imbalanced_learn_python_package_for_machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bzlL-fHRgQah"
   },
   "source": [
    "# **A. Install imbalanced-learn**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RkGjy7jBKkKy",
    "outputId": "b1e4e075-05be-468f-dda6-9bc9642e9e49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting imbalanced-learn\n",
      "  Using cached imbalanced_learn-0.9.1-py3-none-any.whl (199 kB)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.6.2)\n",
      "Requirement already satisfied: scikit-learn>=1.1.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.1.3)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.20.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from imbalanced-learn) (2.1.0)\n",
      "Requirement already satisfied: joblib>=1.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.0.1)\n",
      "Installing collected packages: imbalanced-learn\n",
      "Successfully installed imbalanced-learn-0.9.1\n"
     ]
    }
   ],
   "source": [
    "! pip install -U imbalanced-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9R-SY-ZmgYK_"
   },
   "source": [
    "# **B. Read data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "IGbr9AzuKKFH",
    "outputId": "423b98eb-680f-49c4-8fe6-6d447db785d1"
   },
   "outputs": [
    {
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       "<p>578 rows × 882 columns</p>\n",
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      ],
      "text/plain": [
       "     PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  PubchemFP4  PubchemFP5  \\\n",
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       "     PubchemFP6  PubchemFP7  PubchemFP8  PubchemFP9  ...  PubchemFP872  \\\n",
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       "\n",
       "     PubchemFP873  PubchemFP874  PubchemFP875  PubchemFP876  PubchemFP877  \\\n",
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       "\n",
       "     PubchemFP878  PubchemFP879  PubchemFP880  Activity  \n",
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       "\n",
       "[578 rows x 882 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('C://data/hcv_classification.csv',index_col=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "rzDaIo_L-upA"
   },
   "outputs": [],
   "source": [
    "X = df.drop(['Activity'], axis=1)\n",
    "y = df['Activity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A2NNvmc8grik"
   },
   "source": [
    "# **C. Class distribution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KbgTftGQKtH4",
