P450_2d6 screen.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8811d6bd",
   "metadata": {},
   "source": [
    "# **<font color='red'>Define the project</font>**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "83ac455c",
   "metadata": {},
   "source": [
    "### Problem\n",
    "\n",
    "1. Cancer is the second leading cause of death in the USA. \n",
    "2. Cancers can originate from different organs and exhibit various subtypes.\n",
    "3. It is vital to constantly develop new biomarkers for cancer diagnosis and drugs for cancer treatment."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "67a3d540",
   "metadata": {},
   "source": [
    "### Proposal\n",
    "\n",
    "To address the issues described above, we plan to use machine learning aided strategy to speed up drug screen and optimization. By this strategy, we will have benefits as described below:\n",
    "\n",
    "1. It can greatly speed up the process on drug discovery and optimization.   \n",
    "2. It can greatly decrease the cost.   \n",
    "3. Model generated can be used to predict activity of new chemicals.   \n",
    "4. Insights from studing important features of the model can direct drug optimization.  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "81bf2a09",
   "metadata": {},
   "source": [
    "### Solution\n",
    "\n",
    "To acheive the goal, we will execute the project in 3 steps:\n",
    "\n",
    "1. Step 1: We will use unsupervised Butina clustering to generate smaller diversity enriched chemical library for drug screen (not covered in this notebook).\n",
    "2. Step 2: Develop a quantitative structure-activity relationships (QSAR) based Machine learning Classifier to predict new chemical's activity based on their chemical fingerprints using data from experimental drug screen. \n",
    "3. Step 3: Extract structural insights from studying the important features of the Classifier. Top features represent chemical moiety important for drug activity. They can be incorporated to drug optimization"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4237649f",
   "metadata": {},
   "source": [
    "### Process\n",
    "\n",
    "We are goind to process data and build a machine learning model as outlined below: \n",
    "\n",
    "1. Data cleaning\n",
    "2. EDA  \n",
    "3. Feature engineering\n",
    "4. Modeling selection\n",
    "5. Final model \n",
    "6. Model interpretation (feature importance and insights)\n",
    "7. Model deployment (optional)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8811d6bd",
   "metadata": {},
   "source": [
    "# **<font color='red'>Project Management </font>**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e538aa88",
   "metadata": {},
   "source": [
    "### 1. Data import and cleaning"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "db748977",
   "metadata": {},
   "source": [
    "First we need to import libraries used in the data analysis and model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fbd679e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pubchempy as pcp\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c63a8b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import PandasTools\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import DataStructs\n",
    "from rdkit.Chem import RDConfig\n",
    "from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect\n",
    "from rdkit.DataStructs.cDataStructs import ConvertToNumpyArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8f993f4f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pubchem_SID</th>\n",
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       "      <td>CC1CC2=CC=CC=C2N1C(=O)CN3CCC(CC3)N4C5=C(C=C(C=...</td>\n",
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       "      <td>C=CC1=C[C@H]([C@H]2[C@@H](C13OCCCO3)O2)O</td>\n",
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       "      <th>13908</th>\n",
       "      <td>26751440</td>\n",
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       "      <td>C[C@H](C1=CC=CC=C1)N2C(=O)[C@@H]3CC[C@H]4[C@H]...</td>\n",
       "      <td>Inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13909</th>\n",
       "      <td>26751441</td>\n",
       "      <td>10847630.00</td>\n",
       "      <td>COC(=O)NC/C=C\\C1=NC(=CO1)CCCO</td>\n",
       "      <td>Inactive</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>13910 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "       Pubchem_SID  Pubchem_CID  \\\n",
       "0           842618    644890.00   \n",
       "1           842953   6602688.00   \n",
       "2           843526    645818.00   \n",
       "3           843662    645965.00   \n",
       "4           844085    646395.00   \n",
       "...            ...          ...   \n",
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       "\n",
       "                                                  SMILES Activity_Outcome  \n",
       "0      CC(C)(C)N1C(=NN=N1)C(C2=CC=CC=C2OC)N3CCN(CC3)C...           Active  \n",
       "1      C1COCCN1CCN2C3=CC=CC=C3N(C2=N)CC(=O)C4=CC(=C(C...           Active  \n",
       "2               COC1=CC=CC2=C1NC=C(C2=O)C(=O)NCC3=CC=CS3           Active  \n",
       "3      CC1CC2=CC=CC=C2N1C(=O)CN3CCC(CC3)N4C5=C(C=C(C=...           Active  \n",
       "4                    CCSC1=C(C2(CCCCC2)C(=C(N1)N)C#N)C#N           Active  \n",
       "...                                                  ...              ...  \n",
       "13905           C=CC1=C[C@H]([C@H]2[C@@H](C13OCCCO3)O2)O         Inactive  \n",
       "13906  C1COC2([C@@H]3[C@H](O3)[C@H]([C@@H]4C2=CC[C@H]...         Inactive  \n",
       "13907  CCN1C(=O)[C@H]2CC=C3[C@H]([C@H]2C1=O)[C@@H]([C...         Inactive  \n",
       "13908  C[C@H](C1=CC=CC=C1)N2C(=O)[C@@H]3CC[C@H]4[C@H]...         Inactive  \n",
       "13909                      COC(=O)NC/C=C\\C1=NC(=CO1)CCCO         Inactive  \n",
