1_Train_assess_model_by_location.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b50227d6-bbb5-4d0e-a75b-e697ac60ca97",
   "metadata": {},
   "source": [
    "# Train a classification model for Sentinel-1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3960259-c9a7-4852-81f4-8743e5a38abb",
   "metadata": {},
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a264c18-2871-4240-a6cb-1e07008d7643",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "os.environ['USE_PYGEOS'] = '0'\n",
    "import datacube\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import itertools\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score,cohen_kappa_score,confusion_matrix,ConfusionMatrixDisplay,balanced_accuracy_score\n",
    "from deafrica_tools.classification import predict_xr\n",
    "import random\n",
    "import glob\n",
    "import pickle\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bdd9e56-c886-42c6-bda2-a9b3bd9f2ae3",
   "metadata": {},
   "source": [
    "## Load training datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a92b692-79cf-402c-a1e9-962d14115efc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples:  10000\n",
      "Number of samples:  10000\n",
      "Number of samples:  10000\n",
      "Number of samples:  10000\n",
      "Number of samples:  10000\n",
      "locations:  ['Madagascar_south', 'Madagascar_west', 'Tanzania', 'Kenya', 'Comoros']\n"
     ]
    }
   ],
   "source": [
    "input_folder='data'\n",
    "files=glob.glob(os.path.join(input_folder,'*.npz'))\n",
    "location_names=[]\n",
    "list_data=[]\n",
    "list_labels=[]\n",
    "for file in files:\n",
    "    location_names.append(os.path.basename(file).split('.')[0])\n",
    "    dataset = np.load(file)\n",
    "    data=dataset['data']\n",
    "    labels=dataset['labels']\n",
    "    \n",
    "    # subsample\n",
    "    if data.shape[0]>10000:\n",
    "        rand_indices=random.sample(range(0, data.shape[0]), 10000)\n",
    "        data=data[rand_indices]\n",
    "        labels=labels[rand_indices]\n",
    "    \n",
    "    list_data.append(data)\n",
    "    list_labels.append(labels)\n",
    "    print('Number of samples: ',len(labels))\n",
    "\n",
    "data=np.concatenate(list_data)\n",
    "labels=np.concatenate(list_labels)\n",
    "print('locations: ',location_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab09fad7-71c6-42ce-b282-3ecd6e6c1df3",
   "metadata": {},
   "source": [
    "#### Load feature names for plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22582e6d-5255-46d5-b5e2-bdfa19f856d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['vh', 'vv', 'vv_a_vh', 'vv_m_vh', 'area', 'vh_std', 'vv_std', 'vv_a_vh_std', 'vv_m_vh_std', 'area_std']\n"
     ]
    }
   ],
   "source": [
    "with open ('s1_features', 'rb') as file:\n",
    "    feature_names = pickle.load(file)\n",
    "print(feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7b431a4-c914-4c7e-bfa4-a78e665c4bc9",
   "metadata": {},
   "source": [
    "## Build a classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74cf8e5a-7cd1-4392-9ec9-fe98b9ff489f",
   "metadata": {},
   "source": [
    "### Fit a random forest model per location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7d175fc-7a68-4a09-a9cb-d69d42d3cbca",
   "metadata": {},
   "outputs": [],
   "source": [
    "models=[]\n",
    "for i in range(len(location_names)):\n",
    "    rf=RandomForestClassifier(n_estimators=200,max_samples=0.5,min_samples_leaf=1,bootstrap=True)\n",
    "    rf.fit(list_data[i],list_labels[i])\n",
    "    models.append(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8090efba-104b-41eb-9f52-58372af495e0",
   "metadata": {},
   "source": [
    "### Plot feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d807a5ea-eabe-4bac-8cae-41ff378d909b",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 4000x2000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax=plt.subplots(len(location_names),1,figsize=(40,20))\n",
    "for i in range(len(location_names)):\n",
    "    order=np.argsort(models[i].feature_importances_)\n",
    "    ax[i].bar(x=np.array(feature_names)[order],height=models[i].feature_importances_[order])\n",
    "    ax[i].set_ylabel('Importance',fontsize=30)\n",
    "    ax[i].set_xlabel(location_names[i],fontsize=30)\n",
    "    ax[i].tick_params(axis='x', labelsize=30)\n",
    "    ax[i].tick_params(axis='y', labelsize=30)\n",
    "    # Save the models\n",
    "    outfile='rf_model_'+location_names[i]\n",
    "    with open(outfile,'wb') as out_model:\n",
    "        pickle.dump(models[i],out_model)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d826e281-4abe-45e2-bedc-a87aa6237e5d",
   "metadata": {},
   "source": [
    "### Plot confusion matrix by location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f5d5ae7-7bf1-477d-a8a5-ad655260e840",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skf=model_selection.StratifiedKFold(n_splits=5,shuffle=True,random_state=1) # stratified K-fold splitting\n",
    "fig, ax = plt.subplots(1,len(location_names),figsize=(15,3))\n",
    "for i in range(len(location_names)):\n",
    "    predictions_i=model_selection.cross_val_predict(models[i],list_data[i],list_labels[i],cv=skf)\n",
    "    cm=confusion_matrix(list_labels[i], predictions_i)\n",
    "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    im = ax[i].imshow(cm, cmap='Blues')\n",
    "    for row in range(len(cm)):\n",
    "        for col in range(len(cm)):\n",
    "            value = \"{:.2f}\".format(cm[row, col])\n",
    "            ax[i].text(col, row, value, ha='center', va='center', color='black')\n",
    "    ax[i].set_title(location_names[i])\n",
    "    ax[i].set_xlabel('Predicted label')\n",
    "    ax[i].set_ylabel('True label')\n",
    "    tick_labels = ['water','non-water']\n",
    "    ax[i].set_xticks(np.arange(len(tick_labels)))\n",
    "    ax[i].set_yticks(np.arange(len(tick_labels)))\n",
    "    ax[i].set_xticklabels(tick_labels)\n",
    "    ax[i].set_yticklabels(tick_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8328b7bb-bd6f-4f6e-a1f4-b68f61eef251",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}