3_Evaluate_Fit_Classifier.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "03e34a93-c6d5-443f-9682-460058c99243",
   "metadata": {},
   "source": [
    "# Fit, Optimize and Evaluate A Classifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2166e46-c953-47f0-a82b-4b5bc68b5b88",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "Now that we have training features extracted (and filtered as needed), we can train a machine learning model. There are various classification models available, and deciding which one to pick depends on the classification task at-hand. The table below provides a useful summary of the pros and cons of different models (all of which are available through [scikit-Learn](https://scikit-learn.org/stable/)). This sckit-learn [cheat sheet](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html) may also help. _Table 1: Some of the pros and cons of different classifiers available through scikit-learn_\n",
    "<img align=\"center\" src=\"../../../../../Supplementary_data/Scalable_machine_learning/classifier_pro_cons.png\" width=\"700\">\n",
    "\n",
    "\n",
    "In this example we are using a Random Forest classifier for the land cover classification task. We'll split the data into a training and testing set and use k-fold cross-validation to estimate the prediction ability of our model. To make full use of the limited number of training dara, here we will fit the final model on _all_ the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7120659-d2bf-4c4d-a5fc-1ee6f290888c",
   "metadata": {},
   "source": [
    "## Description\n",
    "\n",
    "This notebook runs through evaluating, optimizing, and fitting a machine learning classifier (in the default example, a Random Forest model is used). The steps are as follows:\n",
    "\n",
    "1. Import training features\n",
    "2. Optimize the hyperparameters of the model\n",
    "3. Fit a model to all the training data using the best hyperparameters identified in the previous step\n",
    "4. Calculate an unbiased performance estimate via cross-validation\n",
    "5. Save the model to disk for use in the subsequent notebook\n",
    "\n",
    "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell.  \n",
    "\n",
    "Note: The approximate running time for the default setting is about 3 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa5ce54e-f95a-4226-8162-e3c135d56200",
   "metadata": {},
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c9fb1dd-deff-4e08-8710-42b0ccb7e8af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.10.3-CAPI-1.16.1). Conversions between both will be slow.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import datacube\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import accuracy_score,cohen_kappa_score,confusion_matrix,ConfusionMatrixDisplay,balanced_accuracy_score\n",
    "from sklearn.metrics import balanced_accuracy_score, precision_score, recall_score\n",
    "from joblib import dump"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3234bf9e-915b-4b58-aa95-edadc01c660d",
   "metadata": {},
   "source": [
    "## Analysis parameters\n",
    "\n",
    "* `training_features_path`: The path to the file containing training features we extracted through previous notebook. `1_Extract_Training_Features.ipynb` (if you don't want to filter your training data) or through `2_Filter_Training_Data.ipynb` (if training data filtering is needed).\n",
    "* `dict_map` : A dictionary map of class names corresponding to pixel values. This should be consistent with your classes derived from the notebook `0_Generate_Training_Points.ipynb`.\n",
    "* `class_attr`: This is the name of column in your shapefile/geojson file attribute table that contains the class labels. **The class labels must be integers.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb004d94-5b7b-40e0-a217-fda2633629f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_features_path='Results/Training_features_filtered.txt'\n",
    "dict_map={1:'Forest',5:'Grassland',7:'Shrubland',9:'Perennial Cropland',10:'Annual Cropland',\n",
    "          11:'Wetland',12:'Water Body',13:'Urban Settlement',14:'Bare Land'}\n",
    "class_attr = 'LC_Class_I'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dfe6528-7830-485d-aeef-4a57c983160f",
   "metadata": {},
   "source": [
    "## Import training data\n",
    "We now load the training features file. The dataframe should contain columns `class_attr` identifying class labels and the bi-monthly geomedians of the spectral bands and NDVI that we extracted through previous module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46219172-f902-47a3-8884-fe689790f175",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LC_Class_I</th>\n",
       "      <th>blue_0</th>\n",