       "\n",
       "[13910 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('P450_2d6_binary.csv')\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "19aad027",
   "metadata": {},
   "source": [
    "We need to check if there are missing values in the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "188b7adf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pubchem_SID          0\n",
       "Pubchem_CID         14\n",
       "SMILES              14\n",
       "Activity_Outcome     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "025efa86",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pubchem_SID         0\n",
       "Pubchem_CID         0\n",
       "SMILES              0\n",
       "Activity_Outcome    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.isna().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "20fc3edb",
   "metadata": {},
   "source": [
    "### 2. EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "aafa1963",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>C=CC1=C[C@H]([C@H]2[C@@H](C13OCCCO3)O2)O</td>\n",
       "      <td>Inactive</td>\n",
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       "      <td>26751439</td>\n",
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       "      <td>CCN1C(=O)[C@H]2CC=C3[C@H]([C@H]2C1=O)[C@@H]([C...</td>\n",
       "      <td>Inactive</td>\n",
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       "      <th>13908</th>\n",
       "      <td>26751440</td>\n",
       "      <td>16758818.00</td>\n",
       "      <td>C[C@H](C1=CC=CC=C1)N2C(=O)[C@@H]3CC[C@H]4[C@H]...</td>\n",
       "      <td>Inactive</td>\n",
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       "      <th>13909</th>\n",
       "      <td>26751441</td>\n",
       "      <td>10847630.00</td>\n",
       "      <td>COC(=O)NC/C=C\\C1=NC(=CO1)CCCO</td>\n",
       "      <td>Inactive</td>\n",
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      ],
      "text/plain": [
       "       Pubchem_SID  Pubchem_CID  \\\n",
       "0           842618    644890.00   \n",
       "1           842953   6602688.00   \n",
       "2           843526    645818.00   \n",
       "3           843662    645965.00   \n",
       "4           844085    646395.00   \n",
       "...            ...          ...   \n",
       "13905     26751437  16758815.00   \n",
       "13906     26751438  16758816.00   \n",
       "13907     26751439  16758817.00   \n",
       "13908     26751440  16758818.00   \n",
       "13909     26751441  10847630.00   \n",
       "\n",
       "                                                  SMILES Activity_Outcome  \n",
       "0      CC(C)(C)N1C(=NN=N1)C(C2=CC=CC=C2OC)N3CCN(CC3)C...           Active  \n",
       "1      C1COCCN1CCN2C3=CC=CC=C3N(C2=N)CC(=O)C4=CC(=C(C...           Active  \n",
       "2               COC1=CC=CC2=C1NC=C(C2=O)C(=O)NCC3=CC=CS3           Active  \n",
       "3      CC1CC2=CC=CC=C2N1C(=O)CN3CCC(CC3)N4C5=C(C=C(C=...           Active  \n",
       "4                    CCSC1=C(C2(CCCCC2)C(=C(N1)N)C#N)C#N           Active  \n",
       "...                                                  ...              ...  \n",
       "13905           C=CC1=C[C@H]([C@H]2[C@@H](C13OCCCO3)O2)O         Inactive  \n",
       "13906  C1COC2([C@@H]3[C@H](O3)[C@H]([C@@H]4C2=CC[C@H]...         Inactive  \n",
       "13907  CCN1C(=O)[C@H]2CC=C3[C@H]([C@H]2C1=O)[C@@H]([C...         Inactive  \n",
       "13908  C[C@H](C1=CC=CC=C1)N2C(=O)[C@@H]3CC[C@H]4[C@H]...         Inactive  \n",
       "13909                      COC(=O)NC/C=C\\C1=NC(=CO1)CCCO         Inactive  \n",
       "\n",
       "[13910 rows x 4 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5715252e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active chemicals:  2769\n",
      "Inactive chemicals:  11127\n",
      "Ratio:  4.018418201516793\n"
     ]
    }
   ],
   "source": [
    "count_1 = len(df[df.Activity_Outcome=='Active'])\n",
    "count_0 = len(df[df.Activity_Outcome=='Inactive'])\n",
    "\n",
    "print('Active chemicals: ',count_1)\n",
    "print('Inactive chemicals: ',count_0)\n",
    "print('Ratio: ', count_0/count_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a4906505",
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_dict = {\n",
    "            'Inactive':0,\n",
    "            'Active':1\n",
    "            }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea6e9052",
   "metadata": {},
   "source": [
    "# How many bits of fingerprint to use?\n",
    "- need to try 1024 vs 2048\n",
    "- From online example code, radius=2, num_bits=2048"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0e3188db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13896, 2048)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_name = [f'Bit_{i}' for i in range(2048)]\n",
    "fp_cmpd = []\n",
    "for smiles in df.SMILES.tolist():\n",
    "    cmpd = Chem.MolFromSmiles(smiles)\n",
    "    fp = AllChem.GetMorganFingerprintAsBitVect(cmpd,2, nBits=2048)\n",
    "    binary = fp.ToBitString()\n",
    "    fp_cmpd.append(list(binary))\n",
    "df_fp = pd.DataFrame(fp_cmpd, index = df.SMILES, columns=fp_name)\n",
    "df_fp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a8f0875",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['SMILES']\n",
    "y = df['Activity_Outcome'].map(prop_dict)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train_raw, X_test_raw, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=0.3, \n",
    "                                                    random_state=42,\n",
    "                                                    stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4fe12bbf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>Bit_0</th>\n",
       "      <th>Bit_1</th>\n",
       "      <th>Bit_2</th>\n",
       "      <th>Bit_3</th>\n",
       "      <th>Bit_4</th>\n",
