw4EO+++y7c3d2fGIDmzp2L6OjoMus3bdoECwvdPXtBRLrpYuFFXH1wFRL898eo9A/To+tKH6vXSaCxjcZzT9jPo9tXe9tH3rO8/ZgbmaOxaWPUM65X2WFTLZAr5UjOS8bBewdx/P5xPFA+UD/naOKITjad0KlBJ7iZ6UY3g9pWWFiIV199VffvAsvJyYFCoYCDg+YIug4ODjh37ly5rzl06BC+/vprJCUlVbjfjz/+GMbGxpg0aVKV6oiKikJkZKT6cekZoJ49e/IMUB3H9tAtbA/dwvbQLaXtETUkCrNls/FA/gC7Lu/Cj2d/xPYL25FRnIGfM3/Gz5k/w9PWEy97v4yXvV42iDGrSuXmlj/0SHn06i6wvLw8jBgxAmvXroWdXfk940+ePInPPvsMiYmJVU6/pqamMDUtO7aETCarsR/6mtw3VR/bQ7ewPXQL20O3lLaHTCbDy61fxsutX0Z+cT62nd+G2NRY7LiwA+f+PYf5B+dj/sH58HPwQ7hPOMJbh6OZTTOxy69R1fk+FTUA2dnZwcjICJmZmRrrMzMz4ehYdhbuS5cu4erVq+jfv796nfL/p9Q1NjZGeno6Dh48iKysLDRp0kS9jUKhwPvvv4/ly5fj6tWrNXMwREREIqlnUg/DWg/DsNbDcP/hffye/js2n9mMuMtxwpAbmcmYsXcG2ju3x7DWwzDUZygaWzUWu2xRiRqATExMEBAQgPj4eHUfIKVSifj4eERERJTZ3tPTEykpKRrrPvzwQ+Tl5eGzzz6Dq6srRowYgbCwMI1tevXqhREjRmDMmDE1dixERES6wNrMGiP9RmKk30j8W/gvfk37FbGpsfjr6l/qwRnf3/0+OjXphHCfcAzxHgLHemVPOtR1ol8Ci4yMxKhRoxAYGIigoCAsX74cBQUF6rAycuRIuLi4YPHixTAzM0Pr1q01Xt/g/4ewLF1va2sLW1vNEURlMhkcHR3h4VF3h6wnIiJ6nK2FLcYHjMf4gPHIzM/Ez2d/RmxqLA5eP4hD1w/h0PVDeHfnu+jm3g3hPuF4yeslnRqFuyaJHoDCw8ORnZ2N2bNnIyMjA/7+/ti5c6e6Y/T169chlXKMAyIiomfhUM8BE4MmYmLQRPyT+w9+Sv0Jm1M34/jN49h7ZS/2XtmLiTsmIqxZGIb5DMMgz0F1anT+x4kegAAgIiKi3EteALBv374nvnb9+vWV7p/9foiIiP7T2Kox3gt5D++FvIcrd6/gx9QfsTl1M5IykrDz4k7svLgTJttM0KdFH4T7hKO/R3/UM6lbQyHw1AoREZEBa2rTFB90+gCn3jiFcxPPIbpbNLzsvFCsKMbv6b/j1V9fhf2n9hj601D8cvYXPJA/qHyneoABiIiIiAAAHnYemN11NlLfTsXpN09jZueZaG7THA9KHuCnsz9hyE9DYL/EHq/9+hq2nd+mnnRZH+nEJTAiIiLSHRKJBG0c2qCNQxvM7z4fibcTEZsai9jUWFy/fx0bUzZiY8pGNDBrgMGegxHeOhzPNX0OxlL9iRU8A0REREQVkkgkCHAOwCc9PsGVd68gYWwCJgVNglM9J9x7eA/rktah1/e94PQ/J7y17S3su7oPCqVC7LIrxQBEREREVSKVSBHiGoLP+nyGG+/dwF+j/sKbAW/CzsIOOYU5iDkZg+7fdofrMle8++e7OHLjCESec71CDEBERERUbUZSI3Rz74bVL6zG7fdvY9druzDWfywamDXA7fzb+Pz45whdFwr3z9wxLW4aTt46qVNhiAGIiIiInomx1Bg9m/fE1wO/RuaUTPzxyh8Y3mY46pnUw/X71/FpwqcIXBuIVitb4cO9HyIlM0X0MMQARERERFpjYmSCF1q9gO8Hf4+sKVn4ZegveNn7ZZgbm+PinYtYeHAhfGN88WLsi6LWyQBERERENcJcZo7BXoPx48s/ImtqFjYN3qSehDUjP0PU2vTnfjUiIiLSW/VM6uGVNq+gpW1LfHz4Y0wKmiRqPQxAREREVGsCnQPx08s/iV0GL4ERERGR4WEAIiIiIoPDAEREREQGhwGIiIiIDA4DEBERERkcBiAiIiIyOAxAREREZHAYgIiIiMjgMAARERGRwWEAIiIiIoPDAEREREQGhwGIiIiIDA4DEBERERkczgZfDpVKBQDIzc3V+r7lcjkKCwuRm5sLmUym9f1T9bA9dAvbQ7ewPXQL26NypX+3S/+OPwkDUDny8vIAAK6uriJXQkRERNWVl5cHa2vrJ24jUVUlJhkYpVKJW7duoX79+pBIJFrdd25uLlxdXXHjxg1YWVlpdd9UfWwP3cL20C1sD93C9qicSqVCXl4enJ2dIZU+uZcPzwCVQyqVonHjxjX6HlZWVvwG1iFsD93C9tAtbA/dwvZ4ssrO/JRiJ2giIiIyOAxAREREZHAYgGqZqakp5syZA1NTU7FLIbA9dA3bQ7ewPXQL20O72AmaiIiIDA7PABEREZHBYQAiIiIig8MARERERAaHAYiIiIgMDgNQLVq1ahXc3d1hZmaG4OBgHD9+XOySDNLixYvRvn171K9fH/b29hg0aBDS09PFLov+30cffQSJRILJkyeLXYpBu3nzJl577TXY2trC3Nwcbdq0wd9//y12WQZJoVBg1qxZaNq0KczNzdG8eXPMnz+/SvNdUcUYgGpJbGwsIiMjMWfOHCQmJsLPzw+9evVCVlaW2KUZnP3792PixIk4evQo4uLiIJfL0bNnTxQUFIhdmsE7ceIE1qxZA19fX7FLMWh3795Fx44dIZPJ8Oeff+Ls2bP43//+BxsbG7FLM0gff/wxVq9ejZUrVyItLQ0ff/wxPvnkE6xYsULs0vQab4OvJcHBwWjfvj1WrlwJQJhvzNXVFe+88w6mT58ucnWGLTs7G/b29ti/fz+6dOkidjkGKz8/H+3atcMXX3yBBQsWwN/fH8uXLxe7LIM0ffp0HD58GAcPHhS7FALwwgsvwMHBAV9//bV63UsvvQRzc3N8//33Ilam33gGqBYUFxfj5MmTCAsLU6+TSqUICwvDkSNHRKyMAOD+/fsAgIYNG4pciWGbOHEi+vXrp/FzQuLYunUrAgMD8fLLL8Pe3h5t27bF2rVrxS7LYIWGhiI+Ph7nz58HACQnJ+PQoUPo06ePyJXpN06GWgtycnKgUCjg4OCgsd7BwQHnzp0TqSoChDNxkydPRseOHdG6dWuxyzFYmzdvRmJiIk6cOCF2KQTg8uXLWL16NSIjIzFjxgycOHECkyZNgomJCUaNGiV2eQZn+vTpyM3NhaenJ4yMjKBQKLBw4UIMHz5c7NL0GgMQGbSJEyfizJkzOHTokNilGKwbN27g3XffRVxcHMzMzMQuhyD8YxAYGIhFixYBANq2bYszZ84gJiaGAUgEP/74IzZu3IhNmzbBx8cHSUlJmDx5Mpydndkez4ABqBbY2dnByMgImZmZGuszMzPh6OgoUlUUERGBbdu24cCBA2jcuLHY5RiskydPIisrC+3atVOvUygUOHDgAFauXImioiIYGRmJWKHhcXJygre3t8Y6Ly8v/PLLLyJVZNimTp2K6dOnY9iwYQCANm3a4Nq1a1i8eDED0DNgH6BaYGJigoCAAMTHx6vXKZVKxMfHIyQkRMTKDJNKpUJERAS2bNmCvXv3omnTpmKXZNCef/55pKSkICkpSb0EBgZi+PDhSEpKYvgRQceOHcsMDXH+/Hm4ubmJVJFhKywshFSq+efayMgISqVSpIrqBp4BqiWRkZEYNWoUAgMDERQUhOXLl6OgoABjxowRuzSDM3HiRGzatAm///476tevj4yMDACAtbU1zM3NRa7O8NSvX79M/ytLS0vY2tqyX5ZI3nvvPYSGhmLRokUYOnQojh8/ji+//BJffvml2KUZpP79+2PhwoVo0qQJfHx8cOrUKSxduhRjx44VuzS9xtvga9HKlSvx6aefIiMjA/7+/vj8888RHBwsdlkGRyKRlLv+m2++wejRo2u3GCpXt27deBu8yLZt24aoqChcuHABTZs2RWRkJMaPHy92WQYpLy8Ps2bNwpYtW5CVlQVnZ2e88sormD17NkxMTMQuT28xABEREZHBYR8gIiIiMjgMQERERGRwGICIiIjI4DAAERERkcFhACIiIiKDwwBEREREBocBiIiIiAwOAxAREREZHAYgIqIqkEgk+O2338Qug4i0hAGIiHTe6NGjIZFIyiy9e/cWuzQi0lOcDJWI9ELv3r3xzTffaKwzNTUVqRoi0nc8A0REesHU1BSOjo4ai42NDQDh8tTq1avRp08fmJubo1mzZvj55581Xp+SkoLnnnsO5ubmsLW1xYQJE5Cfn6+xzbp16+Dj4wNTU1M4OTkhIiJC4/mcnBy8+OKLsLCwQMuWLbF169aaPWgiqjEMQERUJ8yaNQsvvfQSkpOTMXz4cAwbNgxpaWkAgIKCAvTq1Qs2NjY4ceIEfvrpJ+zZs0cj4KxevRoTJ07EhAkTkJKSgq1bt6JFixYa7xEdHY2hQ4fi9OnT6Nu3L4YPH447d+7U6nESkZaoiIh03KhRo1RGRkYqS0tLjWXhwoUqlUqlAqB68803NV4THByseuutt1QqlUr15ZdfqmxsbFT5+fnq57dv366SSqWqjIwMlUqlUjk7O6tmzpxZYQ0AVB9++KH6cX5+vgqA6s8//9TacRJR7WEfICLSC927d8fq1as11jVs2FD9eUhIiMZzISEhSEpKAgCkpaXBz88PlpaW6uc7duwIpVKJ9PR0SCQS3Lp1C88///wTa/D19VV/bmlpCSsrK2RlZT3tIRGRiBiAiEgvWFpalrkkpS3m5uZV2k4mk2k8lkgkUCqVNVESEdUw9gEiojrh6NGjZR57eXkBALy8vJCcnIyCggL184cPH4ZUKoWHhwfq168Pd3d3xMfH12rNRCQengEiIr1QVFSEjIwMjXXGxsaws7MDAPz0008IDAxEp06dsHHjRhw/fhxff/01AGD48OGYM2cORo0ahblz5yI7OxvvvPMORowYAQcHBwDA3Llz8eabb8Le3h59+vRBXl4eDh8+jHfeead2D5SIagUDEBHphZ07d8LJyUljnYeHB86dOwdAuENr8+bNePvtt+Hk5IQffvgB3t7eAAALCwvs2rUL7777Ltq3bw8LCwu89NJLWLp0qXpfo0aNwsOHD7Fs2TJMmTIFdnZ2GDJkSO0dIBHVKolKpVKJXQQR0bOQSCTYsmULBg0aJHYpRKQn2AeIiIiIDA4DEBERERkc9gEiIr3HK/lEVF08A0REREQGhwGIiIiIDA4DEBERERkcBiAiIiIyOAxAREREZHAYgIiIiMjgMAARERGRwWEAIiIiIoPzfydD5+IxpldMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.save('resNet_unfroze_fullData')\n",
    "\n",
    "plot_cnn(ep_unfrozen, resnet_unfrozen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loss')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "plt.figure()\n",
    "plt.plot([i for i in range(ep)], resnet_history.history['coeff_determination'], color = 'blue', label = \"Train\")\n",
    "#plt.scatter([i for i in range(ep)], resnet_history.history['coeff_determination'], color = 'blue', s = 0.5)\n",
    "plt.plot([i for i in range(ep)], resnet_history.history['val_coeff_determination'], color = 'green', label = 'Validation')\n",
    "#plt.scatter([i for i in range(ep)], resnet_history.history['val_coeff_determination'], color = 'green', s = 0.5)\n",
    "plt.plot([i for i in range(ep,ep+ep_unfrozen)], resnet_unfrozen.history['coeff_determination'], color = 'lightblue', label = \"Train - Unfrozen\")\n",
    "#plt.scatter([i for i in range(ep,ep+ep_unfrozen)], resnet_unfrozen.history['coeff_determination'], color = 'lightblue', s = 0.5)\n",
    "plt.plot([i for i in range(ep,ep+ep_unfrozen)], resnet_unfrozen.history['val_coeff_determination'], color = 'lightgreen', label = 'Validation  - Unfrozen')\n",
    "#plt.scatter([i for i in range(ep,ep+ep_unfrozen)], resnet_unfrozen.history['val_coeff_determination'], color = 'lightgreen', s = 0.5)\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('$R^2$')\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot([i for i in range(ep)], resnet_history.history['loss'], color = 'blue', label = \"Train\")\n",
    "plt.scatter([i for i in range(ep)], resnet_history.history['loss'], color = 'blue', s = 0.5)\n",
    "plt.plot([i for i in range(ep)], resnet_history.history['val_loss'], color = 'green', label = 'Validation')\n",
    "plt.scatter([i for i in range(ep)], resnet_history.history['val_loss'], color = 'green', s = 0.5)\n",
    "plt.plot([i for i in range(ep,ep+ep_unfrozen)], resnet_unfrozen.history['loss'], color = 'lightblue', label = \"Train - Unfrozen\")\n",
    "plt.scatter([i for i in range(ep,ep+ep_unfrozen)], resnet_unfrozen.history['loss'], color = 'lightblue', s = 0.5)\n",