    "outputId": "5a0a72ad-7788-45e9-de16-72d790a60ad1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "active      412\n",
       "inactive    166\n",
       "Name: Activity, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity_count = y.value_counts()\n",
    "activity_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "id": "YM0hGKo6LH4T",
    "outputId": "e5a6ed9e-4385-416c-ce37-caf8325ab564"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Activity'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show pie plot (Approach 1)\n",
    "y.value_counts().plot.pie(autopct='%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "id": "ITo2bYdkNAyn",
    "outputId": "61efde64-847f-4e73-90c4-ce492e0f3272"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fac5b0aa790>,\n",
       "  <matplotlib.patches.Wedge at 0x7fac5b0aaf50>],\n",
       " [Text(-0.6818247054992795, 0.8632004813314349, 'active'),\n",
       "  Text(0.6818247054992798, -0.8632004813314348, 'inactive')],\n",
       " [Text(-0.3719043848177888, 0.4708366261807826, '71.28'),\n",
       "  Text(0.3719043848177889, -0.47083662618078254, '28.72')])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show pie plot (Approach 2)\n",
    "import matplotlib.pyplot as plt\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(y.value_counts(), autopct='%.2f', labels=activity_count.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B7vaPsdtb6yo",
    "outputId": "23607239-aaea-460e-d6fd-6208b7d64847"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['active', 'inactive'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity_count.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CzAfcmYPCQ7T"
   },
   "source": [
    "# **D. Data splitting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Agda0CIMAq4f"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QeD4QKpgAq7i",
    "outputId": "84393bd1-2588-481d-b18a-67d475fd1ad4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((462, 881), (462,), (116, 881), (116,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "id": "nmsLc0-3DtNF",
    "outputId": "d4282680-40f7-4bad-a57d-758a69527daa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Activity'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart of the training set\n",
    "y_train.value_counts().plot.pie(autopct='%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2bmXwX9pEAG4",
    "outputId": "e685460e-aad8-4a76-89a9-497aa80e0f15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "active      330\n",
       "inactive    132\n",