       "      <th>blue_1</th>\n",
       "      <th>blue_2</th>\n",
       "      <th>blue_3</th>\n",
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       "      <th>...</th>\n",
       "      <th>swir_2_2</th>\n",
       "      <th>swir_2_3</th>\n",
       "      <th>swir_2_4</th>\n",
       "      <th>swir_2_5</th>\n",
       "      <th>NDVI_0</th>\n",
       "      <th>NDVI_1</th>\n",
       "      <th>NDVI_2</th>\n",
       "      <th>NDVI_3</th>\n",
       "      <th>NDVI_4</th>\n",
       "      <th>NDVI_5</th>\n",
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       "      <td>644.816040</td>\n",
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       "      <td>1013.999817</td>\n",
       "      <td>948.500000</td>\n",
       "      <td>702.840820</td>\n",
       "      <td>...</td>\n",
       "      <td>1123.165527</td>\n",
       "      <td>1310.653076</td>\n",
       "      <td>1791.823853</td>\n",
       "      <td>1560.500000</td>\n",
       "      <td>0.612483</td>\n",
       "      <td>0.616036</td>\n",
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       "      <td>0.587589</td>\n",
       "      <td>0.419678</td>\n",
       "      <td>0.530195</td>\n",
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       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
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       "      <td>2437.500000</td>\n",
       "      <td>464.546265</td>\n",
       "      <td>610.075562</td>\n",
       "      <td>988.042358</td>\n",
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       "      <td>1151.546631</td>\n",
       "      <td>2717.500000</td>\n",
       "      <td>703.996582</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.482598</td>\n",
       "      <td>0.248337</td>\n",
       "      <td>0.581490</td>\n",
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      "text/plain": [
       "   LC_Class_I       blue_0       blue_1      blue_2      blue_3      blue_4  \\\n",
       "0        10.0   552.311523   636.482727  434.500000  517.705444  793.366882   \n",
       "1        10.0   559.533569  1288.758179  430.151794  521.289246  721.967896   \n",
       "2        10.0   605.999939   680.500000  413.134064  514.050171  691.459351   \n",
       "3        10.0   946.741211  2437.500000  464.546265  610.075562  988.042358   \n",
       "4        10.0  1064.862305   506.292664  285.434601  522.204895  717.104858   \n",
       "\n",
       "       blue_5      green_0      green_1     green_2  ...     swir_2_2  \\\n",
       "0  740.098511   773.294678  1023.597839  786.000000  ...  1661.500000   \n",
       "1  307.000000   790.738037  1673.564209  644.816040  ...  1372.661133   \n",
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       "3  522.236145  1151.546631  2717.500000  703.996582  ...   596.633301   \n",
       "4  666.887268  1185.977661   681.131775  490.491119  ...  1078.689941   \n",
       "\n",
       "      swir_2_3     swir_2_4     swir_2_5    NDVI_0    NDVI_1    NDVI_2  \\\n",
       "0  1938.977783  2298.773438  2318.896973  0.406971  0.586414  0.573067   \n",
       "1  1685.551392  1648.115723  1398.500000  0.697875  0.452891  0.755627   \n",
       "2  1310.653076  1791.823853  1560.500000  0.612483  0.616036  0.693201   \n",
       "3   923.750977   722.460571   637.218872  0.264174  0.188451  0.696107   \n",
       "4  1406.790649  1630.183472   850.445007  0.487073  0.752476  0.845713   \n",
       "\n",
       "     NDVI_3    NDVI_4    NDVI_5  \n",
       "0  0.423769  0.351241  0.367363  \n",
       "1  0.620328  0.569913  0.799792  \n",
       "2  0.587589  0.419678  0.530195  \n",
       "3  0.482598  0.248337  0.581490  \n",
       "4  0.520688  0.447816  0.558978  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_training_data= pd.read_csv(training_features_path,sep=\" \")\n",
    "df_training_data.head() # Plot first five rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "605d79a2-2c59-4805-ba84-6a6e2e2ff898",
   "metadata": {},
   "source": [
    "To inspect the number of samples for each class, we can plot a histogram by class. Note that the training features only contain integer class values, we retrive the class names using the `dict_map` for better interpratation of the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fb38112-c17b-43a0-95a0-46685cb2242d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Land Cover Class')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count numbers of classes\n",
    "class_counts=df_training_data[class_attr].value_counts()\n",
    "\n",
    "# get class names\n",
    "class_indices=class_counts.index\n",
    "class_legends=[dict_map[class_indices[i]] for i in range(len(class_indices))]\n",