    "plt.plot([i for i in range(ep,ep+ep_unfrozen)], resnet_unfrozen.history['val_loss'], color = 'lightgreen', label = 'Validation  - Unfrozen')\n",
    "plt.scatter([i for i in range(ep,ep+ep_unfrozen)], resnet_unfrozen.history['val_loss'], color = 'lightgreen', s = 0.5)\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_cnn = np.asarray(X_test).reshape(len(X_test), 128, 128, 4)\n",
    "\n",
    "X_test_cnn = X_test_cnn[:,:,:,:3].astype('float32') # Taking out the fourth channel as ghe information is the same everywhere\n",
    "y_train_cnn = np.asarray(y_train).astype('float32')\n",
    "\n",
    "X_test_p = applications.resnet_v2.preprocess_input(np.copy(X_test_cnn))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 1s 7ms/step - loss: 0.4224 - coeff_determination: 0.5705\n",
      "89/89 [==============================] - 2s 24ms/step - loss: 0.4335 - coeff_determination: 0.5637\n"
     ]
    }
   ],
   "source": [
    "test_eval_cnn = cnn_satellite.evaluate(X_cnn_res50_val, y_val_cnn, verbose=1)\n",
    "\n",
    "test_eval_resnet = model.evaluate(X_cnn_res50_val, y_val_cnn, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr = SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyper_C_list = [1, 10,100,1000, 10000]\n",
    "hyper_ep_list = [i/10 for i in range(1,11,2)]\n",
    "hyper_kernel_list = ['poly', 'rbf', 'sigmoid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 75 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=-1)]: Done 176 tasks      | elapsed: 14.3min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed: 25.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, estimator=SVR(), n_jobs=-1,\n",
       "             param_grid={'C': [1, 10, 100, 1000, 10000],\n",
       "                         'epsilon': [0.1, 0.3, 0.5, 0.7, 0.9],\n",
       "                         'kernel': ['poly', 'rbf', 'sigmoid']},\n",
       "             scoring='r2', verbose=1)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_svr = GridSearchCV(SVR(),\n",
    "                           {\n",
    "                              'C':hyper_C_list,\n",
    "                              'epsilon': hyper_ep_list,\n",
    "                              'kernel':hyper_kernel_list,\n",
    "                            },cv=4, scoring=\"r2\",verbose=1,n_jobs=-1\n",
    "                           )\n",
    "grid_search_svr.fit(X_train_pca,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "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></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_epsilon</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59.417859</td>\n",
       "      <td>0.256741</td>\n",
       "      <td>10.087827</td>\n",
       "      <td>0.052256</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>poly</td>\n",
       "      <td>{'C': 1, 'epsilon': 0.1, 'kernel': 'poly'}</td>\n",
       "      <td>3.593572e-01</td>\n",
       "      <td>3.755697e-01</td>\n",
       "      <td>3.606406e-01</td>\n",
       "      <td>3.441844e-01</td>\n",
       "      <td>3.599380e-01</td>\n",
       "      <td>1.110583e-02</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62.711058</td>\n",
       "      <td>0.082521</td>\n",
       "      <td>12.257909</td>\n",
       "      <td>0.056567</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1, 'epsilon': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>4.354827e-01</td>\n",
       "      <td>4.528930e-01</td>\n",
       "      <td>4.367523e-01</td>\n",
       "      <td>4.050677e-01</td>\n",
       "      <td>4.325489e-01</td>\n",
       "      <td>1.728709e-02</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.158629</td>\n",