       "Name: Activity, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WqpU647eE3x0"
   },
   "source": [
    "# **E. Class balancing**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z9Iaj_7Z_1tD"
   },
   "source": [
    "### **Random Undersampling**\n",
    "\n",
    "``'not minority'`` = resample all classes but the minority class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "UmASVvdo9ZXT",
    "outputId": "809b5936-d5ff-47ca-b44f-9f84b4358d09"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD3CAYAAADFeRJuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAbDElEQVR4nO3deZgcVdXH8e+ZJeskE8ISkoB0MGIw0aAQERQEUVQmIggYBDESIWzKor7SKEKB6Du4IaKAwQVF4E2IQCKNkUWCLLIFkkhYZBshZAGSTGcyWWfmvH/cmqQzayfp7ltVfT7P08/0VlVnevo393Z11b2iqhhjkqPCdwHGmMKyUBuTMBZqYxLGQm1MwliojUkYC7UxCWOhjjAR+aqIPOy7jkISkbkiclp4/WQRucd3TUljoS4gEVERGd3hvkBE/uyrpihT1ZtV9UjfdSSNhTqhRKTSdw3GDwt1CYnIYSKyWES+JSJvichSETk15/GdRWS2iKwWkSeAd3dYfoyI3CsiK0XkRRH5Ys5jN4rIdSJyt4g0A4d3WFZE5Kpwu1kRWSgi48LH6kTkmXC7b4hIkLNcKuyBnBo+tkpEzhSRCeE6GkXkVznP/6qIPCIi14TbeUFEjujm9djq40W4nTNF5KVwO78WEQkfqxSRn4nIOyLymoh8PXx+1Xb+ORLLXpDS2x2oBUYCnwJmisidqroK+DWwHhgOjAL+DrwGICIDgXuBS4DPAh8A7hGRRaq6KFz3ScBRwESgT4ftHgkcCuwDZIExQGP4WDPwFWARMA64V0Tmq+qdOcsfCLwnXMdsYA7wSaAaeEZEblPVB3OeOxPYBfgCcLuIjFLVlXm8PhOBCcBgYB7w13Bbp4e/935hvbflsa6yZC116W0CLlfVTap6N7AGeG/YXT4OuERVm1X1WeCPOctNBBpU9Q+q2qKqTwN/AY7Pec4sVX1EVdtUdX0X2x2EC7Oo6vOquhRAVeeq6r/D5RYCtwIf77D8D1R1varegwvVrar6lqq+CTwEfDDnuW8Bvwh/x+nAi0Bdnq9Pvao2qurrwAO4EAN8EbhaVReH/wDr81xf2bFQF1YrruXKVY0LVLsVqtqSc3stUAPsius5vZHz2H9zru8FHBh2dxtFpBE4Gdfyt9u8rIgsEpE14eUQVf0H8Ctcb2C5iEwTkcHhcw8UkQdE5G0RyQJn4lrZXMtzrq/r4nZNzu03deszhf4LjCA/y3Kut782hMvnvja5100OC3VhvQ6kOtw3iq3D2Z23gRZgz5z73pVz/Q3gQVUdknOpUdWzcp6zOUiqOjZ8vEZVHwrv+6Wq7g+MxXXD/yd8+i24LvWeqloLXA9IHjV3Z2T7Z+Gc32PJDqwPYCmwR87tPbt7YrmzUBfWdOBiEdlDRCpE5JPA53CfL3ukqq3A7UAgIgNE5H3A5Jyn3AXsIyKniEh1eJkgIvvmU1j43ANFpBrXfV6P61mA65avVNX1IvJh3GfzHbEbcG5Y4wnAvsDdO7jOGcB5IjJSRIYAF+7g+hLLQl1YlwOPAg8Dq4AfAyeHn4/z8XVcd3MZcCPwh/YHVLUJt7PrRFyrtwy4Euib57oHAzeEdf0XWAH8NHzsbOByEWnC7Yibkec6u/M4bqfaO8APgeNVdcUOrvMG4B5gIfAM7p9EC1v+MZmQ2CAJppBE5KvAaar6sSJv57PA9aq6VzG3E0fWUptYEJH+InKUiFSJyEjgUuAO33VFkYXaxIUAl+E+PjwDPI/7qGA6sO63MQljLbUxCWOhNiZhLNTGJIyF2piEsVAbkzAWamMSxkJtTMJYqI1JGAu1MQljoTYmYSzUZruEgygenHP7TBH5is+ajGMDD5rtdRhufLVHAVT1eq/VmM2spTZbEZE7RWReOMbZ1PC+z4jI0yKyQETuF5EUbhyzC0RkvogcIm7Sgm+LyL7h8Mbt60uJyMLw+v4i8mC4/r+LyHAvv2TCWUttOpqiqitFpD/wpIjMwo06cqiqviYiQ8PHrwfWqOpPAdrH9lbV50Wkj4jsraqvApOAGeEwStcAn1fVt0VkEm5UlCk+fskks1Cbjs4VkWPD63sCU4F/quprAHmO3T0DN6RvPS7Uk4D3smVMcYBK3GCCpsAs1GYzETkMN0D/Qaq6VkTmAgtwgdwW04HbROR2QFX1JRF5P7BIVQ8qYMmmC/aZ2uSqBVaFgR4DfAQ3