       "      <th>Bit_5</th>\n",
       "      <th>Bit_6</th>\n",
       "      <th>Bit_7</th>\n",
       "      <th>Bit_8</th>\n",
       "      <th>Bit_9</th>\n",
       "      <th>...</th>\n",
       "      <th>Bit_2038</th>\n",
       "      <th>Bit_2039</th>\n",
       "      <th>Bit_2040</th>\n",
       "      <th>Bit_2041</th>\n",
       "      <th>Bit_2042</th>\n",
       "      <th>Bit_2043</th>\n",
       "      <th>Bit_2044</th>\n",
       "      <th>Bit_2045</th>\n",
       "      <th>Bit_2046</th>\n",
       "      <th>Bit_2047</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMILES</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(CCC(=O)O)CCN</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>CN(C)CC(CCCCC(CN(C)C)C(=O)C1=CC=C(C=C1)OC)C(=O)C2=CC=C(C=C2)OC.Cl</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC1=C(C=C(C=C1)NS(=O)(=O)C2=C(C=CC(=C2)C(=O)NCC3=CC=CC=N3)Cl)C</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>CCOC1=CC(=C(C=C1)C2=C[N+](=O)C3=C(N2[O-])CCCC3)OCC</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN1[C@@H]2CC[C@H]1CC(C2)OC(C3=CC=CC=C3)C4=CC=CC=C4.CS(=O)(=O)O</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC(=O)O[C@@H]1C[C@]2([C@H](CCC2=O)C3=C1[C@]4([C@H](OC(=O)C5=COC(=C54)C3=O)COC)C)C</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COC1=CC=C(C=C1)C(=O)NC2=C(C(=CS2)C3=C(C=C(C=C3)Cl)Cl)C(=O)N</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCOC(=O)N1CCC(CC1)NC(=O)CCC2=NC(=NO2)C3=CCN(CC3)C</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1COCCN1C(=S)SCCN2C(=O)C3=CC=CC=C3C2=O</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC(C)CN1CC[C@]2(C1)CCCN(C2)C(=O)C3=CC=CO3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9727 rows × 2048 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Bit_0  Bit_1  Bit_2  \\\n",
       "SMILES                                                                    \n",
       "C(CCC(=O)O)CCN                                          0      0      0   \n",
       "CN(C)CC(CCCCC(CN(C)C)C(=O)C1=CC=C(C=C1)OC)C(=O)...      0      1      0   \n",
       "CC1=C(C=C(C=C1)NS(=O)(=O)C2=C(C=CC(=C2)C(=O)NCC...      0      0      0   \n",
       "CCOC1=CC(=C(C=C1)C2=C[N+](=O)C3=C(N2[O-])CCCC3)OCC      0      0      0   \n",
       "CN1[C@@H]2CC[C@H]1CC(C2)OC(C3=CC=CC=C3)C4=CC=CC...      0      1      0   \n",
       "...                                                   ...    ...    ...   \n",
       "CC(=O)O[C@@H]1C[C@]2([C@H](CCC2=O)C3=C1[C@]4([C...      0      0      0   \n",
       "COC1=CC=C(C=C1)C(=O)NC2=C(C(=CS2)C3=C(C=C(C=C3)...      0      1      0   \n",
       "CCOC(=O)N1CCC(CC1)NC(=O)CCC2=NC(=NO2)C3=CCN(CC3)C       0      0      0   \n",
       "C1COCCN1C(=S)SCCN2C(=O)C3=CC=CC=C3C2=O                  0      0      0   \n",
       "CC(C)CN1CC[C@]2(C1)CCCN(C2)C(=O)C3=CC=CO3               0      1      0   \n",
       "\n",
       "                                                    Bit_3  Bit_4  Bit_5  \\\n",
       "SMILES                                                                    \n",
       "C(CCC(=O)O)CCN                                          0      0      0   \n",
       "CN(C)CC(CCCCC(CN(C)C)C(=O)C1=CC=C(C=C1)OC)C(=O)...      0      0      0   \n",
       "CC1=C(C=C(C=C1)NS(=O)(=O)C2=C(C=CC(=C2)C(=O)NCC...      0      0      0   \n",
       "CCOC1=CC(=C(C=C1)C2=C[N+](=O)C3=C(N2[O-])CCCC3)OCC      0      0      0   \n",
       "CN1[C@@H]2CC[C@H]1CC(C2)OC(C3=CC=CC=C3)C4=CC=CC...      0      0      0   \n",
       "...                                                   ...    ...    ...   \n",
       "CC(=O)O[C@@H]1C[C@]2([C@H](CCC2=O)C3=C1[C@]4([C...      0      0      0   \n",
       "COC1=CC=C(C=C1)C(=O)NC2=C(C(=CS2)C3=C(C=C(C=C3)...      0      0      0   \n",
       "CCOC(=O)N1CCC(CC1)NC(=O)CCC2=NC(=NO2)C3=CCN(CC3)C       0      0      0   \n",
       "C1COCCN1C(=S)SCCN2C(=O)C3=CC=CC=C3C2=O                  0      0      0   \n",
       "CC(C)CN1CC[C@]2(C1)CCCN(C2)C(=O)C3=CC=CO3               0      0      0   \n",
       "\n",
       "                                                    Bit_6  Bit_7  Bit_8  \\\n",
       "SMILES                                                                    \n",
       "C(CCC(=O)O)CCN                                          0      0      0   \n",
       "CN(C)CC(CCCCC(CN(C)C)C(=O)C1=CC=C(C=C1)OC)C(=O)...      0      0      0   \n",
       "CC1=C(C=C(C=C1)NS(=O)(=O)C2=C(C=CC(=C2)C(=O)NCC...      0      0      0   \n",
       "CCOC1=CC(=C(C=C1)C2=C[N+](=O)C3=C(N2[O-])CCCC3)OCC      0      0      0   \n",
       "CN1[C@@H]2CC[C@H]1CC(C2)OC(C3=CC=CC=C3)C4=CC=CC...      0      0      0   \n",
       "...                                                   ...    ...    ...   \n",
       "CC(=O)O[C@@H]1C[C@]2([C@H](CCC2=O)C3=C1[C@]4([C...      0      0      0   \n",
       "COC1=CC=C(C=C1)C(=O)NC2=C(C(=CS2)C3=C(C=C(C=C3)...      0      0      0   \n",
       "CCOC(=O)N1CCC(CC1)NC(=O)CCC2=NC(=NO2)C3=CCN(CC3)C       0      0      0   \n",
       "C1COCCN1C(=S)SCCN2C(=O)C3=CC=CC=C3C2=O                  0      0      0   \n",
       "CC(C)CN1CC[C@]2(C1)CCCN(C2)C(=O)C3=CC=CO3               0      0      0   \n",
       "\n",
       "                                                    Bit_9  ...  Bit_2038  \\\n",
       "SMILES                                                     ...             \n",
       "C(CCC(=O)O)CCN                                          0  ...         0   \n",
       "CN(C)CC(CCCCC(CN(C)C)C(=O)C1=CC=C(C=C1)OC)C(=O)...      0  ...         0   \n",
       "CC1=C(C=C(C=C1)NS(=O)(=O)C2=C(C=CC(=C2)C(=O)NCC...      0  ...         0   \n",
       "CCOC1=CC(=C(C=C1)C2=C[N+](=O)C3=C(N2[O-])CCCC3)OCC      0  ...         0   \n",
       "CN1[C@@H]2CC[C@H]1CC(C2)OC(C3=CC=CC=C3)C4=CC=CC...      0  ...         0   \n",
       "...                                                   ...  ...       ...   \n",
       "CC(=O)O[C@@H]1C[C@]2([C@H](CCC2=O)C3=C1[C@]4([C...      0  ...         0   \n",