       "      <td>3.385826</td>\n",
       "      <td>13.336206</td>\n",
       "      <td>0.068773</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>sigmoid</td>\n",
       "      <td>{'C': 1, 'epsilon': 0.1, 'kernel': 'sigmoid'}</td>\n",
       "      <td>-5.002699e+03</td>\n",
       "      <td>-5.218150e+03</td>\n",
       "      <td>-5.249682e+03</td>\n",
       "      <td>-5.962994e+03</td>\n",
       "      <td>-5.358381e+03</td>\n",
       "      <td>3.617828e+02</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44.300005</td>\n",
       "      <td>0.134319</td>\n",
       "      <td>7.758465</td>\n",
       "      <td>0.116692</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>poly</td>\n",
       "      <td>{'C': 1, 'epsilon': 0.3, 'kernel': 'poly'}</td>\n",
       "      <td>3.603206e-01</td>\n",
       "      <td>3.744777e-01</td>\n",
       "      <td>3.643373e-01</td>\n",
       "      <td>3.452746e-01</td>\n",
       "      <td>3.611026e-01</td>\n",
       "      <td>1.049397e-02</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.410237</td>\n",
       "      <td>0.204040</td>\n",
       "      <td>9.714529</td>\n",
       "      <td>0.071134</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1, 'epsilon': 0.3, 'kernel': 'rbf'}</td>\n",
       "      <td>4.422160e-01</td>\n",
       "      <td>4.557420e-01</td>\n",
       "      <td>4.409005e-01</td>\n",
       "      <td>4.142245e-01</td>\n",
       "      <td>4.382708e-01</td>\n",
       "      <td>1.504947e-02</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>39.473873</td>\n",
       "      <td>1.200229</td>\n",
       "      <td>2.126850</td>\n",
       "      <td>0.013703</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 10000, 'epsilon': 0.7, 'kernel': 'rbf'}</td>\n",
       "      <td>5.856945e-01</td>\n",
       "      <td>5.909359e-01</td>\n",
       "      <td>5.880254e-01</td>\n",
       "      <td>5.727268e-01</td>\n",
       "      <td>5.843456e-01</td>\n",
       "      <td>6.960408e-03</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>55.241236</td>\n",
       "      <td>3.149767</td>\n",
       "      <td>9.121775</td>\n",
       "      <td>1.543446</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>sigmoid</td>\n",
       "      <td>{'C': 10000, 'epsilon': 0.7, 'kernel': 'sigmoid'}</td>\n",
       "      <td>-5.650226e+11</td>\n",
       "      <td>-5.866412e+11</td>\n",
       "      <td>-5.117707e+11</td>\n",
       "      <td>-6.179702e+11</td>\n",
       "      <td>-5.703512e+11</td>\n",
       "      <td>3.870727e+10</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>23.925573</td>\n",
       "      <td>1.590841</td>\n",
       "      <td>1.003019</td>\n",
       "      <td>0.017861</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.9</td>\n",
       "      <td>poly</td>\n",
       "      <td>{'C': 10000, 'epsilon': 0.9, 'kernel': 'poly'}</td>\n",
       "      <td>5.445189e-01</td>\n",
       "      <td>5.412445e-01</td>\n",
       "      <td>5.426073e-01</td>\n",
       "      <td>5.307069e-01</td>\n",
       "      <td>5.397694e-01</td>\n",
       "      <td>5.359926e-03</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>25.721082</td>\n",
       "      <td>0.810011</td>\n",
       "      <td>1.233614</td>\n",
       "      <td>0.111335</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.9</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 10000, 'epsilon': 0.9, 'kernel': 'rbf'}</td>\n",
       "      <td>5.435591e-01</td>\n",
       "      <td>5.444258e-01</td>\n",
       "      <td>5.442095e-01</td>\n",
       "      <td>5.323241e-01</td>\n",
       "      <td>5.411296e-01</td>\n",