sOHHRWQUgIgMDZ/bhBuFtBNVfQU3IOD3cQEHN6D/riJyULieahEZW7TfpIxZqE2uOUBVuGPrB8BjuPHIp+KmzlnAlpD+FTi2fUdZF+uaDnyZcGRSVd2Im03kynA984GDu1jO7CAbzsiYhLGW2piEsVAbkzAWamMSxkJtTMLY99QJlEpn+uAmrm+/jOjwcxjQH/f3b7+Am5uqBTf1bjNuvq6luLm7Ov5c3lBfZ/NYRZDt/Y65VDqzE/AhYH/ggPDnKHZsKtp8tOK+e34KmBde5jfU1zUXebumFxbqGEmlM5XAQbjvd9sDvLfXorbWBrzAlqA/1FBf94zfksqPhTriUunMIOAzuHmujwJ29lvRNnsDN7f2bOAfDfV1Gz3Xk3gW6ghKpTN7AkeHl8OAPl4LKpwm3BzTs4FMQ33djs5ZbbpgoY6IVDrTHzgJOAOY4LmcUmgF/gFcD8yynW6FY6H2LJXOvAc4G5gM7OS5HF8W487ZntZQX7fMdzFxZ6H2INzhdTQuzEdQ/D3VcbEJN5H8tQ31dQ/6LiauLNQllEpn+gFfB84D9vBcTtQtAn4C3NRQX9fmu5g4sVCXQNgynwoEwEi/1cTOs8D3GurrZvsuJC4s1EWWSmeOA64AxviuJeYeAdIN9XUP+y4k6izURZJKZw7HjdH1Yd+1JEwGuKihvu7fvguJKgt1gaXSmb2Ba4FP+64lwdqAm4BvNtTX5TMQYlmxUBdIKp0R4Bxc6zzQcznlYjlwZkN93Z2+C4kSC3UBhK3z73BHf5nSuwX4hrXajoV6B1jrHCnWaocs1NvJWufIKvtW20K9HVLpzPHAjVjrHFVLgS801Nc95rsQHyzU2yDsbge4Qert0M5o2wBMbaiv+5PvQkrNQp2nVDozEPgT8AXftZht8nPgO+V0FpiFOg+pdCYFzAI+4LkUs33+DpzYUF/X6LuQUrBQ9yKVznwcmAns4rsWs0P+AxzdUF/3ou9Cis2GCO5BKp05DbgXC3QS7AM8nkpnPum7kGKzUHcjlc6cjztxv9pzKaZwaoG7UunM53wXUkwW6i6k0pk0cJXvOkxR9AX+Ep49l0gW6g5S6cylwP/6rsMUVTUwPZXOfMl3IcVgO8pypNKZ7wI/9F2HKZlW3F7xmb4LKSQLdSiVzlyA+07TlJdNwHEN9XV/9V1IoViogVQ6cwZuqFpTnjYAExvq6+7zXUghlH2oU+nMp4C/AZW+azFeZYEDk/A9dlmHOpXOjAaeoHzH2zZb+w8u2I2+C9kRZRvqVDozGHgM2Nd3LYW2+LopVPTpDxUVSEUlwyf/gtZ1Tbwz60paVi+navAwdjkmTWW/mk7Lrnt1HivvnwZtbdSMP5Laj5wAkPfyCTAH1xWP7bHiZfmVViqdqcCdd5u4QLcb9qUfMeLUaxg++RcArH7sNvqlxjNy6g30S41n9WO3dVpG21pZee917HbCZYw47Vqan3uQje+8nvfyCfEZ4Me+i9gRZRlq4EdAne8iSmnty48zcNwRAAwcdwRrX+p8qvHGpf+hashwqofsjlRWM3DfQ1kXPi+f5RPkm6l05iu+i9heZRfqVDpzEnCh7zqKSoS3ZlzC0hvPo2n+HABamxupqhkKQFXNUNqaGzst1tK0gqrBu26+XTloF1rXrMh7+YSZlkpnDvRdxPao8l1AKaXSmbG4IYgSbfeTf0zVoJ1pbW5k+fSLqd55R2b4KduxIPoCd6TSmXFxGxqpbFrqVDpThRuCqJ/nUoquapCbl75y4BAG7HMQG5b8h8qBQ2hZ496bLWtWUjFwSJfLtax+e/Pt1qZ3qAxb53yWT6DhwDW+i9hWZRNq4DvAAb6LKLa2jetp27B28/X1rz1Dn133YsDoA2l+9n4Amp+9nwGjO/cs+wzfh5ZVS9jUuAxt3UTz8/+kf/i8fJZPqJNS6cwxvovYFmXxlVbY7X4a6OO7lmLb1LiMt2+/wt1oa2Pg+z5O7cGTaF23mndm1dOy+m2qBu/KLp+/iMr+g2hpWsGKOb9k2AmXAbDulSdZef8NoG3UvP9T1B48CaDb5cvEMmBsXLrhiQ912O3+F2XQSpuiuqWhvu5k30Xkoxy632XR7TZFF5tueKJb6nLqdpuSiEU3PLEtdThG9++wQJvC2R34me8iepPYUAPHA2Wzi9aUzFdS6cz7fRfRk0SGOtw5doXvOkwiVeAOM46sRIYamIIbEtaYYpiYSmc+5ruI7iQu1Kl0pj9wqe86TOLV+y6gO4kLNXAuMMJ3ESbxPhrV8cMT9ZVWKp3ZCXgVGOK5FFMengXGN9TXtfkuJFfSWuoLsUCb0hkHfNl3ER0lJtSpdKYGONt3HabsfMd3AR0lJtTAKUDZnGFgImNsODNqZCQp1Gf5LsCUrUj1EBOxoyyVzhwC/NN3HaZsbQLe1VBft8x3IZCcljpS/ylN2akGTvddRLvYt9SpdGYY8Dp24obxazGQisJ44UloqU/DAm382wM42ncREPNQh4PyT/VdhzGhSOysjXWocadWvst3EcaEPpFKZ4b6LiLuoY5Ed8eYUCURmPnFQm1MYXl/T8Z273cqnXk38LLvOozpoAnYpaG+bqOvAuLcUnv/j2hMFwYBh/sswEJtTOF5fW/GMtThedORHU7GlD2vgyfEMtTAZymzGTtNrOyZSmf287XxuIb6EN8FGNMLb+/RuIZ6f98FGNMLb1M9xS7UqXSmGviA7zqM6YW3hid2ocaNC9XXdxHG9GJMKp0Z4GPDcQy1db1NHFQC+/nYcK+hFhHvB6h3YKE2ceHlvZpPS/24iNwmIkeJiBS9ot5ZqE1ceNlZlk+o9wGm4UbrfFlEfiQiXuapsp1kJmai2VKrc6+qfgk3yshk4AkReVBEDip6hVsbje0kM/ExJpXOVJZ6o70elSUiO+NmITgFWA58A5iN2wlwGzCqiPV1ZHNkmTipBHYDlpZyo/kcavkv4CbgGFVdnHP/UyJyfXHK6paF2sTNCEoc6nw+U1+sqj/IDbSInACgqlcWrbKuDS/x9ozZUSV/z+YT6nQX911U6ELyZC21iZuSv2e77X6LyGeBo4CRIvLLnIcGAy3FLqwb1lKbuCn5e7anz9RLgKdwJ3zPy7m/CbigmEX1wFpqEzfRaalVdQGwQERuVlVfLXNH1lKbuIlOSy0iM1T1i8AzItJpdEJV9XEQyO4etmnMjohOqIHzwp8TS1FIngb6LsCYbVTyM7V66n63f7f2BWCGqr5ZmpK6lkpnbPgiE0fVpd5gPl9pDQbuEZGHROQcERlW7KK6YaE2cVTy920+x35fpqpjgXNwe/IeFJH7il5ZZxZqE0clf99uywbfApYBK3DHs5bU033P0CGsWVnq7RqzI9qQ1bCqpNvM54SOs4BJwK7ATOB0VX2u2IV1NFSaWoGoDdhgTI8q0Gypt5lPS70XcL6qzi9yLb2JynflxmyLkr9vu/1MLSKDw6s/Bl4XkaG5l9KUlyPIWqhNHJX8fdtTS30L7jvqeYACuUMZKbB3Eevqznqgn4ftGrO91pd6gz19Tz0x/FnKQRB6sxz3ccCYuFhW6g3mM5ro/fncVyIlPdncmAIo+Xu2p2O/++EOcdtFRHZiS/d7MP7OllriabvGbK+Sv2d7+kx9BnA+LsDz2BLq1cCvi1tWt6ylNnETnZZaVa8GrhaRb6jqNSWsqSfWUpu4Kfl7Np9jv9tEZEj7DRHZSUTOLl5JPbKW2sRNyd+z+YT6dFVtbL+hqquA04tWUc8s1CZuIhnqitzpdkSkEuhTvJJ6ZN1vEydKlD5T5/g7MCMc41uBM4G/FbWq7r0MtOIGSTcm6l4lyG4q9UbzaakvBO4HzsKdfrkQ6F/MoroVZNcCz3vZtjHbbl7vTym8fM6nbgMeA17FzeJ3BH6D9ZTHbRuzLbyEuqeDT/YBTgS+hDuHejqAqh5emtK6NQ/4qucajMmHlwaop8/ULwAPAZ9T1ZcBRMTXeN+5vPz3M2Y7PO1joz11v4/DHYz+gIjcICJHsPWZWr4swO0sMybKXiHINvrYcLehVtU7VHUSMAaYi5uVY5iIXCciR5aovs5sZ5mJB289ynx2lDWr6s3hqZh7APPpetK8UrKdZSbqohvqXKq6UlV/o6qfKFZBeXrU8/aN6Y239+g2hTpCMrgDYYyJoneAf/naeDxDHWSXYHvBTXTdTZD1tjM3nqF2ZvsuwJhueH1vxjnUf/VdgDFd2IA7X8Kb+IY6yM4HXvddhjEdzCXIrvFZQHxD7VhrbaLG+8fCuIfa+wtoTAfe35NxD/VcwCbNM1HxBEF2se8i4h3qILsR+IPvMowJ/cZ3ARD3UDvXYQeiGP9WAbf6LgKSEOog+wpwj+8yTNm7kSC7zncRkIRQO9f6LsCUNcX1GCMhKaG+C/iv7yJM2bqPIPuS7yLaJSPUQbYNmOa7DFO2ItVTTEaond8CG30XYcrOG0TsIKjkhDrIvgX82XcZpuxc7fOMrK4kJ9ROgDug3phSWIy/GWC7laxQB9k3iOCLbBIrIMiu911ER8kKtfMj3BzaxhTT88CNvovoSvJCHWRXAD/xXYZJvIuj9lm6XfJC7VyFG7PcmGJ4nCB7u+8iupPMUAfZZuAHvsswieV7iOweJTPUzg24qW+NKaQ5BNm5vovoSXJD7eYFnoqdwWUKpxk423cRvUluqAGC7ANE6EB7E3sXEmRf811Eb5Idauc7QOT/ECbyHiBix3h3R1TLoHca1B4O3E80Zu0sutQvmhjUV6gUqKqAp6bWsHKdMmnmWhoaldQQYcbxA9ipf+eXY87LLZw3Zz2tbcppH+pD+mN9AfJePqGagffHoZWG8mipy7Ib/sDkAcw/s4anptYAUP/wBo4YVcVL36jhiFFV1D/c+Wja1jblnLvX8beTB/DcOTXc+uwmnnu7Ne/lEywW3e525RFqp6y74bNebGHy+GoAJo+v5s4XWzo954k3Wxk9tIK9d6qgT6Vw4thqZr3QkvfyCRWbbne78gm1++56CmWwN1wEjrxpLftPW8O0ee5s1OVr2hg+yP25hw+q4K3mtk7Lvdmk7Dl4y1tij8HCm01teS+fQGuArxFkY/WeqfJdQEkF2bkEtZeQ8ANTHpkykBFh8D5101rG7JLf/+6udq+UzafmzhSYHKdud7vyaanbBdkrgBm+yyimEWGLutvACo4dU8UTb7YyrKaCpWGru7Spjd0Gdv7T7zFYeGP1lhZ48WrdvK58lk+Yy6N8KGhPEv+X6capwNO+iyiG5o1K0wbdfP2eV1oZt1slR+9TxR8XbALgjws28fn3du6kTRhZyUsr2nhtVRsbW5X/W7SJo8Pn5bN8gvwFuMx3EdurPL7S6kpQuyfwJDDMdymF9OqqNo6dvhaAljY4aVw13zu0LyvWtvHFmet4Pau8q1a47YQBDO0vLGlq47TZ67n75AEA3P3SJs6fs4FWVabs14fvHeq+0upu+QRaAHw03AcTS+UbaoCg9qPAP4A+vksxkfA2MIEgG+uRacu1++0E2UeAs3yXYSJhE3B83AMN5R5qgCD7e+Bnvssw3p1FkP2n7yIKwUINEGS/DVzvuwzjzfkE2d/5LqJQLNRbnE1Ex5wyRZUmyF7tu4hCslC3c0cNfQ24xXcppmQuJche6buIQrNQ53LT95yCtdjl4CKC7OW+iygGC3VHLthTiMgE4qYoLiDI1vsuoljK+3vq3gS1PwW+5bsMUzCtwDkE2UT/w7ZQ9yaonYI7F9sOUIm3lcAkgux9vgspNgt1PoLag4HbSdghpWXkOeDzBNmyGF3WPlPnI8g+CkwgoSeBJNxdwEfKJdBgoc6fm3zvY8B036WYvNXjWugm34WUknW/t0dQ+z3cQAuJPE0pAdbhRiy51XchPliot1dQexjwe2CU50rM1p4ETiXILvJdiC/W/d5ebuqVD+AGpbP/jP5tAL4LHFTOgQZrqQvDjSv+O6zV9qXsW+dc1lIXghtX3Frt0rPWuQvWUheaa7WnAaN9l5JwjwGnWZg7s1AXQ1BbDZwOfB/Y3XM1SfMicDFBdqbvQqLKQl1MQe1A4Hzc7CCD/RYTe2/iRvj8PUG21XcxUWahLoWgdmfgIuAcoJ/nauJmFe4gkmsIsut8FxMHFupScsMSX4o7Z9tOEOnZatyOxysJso2ea4kVC7UPQe1uuFFWzgD28lxN1CzEnRX3Z4LsGt/FxJGF2qegtgKow42P9mnK97DTjbhZMa4lyD7su5i4s1BHRVD7buBM3JRAO3uuplRex40w81uC7Fu+i0kKC3XUuK/DDgOOBj5H8rrni4DZ4eXxuE0TGwcW6qgLasezJeAHEL8uegvwEO1BDrKveq4n8SzUcRLUDgcmAgcD+wPvAyq91tTZBtzOrqdwYf6b7b0uLQt1nAW1/YHxuIAfQOmDnhvgeeFlEUF2U4m2b7pgoU4aF/TRwAhgeHgZ0eHn7vR8EIwCa4Gl4WVJFz+XAK9YgKPHQl3OgtpKoAqoxgW5BWixwzDjzUJtTMLY+dTGJIyF2piEsVAbkzAW6hISkUcLvL6UiJyUc/sAEfllIbdh4sd2lMWYiBwGfFtVJ3ouxUSItdQlJCJrwp+HichcEZkpIi+IyM0iIuFjl4jIkyLyrIhMy7l/tIjcJyILRORpEXk3bvCAQ0RkvohcEK73LhGpEJEGERmSs+2XRWSYiOwqIn8Jt/GkiHzUw0thishC7c8HcUMdvQ/YG2gP169UdYKqjgP64w4LBbgZ+LWqjscdJroUSAMPqep+qnpV+4pVtQ2YBRwLICIHAg2quhy4GrhKVScAxwG/LepvaUrOQu3PE6q6OAzgfCAV3n+4iDwuIv8GPgGMFZFBwEhVvQNAVder6tpe1j8dmBReP5Etc4B9EviViMzHnWQxOFy/SYgq3wWUsQ0511uBKhHphxvC5wBVfUNEAtzhnNtzZta/gNEisitwDHBFeH8FcJCq2nhfCWUtdbS0H4/9jojUAMcDqOpqYLGIHAMgIn1FZADQBHTZyqrbA3oH8HPgeVVdET50D/D19ueJyH6F/zWMTxbqCFHVRuAG4N/AnbjpZNqdApwrIguBR3EnZSwEWsKdZxd0scrpwJfZevrdc4EDRGShiDyHG23FJIh9pWVMwlhLbUzCWKiNSRgLtTEJY6E2JmEs1MYkjIXamISxUBuTMBZqYxLGQm1MwliojUkYC7UxCWOhNiZhLNTGJIyF2piEsVAbkzD/D4xEc9hdxfvoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "rus = RandomUnderSampler(sampling_strategy=1) # Numerical value\n",
    "# rus = RandomUnderSampler(sampling_strategy=\"not minority\") # String\n",
    "X_train_rus, y_train_rus = rus.fit_resample(X_train, y_train)\n",
    "\n",
    "ax = y_train_rus.value_counts().plot.pie(autopct='%.2f')\n",
    "_ = ax.set_title(\"Under-sampling\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WDfajzxR_GDa",
    "outputId": "6901a050-40ef-4c5d-c68e-594874d5ffd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "active      132\n",