       "COC1=CC=C(C=C1)C(=O)NC2=C(C(=CS2)C3=C(C=C(C=C3)...      0  ...         0   \n",
       "CCOC(=O)N1CCC(CC1)NC(=O)CCC2=NC(=NO2)C3=CCN(CC3)C       0  ...         0   \n",
       "C1COCCN1C(=S)SCCN2C(=O)C3=CC=CC=C3C2=O                  0  ...         0   \n",
       "CC(C)CN1CC[C@]2(C1)CCCN(C2)C(=O)C3=CC=CO3               0  ...         0   \n",
       "\n",
       "                                                    Bit_2039  Bit_2040  \\\n",
       "SMILES                                                                   \n",
       "C(CCC(=O)O)CCN                                             0         0   \n",
       "CN(C)CC(CCCCC(CN(C)C)C(=O)C1=CC=C(C=C1)OC)C(=O)...         0         0   \n",
       "CC1=C(C=C(C=C1)NS(=O)(=O)C2=C(C=CC(=C2)C(=O)NCC...         0         0   \n",
       "CCOC1=CC(=C(C=C1)C2=C[N+](=O)C3=C(N2[O-])CCCC3)OCC         0         0   \n",
       "CN1[C@@H]2CC[C@H]1CC(C2)OC(C3=CC=CC=C3)C4=CC=CC...         0         0   \n",
       "...                                                      ...       ...   \n",
       "CC(=O)O[C@@H]1C[C@]2([C@H](CCC2=O)C3=C1[C@]4([C...         0         0   \n",
       "COC1=CC=C(C=C1)C(=O)NC2=C(C(=CS2)C3=C(C=C(C=C3)...         0         0   \n",
       "CCOC(=O)N1CCC(CC1)NC(=O)CCC2=NC(=NO2)C3=CCN(CC3)C          0         0   \n",
       "C1COCCN1C(=S)SCCN2C(=O)C3=CC=CC=C3C2=O                     0         0   \n",
       "CC(C)CN1CC[C@]2(C1)CCCN(C2)C(=O)C3=CC=CO3                  0         0   \n",
       "\n",
       "                                                    Bit_2041  Bit_2042  \\\n",
       "SMILES                                                                   \n",
       "C(CCC(=O)O)CCN                                             0         0   \n",
       "CN(C)CC(CCCCC(CN(C)C)C(=O)C1=CC=C(C=C1)OC)C(=O)...         0         0   \n",
       "CC1=C(C=C(C=C1)NS(=O)(=O)C2=C(C=CC(=C2)C(=O)NCC...         0         0   \n",
       "CCOC1=CC(=C(C=C1)C2=C[N+](=O)C3=C(N2[O-])CCCC3)OCC         0         0   \n",
       "CN1[C@@H]2CC[C@H]1CC(C2)OC(C3=CC=CC=C3)C4=CC=CC...         0         0   \n",
       "...                                                      ...       ...   \n",
       "CC(=O)O[C@@H]1C[C@]2([C@H](CCC2=O)C3=C1[C@]4([C...         0         0   \n",
       "COC1=CC=C(C=C1)C(=O)NC2=C(C(=CS2)C3=C(C=C(C=C3)...         0         0   \n",
       "CCOC(=O)N1CCC(CC1)NC(=O)CCC2=NC(=NO2)C3=CCN(CC3)C          0         0   \n",
       "C1COCCN1C(=S)SCCN2C(=O)C3=CC=CC=C3C2=O                     0         0   \n",
       "CC(C)CN1CC[C@]2(C1)CCCN(C2)C(=O)C3=CC=CO3                  0         0   \n",
       "\n",
       "                                                    Bit_2043  Bit_2044  \\\n",
       "SMILES                                                                   \n",
       "C(CCC(=O)O)CCN                                             0         0   \n",
       "CN(C)CC(CCCCC(CN(C)C)C(=O)C1=CC=C(C=C1)OC)C(=O)...         0         0   \n",
       "CC1=C(C=C(C=C1)NS(=O)(=O)C2=C(C=CC(=C2)C(=O)NCC...         0         0   \n",
       "CCOC1=CC(=C(C=C1)C2=C[N+](=O)C3=C(N2[O-])CCCC3)OCC         0         0   \n",
       "CN1[C@@H]2CC[C@H]1CC(C2)OC(C3=CC=CC=C3)C4=CC=CC...         0         0   \n",
       "...                                                      ...       ...   \n",
       "CC(=O)O[C@@H]1C[C@]2([C@H](CCC2=O)C3=C1[C@]4([C...         0         0   \n",
       "COC1=CC=C(C=C1)C(=O)NC2=C(C(=CS2)C3=C(C=C(C=C3)...         0         0   \n",
       "CCOC(=O)N1CCC(CC1)NC(=O)CCC2=NC(=NO2)C3=CCN(CC3)C          0         0   \n",
       "C1COCCN1C(=S)SCCN2C(=O)C3=CC=CC=C3C2=O                     0         0   \n",
       "CC(C)CN1CC[C@]2(C1)CCCN(C2)C(=O)C3=CC=CO3                  0         0   \n",
       "\n",
       "                                                    Bit_2045  Bit_2046  \\\n",
       "SMILES                                                                   \n",
       "C(CCC(=O)O)CCN                                             0         0   \n",
       "CN(C)CC(CCCCC(CN(C)C)C(=O)C1=CC=C(C=C1)OC)C(=O)...         0         0   \n",
       "CC1=C(C=C(C=C1)NS(=O)(=O)C2=C(C=CC(=C2)C(=O)NCC...         0         0   \n",
       "CCOC1=CC(=C(C=C1)C2=C[N+](=O)C3=C(N2[O-])CCCC3)OCC         0         0   \n",
       "CN1[C@@H]2CC[C@H]1CC(C2)OC(C3=CC=CC=C3)C4=CC=CC...         0         0   \n",
       "...                                                      ...       ...   \n",
       "CC(=O)O[C@@H]1C[C@]2([C@H](CCC2=O)C3=C1[C@]4([C...         0         0   \n",
       "COC1=CC=C(C=C1)C(=O)NC2=C(C(=CS2)C3=C(C=C(C=C3)...         0         0   \n",
       "CCOC(=O)N1CCC(CC1)NC(=O)CCC2=NC(=NO2)C3=CCN(CC3)C          0         0   \n",
       "C1COCCN1C(=S)SCCN2C(=O)C3=CC=CC=C3C2=O                     0         0   \n",
       "CC(C)CN1CC[C@]2(C1)CCCN(C2)C(=O)C3=CC=CO3                  0         0   \n",
       "\n",
       "                                                    Bit_2047  \n",
       "SMILES                                                        \n",
       "C(CCC(=O)O)CCN                                             0  \n",
       "CN(C)CC(CCCCC(CN(C)C)C(=O)C1=CC=C(C=C1)OC)C(=O)...         0  \n",
       "CC1=C(C=C(C=C1)NS(=O)(=O)C2=C(C=CC(=C2)C(=O)NCC...         0  \n",
       "CCOC1=CC(=C(C=C1)C2=C[N+](=O)C3=C(N2[O-])CCCC3)OCC         0  \n",
       "CN1[C@@H]2CC[C@H]1CC(C2)OC(C3=CC=CC=C3)C4=CC=CC...         0  \n",
       "...                                                      ...  \n",
       "CC(=O)O[C@@H]1C[C@]2([C@H](CCC2=O)C3=C1[C@]4([C...         0  \n",
       "COC1=CC=C(C=C1)C(=O)NC2=C(C(=CS2)C3=C(C=C(C=C3)...         0  \n",
       "CCOC(=O)N1CCC(CC1)NC(=O)CCC2=NC(=NO2)C3=CCN(CC3)C          0  \n",
       "C1COCCN1C(=S)SCCN2C(=O)C3=CC=CC=C3C2=O                     0  \n",
       "CC(C)CN1CC[C@]2(C1)CCCN(C2)C(=O)C3=CC=CO3                  0  \n",
       "\n",