       "      <td>5.093847e-03</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>36.888744</td>\n",
       "      <td>1.355030</td>\n",
       "      <td>4.681876</td>\n",
       "      <td>0.422171</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.9</td>\n",
       "      <td>sigmoid</td>\n",
       "      <td>{'C': 10000, 'epsilon': 0.9, 'kernel': 'sigmoid'}</td>\n",
       "      <td>-5.653706e+11</td>\n",
       "      <td>-5.886091e+11</td>\n",
       "      <td>-5.145841e+11</td>\n",
       "      <td>-6.138102e+11</td>\n",
       "      <td>-5.705935e+11</td>\n",
       "      <td>3.659431e+10</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0       59.417859      0.256741        10.087827        0.052256       1   \n",
       "1       62.711058      0.082521        12.257909        0.056567       1   \n",
       "2       76.158629      3.385826        13.336206        0.068773       1   \n",
       "3       44.300005      0.134319         7.758465        0.116692       1   \n",
       "4       44.410237      0.204040         9.714529        0.071134       1   \n",
       "..            ...           ...              ...             ...     ...   \n",
       "70      39.473873      1.200229         2.126850        0.013703   10000   \n",
       "71      55.241236      3.149767         9.121775        1.543446   10000   \n",
       "72      23.925573      1.590841         1.003019        0.017861   10000   \n",
       "73      25.721082      0.810011         1.233614        0.111335   10000   \n",
       "74      36.888744      1.355030         4.681876        0.422171   10000   \n",
       "\n",
       "   param_epsilon param_kernel  \\\n",
       "0            0.1         poly   \n",
       "1            0.1          rbf   \n",
       "2            0.1      sigmoid   \n",
       "3            0.3         poly   \n",
       "4            0.3          rbf   \n",
       "..           ...          ...   \n",
       "70           0.7          rbf   \n",
       "71           0.7      sigmoid   \n",
       "72           0.9         poly   \n",
       "73           0.9          rbf   \n",
       "74           0.9      sigmoid   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0          {'C': 1, 'epsilon': 0.1, 'kernel': 'poly'}       3.593572e-01   \n",
       "1           {'C': 1, 'epsilon': 0.1, 'kernel': 'rbf'}       4.354827e-01   \n",
       "2       {'C': 1, 'epsilon': 0.1, 'kernel': 'sigmoid'}      -5.002699e+03   \n",
       "3          {'C': 1, 'epsilon': 0.3, 'kernel': 'poly'}       3.603206e-01   \n",
       "4           {'C': 1, 'epsilon': 0.3, 'kernel': 'rbf'}       4.422160e-01   \n",
       "..                                                ...                ...   \n",
       "70      {'C': 10000, 'epsilon': 0.7, 'kernel': 'rbf'}       5.856945e-01   \n",
       "71  {'C': 10000, 'epsilon': 0.7, 'kernel': 'sigmoid'}      -5.650226e+11   \n",
       "72     {'C': 10000, 'epsilon': 0.9, 'kernel': 'poly'}       5.445189e-01   \n",
       "73      {'C': 10000, 'epsilon': 0.9, 'kernel': 'rbf'}       5.435591e-01   \n",
       "74  {'C': 10000, 'epsilon': 0.9, 'kernel': 'sigmoid'}      -5.653706e+11   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  mean_test_score  \\\n",
       "0        3.755697e-01       3.606406e-01       3.441844e-01     3.599380e-01   \n",
       "1        4.528930e-01       4.367523e-01       4.050677e-01     4.325489e-01   \n",
       "2       -5.218150e+03      -5.249682e+03      -5.962994e+03    -5.358381e+03   \n",
       "3        3.744777e-01       3.643373e-01       3.452746e-01     3.611026e-01   \n",
       "4        4.557420e-01       4.409005e-01       4.142245e-01     4.382708e-01   \n",
       "..                ...                ...                ...              ...   \n",