       "inactive    132\n",
       "Name: Activity, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Class distribution\n",
    "y_train_rus.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cmkd6twKDCTP"
   },
   "source": [
    "### **Random Oversampling**\n",
    "\n",
    "``\"not majority\"`` = resample all classes but the majority class\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "8r6dnTdcDSds",
    "outputId": "78b2b3f5-6881-4f17-fbf7-962d538a804d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "ros = RandomOverSampler(sampling_strategy=1) # Float\n",
    "ros = RandomOverSampler(sampling_strategy=\"not majority\") # String\n",
    "X_train_ros, y_train_ros = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "ax = y_train_ros.value_counts().plot.pie(autopct='%.2f')\n",
    "_ = ax.set_title(\"Over-sampling\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jfguxlUEDbow",
    "outputId": "7524ecc0-a46e-40bb-d73c-2d474f38d26c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "active      330\n",
       "inactive    330\n",
       "Name: Activity, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_ros.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-qeS6soPWviH"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GvUYSd-IWuhq"
   },
   "source": [
    "# **F. Model building with/without class balancing**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s7cuwLaQhyEz"
   },
   "source": [
    "### **1. Model building with undersampled balanced class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Y-3k6VeZEpn_"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "49sNE8tTEs_B",
    "outputId": "d4058c67-abdb-4b6a-c63b-9f2ac0b2d9ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=42)\n",
    "model.fit(X_train_rus, y_train_rus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zXiKxunVE9uo"
   },
   "outputs": [],
   "source": [
    "# Apply model to make prediction\n",
    "from sklearn.metrics import matthews_corrcoef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fMtMGu50FCAo"
   },
   "outputs": [],
   "source": [
    "y_test_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s_xY1xi1FFsK"
   },
   "outputs": [],
   "source": [
    "mcc_test = matthews_corrcoef(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "mABfru0zFLdx",
    "outputId": "23978c08-e842-4a3d-fd34-85429509d127"
   },
   "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",
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       "    }\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>Performance_metric_names</th>\n",