       "[9727 rows x 2048 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_name = [f'Bit_{i}' for i in range(2048)]\n",
    "fp_cmpd = []\n",
    "for smiles in X_train_raw.tolist():\n",
    "    cmpd = Chem.MolFromSmiles(smiles)\n",
    "    fp = AllChem.GetMorganFingerprintAsBitVect(cmpd,2, nBits=2048)\n",
    "    binary = fp.ToBitString()\n",
    "    fp_cmpd.append(list(binary))\n",
    "X_train = pd.DataFrame(fp_cmpd, index = X_train_raw, columns=fp_name)\n",
    "X_train = X_train.astype(int)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "32fca226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train.Bit_0[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fcb330e9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bit_0</th>\n",
       "      <th>Bit_1</th>\n",
       "      <th>Bit_2</th>\n",
       "      <th>Bit_3</th>\n",
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       "      <th>Bit_2044</th>\n",
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       "      <th>Bit_2046</th>\n",
       "      <th>Bit_2047</th>\n",
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       "</table>\n",
       "<p>4169 rows × 2048 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Bit_0  Bit_1  Bit_2  \\\n",
       "SMILES                                                                    \n",
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       "...                                                   ...    ...    ...   \n",
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       "CN(C)C1=CC(=O)N2CCCSC2=N1                               0      0      0   \n",
       "\n",
       "                                                    Bit_3  Bit_4  Bit_5  \\\n",
       "SMILES                                                                    \n",
       "CC1=C(C=CC=C1Cl)N2C(C(C2=O)N3C(=O)C4=C(C3=O)C(=...      0      0      1   \n",
       "C1CC=C(C(=O)C1)[C@H](CCOC(C2=CC=CC=C2)(C3=CC=CC...      0      0      0   \n",
       "COC(=O)C1=CC(=CC(=C1)NC(=O)CC2C(=O)NC3CCCCC3N2)...      0      0      0   \n",
       "C[C@@H]/1COC(=O)[C@H](NC(=O)C/C=C\\[C@@H]([C@H](...      0      0      0   \n",
       "COCC(=O)N1CCC2(CC1)CN(C2)CC3=CC=C(C=C3)C#N              0      0      0   \n",
       "...                                                   ...    ...    ...   \n",
       "CC1=CC=C(C=C1)/C(=C/C2=CC(=CC=C2)[N+](=O)[O-])/C#N      0      0      0   \n",
       "C1=CC(=CC=C1CN2C3=NC=NC=C3N=C(C2=O)C4=CC(=C(C=C...      0      0      0   \n",
       "CCN(CC)CCCNC(=O)C1CCC(=O)N(C1C2=CC=C(C=C2)F)C3=...      0      0      0   \n",
       "CC(C)NC(=O)CN1CCC(CC1)(C2=CC(=CC=C2)F)NC(=O)C           0      0      0   \n",
       "CN(C)C1=CC(=O)N2CCCSC2=N1                               0      0      0   \n",
       "\n",
       "                                                    Bit_6  Bit_7  Bit_8  \\\n",
       "SMILES                                                                    \n",
       "CC1=C(C=CC=C1Cl)N2C(C(C2=O)N3C(=O)C4=C(C3=O)C(=...      0      0      0   \n",
       "C1CC=C(C(=O)C1)[C@H](CCOC(C2=CC=CC=C2)(C3=CC=CC...      0      0      0   \n",
       "COC(=O)C1=CC(=CC(=C1)NC(=O)CC2C(=O)NC3CCCCC3N2)...      0      0      0   \n",
       "C[C@@H]/1COC(=O)[C@H](NC(=O)C/C=C\\[C@@H]([C@H](...      0      0      0   \n",
       "COCC(=O)N1CCC2(CC1)CN(C2)CC3=CC=C(C=C3)C#N              0      0      0   \n",
       "...                                                   ...    ...    ...   \n",
       "CC1=CC=C(C=C1)/C(=C/C2=CC(=CC=C2)[N+](=O)[O-])/C#N      0      0      0   \n",
       "C1=CC(=CC=C1CN2C3=NC=NC=C3N=C(C2=O)C4=CC(=C(C=C...      0      0      0   \n",
       "CCN(CC)CCCNC(=O)C1CCC(=O)N(C1C2=CC=C(C=C2)F)C3=...      0      0      0   \n",
       "CC(C)NC(=O)CN1CCC(CC1)(C2=CC(=CC=C2)F)NC(=O)C           0      0      0   \n",
       "CN(C)C1=CC(=O)N2CCCSC2=N1                               0      0      0   \n",
       "\n",
       "                                                    Bit_9  ...  Bit_2038  \\\n",
       "SMILES                                                     ...             \n",
       "CC1=C(C=CC=C1Cl)N2C(C(C2=O)N3C(=O)C4=C(C3=O)C(=...      0  ...         0   \n",
       "C1CC=C(C(=O)C1)[C@H](CCOC(C2=CC=CC=C2)(C3=CC=CC...      0  ...         0   \n",
       "COC(=O)C1=CC(=CC(=C1)NC(=O)CC2C(=O)NC3CCCCC3N2)...      0  ...         0   \n",
       "C[C@@H]/1COC(=O)[C@H](NC(=O)C/C=C\\[C@@H]([C@H](...      0  ...         0   \n",
       "COCC(=O)N1CCC2(CC1)CN(C2)CC3=CC=C(C=C3)C#N              0  ...         0   \n",
       "...                                                   ...  ...       ...   \n",
       "CC1=CC=C(C=C1)/C(=C/C2=CC(=CC=C2)[N+](=O)[O-])/C#N      0  ...         0   \n",
       "C1=CC(=CC=C1CN2C3=NC=NC=C3N=C(C2=O)C4=CC(=C(C=C...      0  ...         0   \n",
       "CCN(CC)CCCNC(=O)C1CCC(=O)N(C1C2=CC=C(C=C2)F)C3=...      0  ...         0   \n",
       "CC(C)NC(=O)CN1CCC(CC1)(C2=CC(=CC=C2)F)NC(=O)C           0  ...         0   \n",
       "CN(C)C1=CC(=O)N2CCCSC2=N1                               0  ...         0   \n",
       "\n",
       "                                                    Bit_2039  Bit_2040  \\\n",
       "SMILES                                                                   \n",
       "CC1=C(C=CC=C1Cl)N2C(C(C2=O)N3C(=O)C4=C(C3=O)C(=...         0         0   \n",
       "C1CC=C(C(=O)C1)[C@H](CCOC(C2=CC=CC=C2)(C3=CC=CC...         0         0   \n",
       "COC(=O)C1=CC(=CC(=C1)NC(=O)CC2C(=O)NC3CCCCC3N2)...         0         0   \n",
       "C[C@@H]/1COC(=O)[C@H](NC(=O)C/C=C\\[C@@H]([C@H](...         0         0   \n",
       "COCC(=O)N1CCC2(CC1)CN(C2)CC3=CC=C(C=C3)C#N                 0         0   \n",
       "...                                                      ...       ...   \n",
       "CC1=CC=C(C=C1)/C(=C/C2=CC(=CC=C2)[N+](=O)[O-])/C#N         0         0   \n",
       "C1=CC(=CC=C1CN2C3=NC=NC=C3N=C(C2=O)C4=CC(=C(C=C...         0         0   \n",