       "70       5.909359e-01       5.880254e-01       5.727268e-01     5.843456e-01   \n",
       "71      -5.866412e+11      -5.117707e+11      -6.179702e+11    -5.703512e+11   \n",
       "72       5.412445e-01       5.426073e-01       5.307069e-01     5.397694e-01   \n",
       "73       5.444258e-01       5.442095e-01       5.323241e-01     5.411296e-01   \n",
       "74      -5.886091e+11      -5.145841e+11      -6.138102e+11    -5.705935e+11   \n",
       "\n",
       "    std_test_score  rank_test_score  \n",
       "0     1.110583e-02               47  \n",
       "1     1.728709e-02               42  \n",
       "2     3.617828e+02               51  \n",
       "3     1.049397e-02               46  \n",
       "4     1.504947e-02               40  \n",
       "..             ...              ...  \n",
       "70    6.960408e-03               16  \n",
       "71    3.870727e+10               71  \n",
       "72    5.359926e-03               29  \n",
       "73    5.093847e-03               28  \n",
       "74    3.659431e+10               72  \n",
       "\n",
       "[75 rows x 15 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df_svr = pd.DataFrame(grid_search_svr.cv_results_)\n",
    "cv_df_svr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "for i, k in enumerate(hyper_kernel_list):\n",
    "    plt.figure(i)\n",
    "    heat_cv = cv_df_svr[cv_df_svr['param_kernel'] == k][['param_C','param_epsilon','mean_test_score']]\n",
    "    sns.heatmap(heat_cv.pivot_table( index='param_C', columns='param_epsilon', values='mean_test_score')).set_title(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6019561304621089"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_svr.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10000, 'epsilon': 0.3, 'kernel': 'poly'}"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_svr.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=10000, epsilon=0.3, kernel='poly')"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = SVR(C = 10000, epsilon = 0.3, kernel = 'poly')\n",
    "best_model.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6279774439163159"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.score(X_train_pca,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6081839914322393"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.score(X_test_pca, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict with models run on server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting value: line 1 column 1 (char 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)\n",
      "\u001b[0;32m<ipython-input-166-a9591bedb854>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcnn_satellite\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'resNet_fullData'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/saving/save.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, options)\u001b[0m\n",
      "\u001b[1;32m    210\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    211\u001b[0m         \u001b[0mloader_impl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_saved_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m--> 212\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msaved_model_load\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    214\u001b[0m   raise IOError(\n",
      "\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/load.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(path, compile, options)\u001b[0m\n",