       "      <th>Performance_metric_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MCC_test</td>\n",
       "      <td>0.712435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Performance_metric_names  Performance_metric_values\n",
       "0                 MCC_test                   0.712435"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display model performance results\n",
    "df_labels = pd.Series(['MCC_test'], name = 'Performance_metric_names')\n",
    "df_values = pd.Series([mcc_test], name = 'Performance_metric_values')\n",
    "df2 = pd.concat([df_labels, df_values], axis=1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QkcLwvTVlLXV"
   },
   "source": [
    "### **2. Model building with oversampled balanced class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p8BaEINYK7yd"
   },
   "outputs": [],
   "source": [
    "# Training Random Forest Models\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Vv1ApqeBK-EI",
    "outputId": "33a0912c-b971-4414-9fc5-5ea45f19663c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=42)\n",
    "model.fit(X_train_ros, y_train_ros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tgSWKPf3LBH0"
   },
   "outputs": [],
   "source": [
    "# Apply model to make prediction\n",
    "from sklearn.metrics import matthews_corrcoef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CeqIWdvsLFpn"
   },
   "outputs": [],
   "source": [
    "y_test_pred = model.predict(X_test)\n",
    "mcc_test = matthews_corrcoef(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "2yyvwVkSLhqR",
    "outputId": "dbe44831-5f37-4055-de40-f58f1eace75d"
   },
   "outputs": [
    {
     "data": {
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       "    }\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>Performance_metric_names</th>\n",
       "      <th>Performance_metric_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MCC_test</td>\n",
       "      <td>0.744225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Performance_metric_names  Performance_metric_values\n",
       "0                 MCC_test                   0.744225"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display model performance results\n",
    "df_labels = pd.Series(['MCC_test'], name = 'Performance_metric_names')\n",
    "df_values = pd.Series([mcc_test], name = 'Performance_metric_values')\n",
    "df3 = pd.concat([df_labels, df_values], axis=1)\n",
    "df3"
   ]
  }
 ],
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