       "CCN(CC)CCCNC(=O)C1CCC(=O)N(C1C2=CC=C(C=C2)F)C3=...         0         0   \n",
       "CC(C)NC(=O)CN1CCC(CC1)(C2=CC(=CC=C2)F)NC(=O)C              0         0   \n",
       "CN(C)C1=CC(=O)N2CCCSC2=N1                                  0         0   \n",
       "\n",
       "                                                    Bit_2041  Bit_2042  \\\n",
       "SMILES                                                                   \n",
       "CC1=C(C=CC=C1Cl)N2C(C(C2=O)N3C(=O)C4=C(C3=O)C(=...         0         0   \n",
       "C1CC=C(C(=O)C1)[C@H](CCOC(C2=CC=CC=C2)(C3=CC=CC...         0         0   \n",
       "COC(=O)C1=CC(=CC(=C1)NC(=O)CC2C(=O)NC3CCCCC3N2)...         0         0   \n",
       "C[C@@H]/1COC(=O)[C@H](NC(=O)C/C=C\\[C@@H]([C@H](...         0         0   \n",
       "COCC(=O)N1CCC2(CC1)CN(C2)CC3=CC=C(C=C3)C#N                 0         0   \n",
       "...                                                      ...       ...   \n",
       "CC1=CC=C(C=C1)/C(=C/C2=CC(=CC=C2)[N+](=O)[O-])/C#N         0         0   \n",
       "C1=CC(=CC=C1CN2C3=NC=NC=C3N=C(C2=O)C4=CC(=C(C=C...         0         0   \n",
       "CCN(CC)CCCNC(=O)C1CCC(=O)N(C1C2=CC=C(C=C2)F)C3=...         0         0   \n",
       "CC(C)NC(=O)CN1CCC(CC1)(C2=CC(=CC=C2)F)NC(=O)C              0         0   \n",
       "CN(C)C1=CC(=O)N2CCCSC2=N1                                  0         0   \n",
       "\n",
       "                                                    Bit_2043  Bit_2044  \\\n",
       "SMILES                                                                   \n",
       "CC1=C(C=CC=C1Cl)N2C(C(C2=O)N3C(=O)C4=C(C3=O)C(=...         0         0   \n",
       "C1CC=C(C(=O)C1)[C@H](CCOC(C2=CC=CC=C2)(C3=CC=CC...         0         0   \n",
       "COC(=O)C1=CC(=CC(=C1)NC(=O)CC2C(=O)NC3CCCCC3N2)...         0         0   \n",
       "C[C@@H]/1COC(=O)[C@H](NC(=O)C/C=C\\[C@@H]([C@H](...         0         0   \n",
       "COCC(=O)N1CCC2(CC1)CN(C2)CC3=CC=C(C=C3)C#N                 0         0   \n",
       "...                                                      ...       ...   \n",
       "CC1=CC=C(C=C1)/C(=C/C2=CC(=CC=C2)[N+](=O)[O-])/C#N         0         0   \n",
       "C1=CC(=CC=C1CN2C3=NC=NC=C3N=C(C2=O)C4=CC(=C(C=C...         0         0   \n",
       "CCN(CC)CCCNC(=O)C1CCC(=O)N(C1C2=CC=C(C=C2)F)C3=...         0         0   \n",
       "CC(C)NC(=O)CN1CCC(CC1)(C2=CC(=CC=C2)F)NC(=O)C              0         0   \n",
       "CN(C)C1=CC(=O)N2CCCSC2=N1                                  0         0   \n",
       "\n",
       "                                                    Bit_2045  Bit_2046  \\\n",
       "SMILES                                                                   \n",
       "CC1=C(C=CC=C1Cl)N2C(C(C2=O)N3C(=O)C4=C(C3=O)C(=...         0         0   \n",
       "C1CC=C(C(=O)C1)[C@H](CCOC(C2=CC=CC=C2)(C3=CC=CC...         0         0   \n",
       "COC(=O)C1=CC(=CC(=C1)NC(=O)CC2C(=O)NC3CCCCC3N2)...         0         0   \n",
       "C[C@@H]/1COC(=O)[C@H](NC(=O)C/C=C\\[C@@H]([C@H](...         0         0   \n",
       "COCC(=O)N1CCC2(CC1)CN(C2)CC3=CC=C(C=C3)C#N                 0         0   \n",
       "...                                                      ...       ...   \n",
       "CC1=CC=C(C=C1)/C(=C/C2=CC(=CC=C2)[N+](=O)[O-])/C#N         0         0   \n",
       "C1=CC(=CC=C1CN2C3=NC=NC=C3N=C(C2=O)C4=CC(=C(C=C...         0         0   \n",
       "CCN(CC)CCCNC(=O)C1CCC(=O)N(C1C2=CC=C(C=C2)F)C3=...         0         0   \n",
       "CC(C)NC(=O)CN1CCC(CC1)(C2=CC(=CC=C2)F)NC(=O)C              0         0   \n",
       "CN(C)C1=CC(=O)N2CCCSC2=N1                                  0         0   \n",
       "\n",
       "                                                    Bit_2047  \n",
       "SMILES                                                        \n",
       "CC1=C(C=CC=C1Cl)N2C(C(C2=O)N3C(=O)C4=C(C3=O)C(=...         0  \n",
       "C1CC=C(C(=O)C1)[C@H](CCOC(C2=CC=CC=C2)(C3=CC=CC...         0  \n",
       "COC(=O)C1=CC(=CC(=C1)NC(=O)CC2C(=O)NC3CCCCC3N2)...         0  \n",
       "C[C@@H]/1COC(=O)[C@H](NC(=O)C/C=C\\[C@@H]([C@H](...         0  \n",
       "COCC(=O)N1CCC2(CC1)CN(C2)CC3=CC=C(C=C3)C#N                 0  \n",
       "...                                                      ...  \n",
       "CC1=CC=C(C=C1)/C(=C/C2=CC(=CC=C2)[N+](=O)[O-])/C#N         0  \n",
       "C1=CC(=CC=C1CN2C3=NC=NC=C3N=C(C2=O)C4=CC(=C(C=C...         0  \n",
       "CCN(CC)CCCNC(=O)C1CCC(=O)N(C1C2=CC=C(C=C2)F)C3=...         0  \n",
       "CC(C)NC(=O)CN1CCC(CC1)(C2=CC(=CC=C2)F)NC(=O)C              0  \n",
       "CN(C)C1=CC(=O)N2CCCSC2=N1                                  0  \n",
       "\n",
       "[4169 rows x 2048 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_name = [f'Bit_{i}' for i in range(2048)]\n",
    "fp_cmpd = []\n",
    "for smiles in X_test_raw.tolist():\n",
    "    cmpd = Chem.MolFromSmiles(smiles)\n",
    "    fp = AllChem.GetMorganFingerprintAsBitVect(cmpd,2, nBits=2048)\n",
    "    binary = fp.ToBitString()\n",
    "    fp_cmpd.append(list(binary))\n",
    "X_test = pd.DataFrame(fp_cmpd, index = X_test_raw, columns=fp_name)\n",
    "X_test = X_test.astype(int)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1239eac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def report_loss_cf(name, model, X_train, y_train, X_test, y_test, graph=0):\n",
    "    \n",
    "    print('Model performance for: ', name)\n",
    "    \n",
    "    def calculate_loss(model, X, y):\n",
    "        pred = model.predict(X)\n",
    "        pred_proba = model.predict_proba(X)\n",
    "\n",
    "        accuracy = accuracy_score(y, pred)\n",
    "        f1 = f1_score(y, pred)\n",
    "        auc = roc_auc_score(y, pred_proba[:,1])\n",
    "        fpr, tpr, _ = roc_curve(y, pred_proba[:,1])\n",
    "        return pred, accuracy, f1, auc, fpr, tpr\n",
    "    \n",