      "\u001b[1;32m    136\u001b[0m   \u001b[0;31m# Recreate layers and metrics using the info stored in the metadata.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    137\u001b[0m   \u001b[0mkeras_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKerasObjectLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject_graph_def\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m--> 138\u001b[0;31m   \u001b[0mkeras_loader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_layers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    140\u001b[0m   \u001b[0;31m# Generate a dictionary of all loaded nodes.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/load.py\u001b[0m in \u001b[0;36mload_layers\u001b[0;34m(self)\u001b[0m\n",
      "\u001b[1;32m    372\u001b[0m         \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m--> 374\u001b[0;31m       self.loaded_nodes[node_metadata.node_id] = self._load_layer(\n",
      "\u001b[0m\u001b[1;32m    375\u001b[0m           \u001b[0mnode_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentifier\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    376\u001b[0m           node_metadata.metadata)\n",
      "\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/load.py\u001b[0m in \u001b[0;36m_load_layer\u001b[0;34m(self, node_id, identifier, metadata)\u001b[0m\n",
      "\u001b[1;32m    383\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_load_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midentifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    384\u001b[0m     \u001b[0;34m\"\"\"Load a single layer from a SavedUserObject proto.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m--> 385\u001b[0;31m     \u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m    386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    387\u001b[0m     \u001b[0;31m# If node was already created\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/json_utils.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(json_string)\u001b[0m\n",
      "\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m     68\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson_string\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m---> 69\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson_string\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject_hook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_decode_helper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n",
      "\u001b[1;32m    368\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mparse_constant\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    369\u001b[0m         \u001b[0mkw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'parse_constant'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparse_constant\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m--> 370\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n",
      "\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    336\u001b[0m         \"\"\"\n",
      "\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m    338\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    339\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n",
      "\u001b[1;32m    353\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m    354\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m--> 355\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[1;32m    356\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
     ]
    }
   ],
   "source": [
    "cnn_satellite = keras.models.load_model('resNet_fullData')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.savetxt(f'X_eth_flat_{len(X_eth), 128, 128, 4}.txt', X_eth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.savetxt('y_eth.txt', Y_eth)"
   ]
  }
 ],
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    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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