examples/shallow/random_forests.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ce734de3",
   "metadata": {},
   "source": [
    "### Random Forests on Iris Multi-Class Classification Problem\n",
    "\n",
    "Previous example: [/examples/datasets_misc.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/datasets_misc.ipynb)  \n",
    "Next example: [/examples/shallow/pca_and_svm.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/shallow/pca_and_svm.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de952ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79ff85ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "iris = sns.load_dataset('iris')\n",
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b422964d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04f7a192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fffbb803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x281791030>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 517.75x400 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('dark_background')\n",
    "sns.pairplot(iris, hue='species', corner=True, height=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ab38c63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train                  : (100, 4)\n",
      "y_train                  : (100,)\n",
      "X_test                   : (50, 4)\n",
      "y_test                   : (50,)\n"
     ]
    }
   ],
   "source": [
    "def print_data(msg, data): print(f'{msg:25}: {data.shape}')\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.drop('species', axis=1), iris['species'], random_state=1, test_size=0.33)\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "print_data('X_train', X_train)\n",
    "print_data('y_train', y_train)\n",
    "print_data('X_test', X_test)\n",
    "print_data('y_test', y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "920a32da",
   "metadata": {},
   "outputs": [
    {
     "ename": "NotFittedError",
     "evalue": "This RandomForestClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/l6/2q9c0f9127bd3ydvrgk19ncw0000gn/T/ipykernel_7766/1489627267.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\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----> 2\u001b[0;31m \u001b[0my_train_pred_before\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/miniconda3/envs/study/lib/python3.10/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    830\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    831\u001b[0m         \"\"\"\n\u001b[0;32m--> 832\u001b[0;31m         \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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    833\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    834\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/miniconda3/envs/study/lib/python3.10/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    870\u001b[0m             \u001b[0mclasses\u001b[0m \u001b[0mcorresponds\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mattribute\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mterm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    871\u001b[0m         \"\"\"\n\u001b[0;32m--> 872\u001b[0;31m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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    873\u001b[0m         \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/miniconda3/envs/study/lib/python3.10/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_is_fitted\u001b[0;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[1;32m   1343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1344\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfitted\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1345\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"name\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\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   1346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotFittedError\u001b[0m: This RandomForestClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "y_train_pred_before = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "16b4ccf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification_report of training data:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        33\n",
      "  versicolor       1.00      1.00      1.00        31\n",
      "   virginica       1.00      1.00      1.00        36\n",
      "\n",
      "    accuracy                           1.00       100\n",
      "   macro avg       1.00      1.00      1.00       100\n",
      "weighted avg       1.00      1.00      1.00       100\n",
      "\n",
      "confusion_matrix of training data:\n",
      "[[33  0  0]\n",
      " [ 0 31  0]\n",
      " [ 0  0 36]]\n",
      "\n",
      "classification_report of test data:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        17\n",
      "  versicolor       0.95      0.95      0.95        19\n",
      "   virginica       0.93      0.93      0.93        14\n",
      "\n",
      "    accuracy                           0.96        50\n",
      "   macro avg       0.96      0.96      0.96        50\n",
      "weighted avg       0.96      0.96      0.96        50\n",
      "\n",
      "confusion_matrix of test data:\n",
      "[[17  0  0]\n",
      " [ 0 18  1]\n",
      " [ 0  1 13]]\n"
     ]
    }
   ],
   "source": [
    "def disp_results(func, gt, pred, msg): print(f'{func.__name__} of {msg}:\\n{func(gt, pred)}')\n",
    "model.fit(X_train, y_train)\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "disp_results(classification_report, y_train, y_train_pred, 'training data')\n",
    "disp_results(confusion_matrix, y_train, y_train_pred, 'training data')\n",
    "print()\n",
    "disp_results(classification_report, y_test, y_test_pred, 'test data')\n",
    "disp_results(confusion_matrix, y_test, y_test_pred, 'test data')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c484cb68",
   "metadata": {},
   "source": [
    "Previous example: [/examples/datasets_misc.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/datasets_misc.ipynb)  \n",
    "Next example: [/examples/shallow/pca_and_svm.ipynb](https://github.com/serhatsoyer/py4ML/blob/main/examples/shallow/pca_and_svm.ipynb)"
   ]
  }
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