    "    y_train_pred, accuracy_train, f1_train, auc_train, fpr_train, tpr_train = calculate_loss(\n",
    "        model, X_train, y_train)\n",
    "    print(f'train set accuracy: {accuracy_train:.3f}')\n",
    "    print(f'train set f1 score: {f1_train:.3f}')\n",
    "    print(f'training set AUC score: {auc_train:.3f}')\n",
    "    print()\n",
    "    \n",
    "    y_test_pred, accuracy_test, f1_test, auc_test, fpr_test, tpr_test = calculate_loss(\n",
    "        model, X_test, y_test)\n",
    "    print(f'test set accuracy: {accuracy_test:.3f}')\n",
    "    print(f'test set f1 score: {f1_test:.3f}')\n",
    "    print(f'test set AUC score: {auc_test:.3f}')\n",
    "\n",
    "    if graph == 0:\n",
    "        return \n",
    "\n",
    "    elif graph == 1:\n",
    "        \n",
    "        fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(7, 3))\n",
    "        cm_train = confusion_matrix(y_train, y_train_pred)\n",
    "        cm_test = confusion_matrix(y_test, y_test_pred)\n",
    "        \n",
    "        sns.heatmap(cm_train, annot=True, cmap=\"Blues\", fmt='g', ax=ax1)\n",
    "        ax1.set_title('train set')\n",
    "        ax1.set_xlabel('Predicted labl')\n",
    "        ax1.set_ylabel('True label')\n",
    "        ax1.xaxis.set_ticklabels(['inactive', 'active'])\n",
    "        ax1.yaxis.set_ticklabels(['inactive', 'active'])\n",
    "        \n",
    "        sns.heatmap(cm_test, annot=True, cmap=\"Blues\", fmt='g', ax=ax2)\n",
    "        ax2.set_title('test set')\n",
    "        ax2.set_xlabel('Predicted labl')\n",
    "        ax2.set_ylabel('True label')\n",
    "        ax2.xaxis.set_ticklabels(['inactive', 'active'])\n",
    "        ax2.yaxis.set_ticklabels(['inactive', 'active'])\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        \n",
    "    elif graph == 2:   \n",
    "        \n",
    "        plt.figure(figsize=(5,3))\n",
    "        plt.plot([0,1], [0,1], linestyle='--', label='random')\n",
    "        plt.plot(fpr_train, tpr_train,label=\"Train_AUC=\"+str(auc_train.round(3)))\n",
    "        plt.plot(fpr_test,tpr_test,label=\"Test_AUC=\"+str(auc_test.round(3)))\n",
    "        plt.xlabel('False Positive Rate')\n",
    "        plt.ylabel('True Positive Rate')\n",
    "        plt.legend(loc=4)\n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c69d49e",
   "metadata": {},
   "source": [
    "## <font color='red'>modeling strategy: used default (either class_weight='balanced', or calculate class_weight using equation: wj=n_samples / (n_classes * n_samplesj)) for all classification models. Pick the model with best f1 score to fine tune (maybe try a couple of models)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2f8863",
   "metadata": {},
   "source": [
    "**wj = n_samples / (n_classes * n_samplesj)**\n",
    "\n",
    "for our sales data (train set):\n",
    "- n_samples=  11632,  n_classes= 2(0&1), n_sample0= 11127, n_samples1= 505\n",
    "- Weights for class 0: w0=  11632/(2*11127) = 0.523\n",
    "- Weights for class 1: w1=  11632/(2*505) = 11.517\n",
    "- w1:w0 = 22:1\n",
    "\n",
    "so class_weight={0:22, 1:1}"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "47f39559",
   "metadata": {},
   "source": [
    "# <font color='red'>F1 score vs AUC, which is the best classification metric?</font>\n",
    "(https://stephenallwright.com/f1-score-vs-auc/#:~:text=Difference%20between%20F1%20and%20AUC%20metric%20definitions&text=AUC%20is%20the%20area%20under,and%20precision%20of%20the%20model.)\n",
    "    \n",
    "**Is F1 the same as AUC?**\n",
    "<br>F1 and AUC are often discussed in similar contexts and have the same end goal, but they are not the same and have very different approaches to measuring model performance.\n",
    "\n",
    "**What is F1 score?**\n",
    "<br>F1 score (also known as F-measure, or balanced F-score) is an error metric whose score ranges from 0 to 1, where 0 is the worst and 1 is the best possible score.\n",
    "\n",
    "It is a popular metric to use for classification models as it provides robust results for **<font color='red'>imbalanced datasets </font>** and evaluates both the recall and precision ability of the model. The reason F1 is able to evaluate a model's precision and recall ability is due to the way it is derived, which is as follows:\n",
    "<div><img src=\"attachment:image.png\" width=\"250\"/></div> \n",
    "\n",
    "**What is AUC?**\n",
    "<br>AUC, or ROC AUC, stands for Area Under the Receiver Operating Characteristic Curve. The score it produces ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random.\n",
    "\n",
    "The metric is calculated as the area underneath the Receiver Operating Characteristic Curve (ROC). The ROC is a graph which maps the relationship between the true positive rate (TPR) of the model and the false positive rate (FPR). It shows at various intervals the TPR that we can expect to receive for a given trade-off with FPR.\n",
    "\n",
    "The area under this ROC curve, AUC, **<font color='red'>therefore equates to the model’s ability to predict classes correctly</font>**, as a large amount of area would show that the model can achieve a high true positive rate with a correspondingly low false positive rate.\n",
    "\n",
    "To illustrate this, let’s look at an illustration of the metric:\n",
    "<div><img src=\"attachment:image-2.png\" width=\"250\"/></div> \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2146c58",
   "metadata": {},
   "source": [
    "## <font color='red'>What is the difference between F1 and AUC? The key differences between F1 and AUC are how they handle imbalanced datasets, the input they take, and their approach to calculating the resulting metrics.</font>\n",
    "\n",
    "**Difference between F1 and AUC metric definitions**\n",