    "\n",
    "# plot training samples distribution\n",
    "plt.figure(figsize=(15,5))\n",
    "ax=plt.bar(class_legends,height=class_counts.to_numpy())\n",
    "plt.bar_label(ax)\n",
    "plt.gca().set_ylabel('Number of Training Samples')\n",
    "plt.gca().set_xlabel('Land Cover Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04784a96-dbca-40af-9bc3-4098ede9536c",
   "metadata": {},
   "source": [
    "> Note: One of the common problems with training data is **class imbalance**. This can occur when one of your classes is relatively rare and therefore the rare class will comprise a smaller proportion of the training set. When imbalanced data is used, it is common that the final classification will under-predict less abundant classes relative to their true proportion. If you think your data is imbalanced, you may consider adding more training samples for under-represented classes or removing samples for over-represented classes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe49424f-bd97-4755-87ba-de9980b2cf20",
   "metadata": {},
   "source": [
    "## Optimize hyperparameters\n",
    "\n",
    "Machine learning models require certain 'hyperparameters': model parameters that can be tuned to increase the prediction ability of a model. Finding the best values for these parameters is a 'hyperparameter search' or an 'hyperparameter optimization'.\n",
    "\n",
    "To optimize the parameters in our model, we use [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) to exhaustively search through a set of parameters and determine the combination that will result in the highest accuracy based upon the accuracy metric defined. We first need to define a grid of parameters to be used in the optimization:\n",
    "* `grid_parameters`: a dictionary of model specific parameters to search through during hyperparameter optimization.\n",
    "\n",
    "> **Note**: the parameters in the `grid_parameters` object depend on the classifier being used. The default example is set up for a Random Forest classifier. To adjust the parameters to suit a different classifier, look up the important parameters under the relevant [sklearn documentation](https://scikit-learn.org/stable/supervised_learning.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "230d39ef-e596-4e75-b9d6-05731866ca78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the parameter grid based on the results of random search \n",
    "grid_parameters={'n_estimators': [int(x) for x in np.linspace(start = 100, stop = 300, num = 3)],\n",
    "                 'max_features': ['sqrt', 'log2'],'class_weight':['balanced', 'balanced_subsample']}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1caafbd4-22a1-4d37-aeb1-a4aec7c4fe1c",
   "metadata": {},
   "source": [
    "We now start searching for the optimal hyperparameters using `GridSearchCV`. It may take a while depending on your searching space. Here we use k-fold splitting strategy for the search, which we will cover later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "79cdc57f-fa47-4905-b000-a061f6383114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid searching best hyper-parameters...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal parameters: \n",
      " {'class_weight': 'balanced_subsample', 'max_features': 'sqrt', 'n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    "# initialise a random forest classifier\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "# stratified K-fold splitting strategy for grid search\n",
    "cv=model_selection.StratifiedKFold(n_splits=5,shuffle=True,random_state=0) \n",
    "\n",
    "# grid search to find optimal random forest classifier hyperparameters\n",
    "print('Grid searching best hyper-parameters...')\n",
    "grid_search=model_selection.GridSearchCV(estimator = rf,param_grid=grid_parameters,cv=cv,n_jobs=-1)\n",
    "\n",
    "#convert variable names into sci-kit learn nomenclature\n",
    "X = df_training_data.to_numpy()[:,1:]\n",
    "y = df_training_data.to_numpy()[:,0]\n",
    "\n",
    "# fit to all the data\n",
    "grid_search.fit(X,y)\n",
    "print('Optimal parameters: \\n',grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e43f41-4533-4521-b957-28f1f8b45ccf",
   "metadata": {},
   "source": [
    "## Fit the model\n",
    "\n",
    "Using the best parameters from our hyperparmeter optimization search, we now fit our model on all the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "463edee9-36f7-49ea-b413-d812f8b7da1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced_subsample&#x27;, n_estimators=300,\n",
       "                       n_jobs=-1, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced_subsample&#x27;, n_estimators=300,\n",
       "                       n_jobs=-1, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced_subsample', n_estimators=300,\n",
       "                       n_jobs=-1, random_state=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(**grid_search.best_params_, random_state=1, n_jobs=-1)\n",
    "rf.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "494c71b7-6e5e-4937-bd4f-122b7ff636e3",
   "metadata": {},
   "source": [
    "## Determine feature importance\n",
    "\n",
    "Here we extract classifier estimates of the relative importance of each feature for training a Random Forest classifier. This is useful for potentially selecting a subset of input bands/variables for model training/classification (i.e. optimising feature space). However, in this case, we are not selecting a subset of features, but rather just trying to understand the importance of each feature. This can help us not only understand our classes better (e.g. what kinds of measurements are more important in predicting the land cover classes), but can also lead to further improvements to the model. \n",
    "\n",
    "Sklearn has good documentation on different methods for [feature selection](https://scikit-learn.org/stable/modules/feature_selection.html).\n",
    "\n",
    "Results will be presented in ascending order with the most important features listed last. Importance is reported as a relative fraction between 0 and 1. These importances are based on how much a given feature, on average, decreases the weighted Gini impurity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "81e338be-59f8-4070-9b23-447818b9a1d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Importance')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 3000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importance\n",
    "plt.figure(figsize=(30,5))\n",
    "order=np.argsort(rf.feature_importances_)\n",
    "plt.bar(x=np.array(df_training_data.columns[1:])[order],height=rf.feature_importances_[order],width=0.5,align='center')\n",
    "plt.xticks(rotation='vertical',fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel('Variable', fontsize=20)\n",
    "plt.ylabel('Importance', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4ae500-0d30-41fa-ac60-445a8afbb53b",
   "metadata": {},
   "source": [
    "Observe the above figure and get an idea of what bands/features are most important for discriminating different classes in our selected area. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91dc42f-e417-4451-80fc-da456eb167dd",
   "metadata": {},
   "source": [
    "## Evaluate performance via cross-validation\n",
    "\n",
    "K-fold [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) is a statistical method used to estimate the performance of machine learning models when making predictions on data not used during training.  It is a popular method because it is conceptually straightforward and because it generally results in a less biased or less optimistic estimate of the model skill than other methods, such as a simple train/test split. This procedure can be used both when optimizing the hyperparameters of a model on a dataset, and when comparing and selecting a model for the dataset. \n",
    "\n",
    "Since our training dataset is small and to make sure each class has samples in the training set, in this example the `StratifiedKFold` splitting is used. Stratified k-fold is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set. We define 5 folds splitting, which divides the dataset into 5 sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "befc0885-66b5-486e-b692-203f082736c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# stratified K-fold splitting\n",
    "skf=model_selection.StratifiedKFold(n_splits=5,shuffle=True,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cffe18d0-9c39-4258-907c-b71ba7a69c69",
   "metadata": {},
   "source": [
    "We now use `cross_val_score` function which calculates an accuracy score that show how well our classifier is doing at recognising points in each test split through cross validation. The default example is set up to calculate the `accuracy_score` or Overall Accuracy (OA), which is the fraction of correct predictions for all the samples. The OA will be between 0 and 1, with a value of 1 indicating a perfect score. There are other scoring paramters that you can choose [here](https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1d4dfcc-8738-47d5-9674-23d3a87c34ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall accuracy from cross validation:  0.75\n"
     ]
    }
   ],
   "source": [
    "# calculate overall accurcy through cross validation\n",
    "overall_acc=model_selection.cross_val_score(rf,X,y,cv=skf,scoring='accuracy')\n",
    "print('Overall accuracy from cross validation: ',np.mean(overall_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f106ba3-6460-4fe9-9f21-17a44e77cc54",
   "metadata": {},
   "source": [
    "To show how the prediction performs on each class, the example will calculate the predictions from cross validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c0af456f-6e1d-4177-91bc-2eac25a8a69c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# predict through cross validation\n",
    "predictions=model_selection.cross_val_predict(rf,X,y,cv=skf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad36c554-43bb-4a23-886d-a12c87d6f5b8",
   "metadata": {},
   "source": [
    "Using the predicted labels, we can calculate class-wise scores, such as precision and recall. The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0. We use the function `precision_score` for the calculation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c66cc22c-cc45-420f-9d43-dfc87ecbaa90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision for each class: \n",
      " {'Forest': 0.0, 'Grassland': 0.0, 'Shrubland': 0.0, 'Perennial Cropland': 1.0, 'Annual Cropland': 0.762, 'Wetland': 0.769, 'Water Body': 0.0, 'Urban Settlement': 0.0, 'Bare Land': 0.667}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "precision=precision_score(y,predictions,labels=list(dict_map.keys()),average=None)\n",
    "print('Precision for each class: \\n',dict(zip([dict_map[value] for value in list(dict_map.keys())],np.around(precision,3))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42838d41-7094-4f7d-9e98-55fa51315a04",
   "metadata": {},
   "source": [
    "Similarly we can calculate the recall scores using the function `recall_score`. The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad9ab939-7004-49e6-b284-6caa0f40ae16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall for each class: \n",
      " {'Forest': 0.0, 'Grassland': 0.0, 'Shrubland': 0.0, 'Perennial Cropland': 0.333, 'Annual Cropland': 0.97, 'Wetland': 0.833, 'Water Body': 0.0, 'Urban Settlement': 0.0, 'Bare Land': 0.667}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "recall=recall_score(y,predictions,labels=list(dict_map.keys()),average=None)\n",
    "print('Recall for each class: \\n',dict(zip([dict_map[value] for value in list(dict_map.keys())],np.around(recall,3))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a171d8-756c-4c13-9512-4d1738da8877",
   "metadata": {},
   "source": [
    "**Note**: In this example, for the classes that don't exist in your training data it will print out scores of 0.0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d833729-e7d5-4f08-b9dc-ce1c50e40263",
   "metadata": {},
   "source": [
    "To further inspect the actual numbers of predictions for each class we calculate and plot the `confusion_matrix`. By definition, entry *i, j* in a confusion matrix is the number of observations actually in group *i*, but predicted to be in group *j*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db755bb8-0bb0-4a54-b34a-7b598c7715f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion Matrix')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate and visualise confusion matrix\n",
    "cm=confusion_matrix(y, predictions)\n",
    "display_labels=[dict_map[rf.classes_[i]] for i in range(len(rf.classes_))]\n",
    "disp=ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=display_labels)\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "disp.plot(ax=ax)\n",
    "ax.set_title('Confusion Matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7883457-87d0-465c-a02e-18ca957e33f5",
   "metadata": {},
   "source": [
    "Have a look at the precision, recall score and confusion matrix to get an idea of the classes are predicted well and what classes are hard to be distinguished by the model. In your project, your accuracy scores are likely to be significantly improved by higher-quality training and validation data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ebcc39f-684d-4b27-b681-b1fd095a9c8a",
   "metadata": {},
   "source": [
    "## Save the model\n",
    "We now export the trained classifier as a binary`.joblib` file, which will allow for importing the model in the subsequent module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae663572-828d-4f81-af54-ceb26817589e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Results/RF_model.joblib']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(rf, 'Results/RF_model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1136a02-19bb-409c-84e9-58e4cec723ce",
   "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": "3.8.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "01a9736267bf300689e8e016092cd01f0c67384dd94651ae6e139a291bc8cc97"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}