    "<br>The way these two metrics are calculated are very different. AUC is the area under the ROC curve which is calculated at thresholds between the True Positive Rate and the False Positive Rate, whilst F1 is a straight forward calculation involving the overall recall and precision of the model.\n",
    "\n",
    "In this regard they are extremely different and tackle the problem of assessing performance from very different angles.\n",
    "\n",
    "**Difference between prediction inputs for F1 and AUC**\n",
    "<br>Another difference between F1 and AUC is the inputs they take. F1 requires predicted classes whilst AUC needs the predicted probabilities.\n",
    "\n",
    "Because of this you will need to define the probability boundary between classes before using F1, unlike with AUC, and you will find that your F1 score changes depending on where it is set.\n",
    "\n",
    "**<font color='red'>F1 score vs AUC on imbalanced datasets</font>** <br>\n",
    "<font color='red'>The last but possibly most important difference to be aware of is their behaviour on imbalanced datasets. AUC does not perform well on imbalanced datasets which often leads to misleading results, whilst F1 is still able to measure performance objectively when the class balance is skewed.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c033ac",
   "metadata": {},
   "source": [
    "## When should I use F1 or AUC?\n",
    "\n",
    "Now that we have looked at their key differences, how does this impact when you should use one or the other?\n",
    "\n",
    "<font color='red'>F1 should be used for situations when you either have an imbalanced dataset or you need to communicate your results to end users, due to the relatively simple definition of F1 in comparison with AUC. AUC should be used when you have a balanced dataset or you don’t want to set a probability boundary between classes, which is required for F1.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03c40246",
   "metadata": {},
   "source": [
    "## Is AUC better than F1 score?\n",
    "\n",
    "The metric which is best depends on your use case and the dataset, but if one of either F1 or AUC had to be recommended then I would suggest F1 score. It is the go-to metric for classification models, and will provide reliable scores for a wide array of projects due to it’s performance on imbalanced datasets and it’s simpler interpretability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c9cfd429",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import BernoulliNB, ComplementNB\n",
    "\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "\n",
    "from sklearn import set_config\n",
    "set_config(display='diagram')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7a316f",
   "metadata": {},
   "source": [
    "# LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29bd8196",
   "metadata": {},
   "source": [
    "class sklearn.linear_model.LogisticRegression(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None)\n",
    "- class_weightdict or ‘balanced’, default=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d0fe995",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance for:  LogisticRegression\n",
      "train set accuracy: 0.899\n",
      "train set f1 score: 0.786\n",
      "training set AUC score: 0.966\n",
      "\n",
      "test set accuracy: 0.801\n",
      "test set f1 score: 0.595\n",
      "test set AUC score: 0.845\n"
     ]
    }
   ],
   "source": [
    "lgr = LogisticRegression(solver='liblinear',class_weight='balanced',max_iter=500,random_state=2022)\n",
    "model_lgr = lgr.fit(X_train, y_train)\n",
    "report_loss_cf('LogisticRegression', model_lgr, X_train, y_train, X_test, y_test, graph=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1e05d38e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'class_weight': 'balanced',\n",
       " 'dual': False,\n",
       " 'fit_intercept': True,\n",
       " 'intercept_scaling': 1,\n",
       " 'l1_ratio': None,\n",
       " 'max_iter': 500,\n",
       " 'multi_class': 'auto',\n",
       " 'n_jobs': None,\n",
       " 'penalty': 'l2',\n",
       " 'random_state': 2022,\n",
       " 'solver': 'liblinear',\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgr.get_params()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "8fadf760",
   "metadata": {},
   "source": [
    "The “saga” solver is often the best choice. The “liblinear” solver is used by default for historical reasons.\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce783003",
   "metadata": {},
   "source": [
    "# RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00c7a32",
   "metadata": {},
   "source": [
    "class sklearn.ensemble.RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9b2e7c1f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance for:  RandomForestClassifier\n",
      "train set accuracy: 0.996\n",
      "train set f1 score: 0.990\n",
      "training set AUC score: 1.000\n",
      "\n",
      "test set accuracy: 0.874\n",
      "test set f1 score: 0.601\n",
      "test set AUC score: 0.877\n"
     ]
    }
   ],
   "source": [
    "rfc=RandomForestClassifier(class_weight='balanced',random_state=2022)\n",
    "model_rfc = rfc.fit(X_train, y_train)\n",
    "report_loss_cf('RandomForestClassifier', model_rfc, X_train, y_train, X_test, y_test, graph=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e9765c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': 'balanced',\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': 2022,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "493cd6ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = model_rfc.feature_importances_\n",
    "\n",
    "plt.figure(figsize=(8,3))\n",
    "plt.plot(importance)\n",
    "plt.xlabel('feature')\n",
    "plt.ylabel('feature importance score')\n",
    "plt.xticks(fontsize=8, rotation=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "60df46ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct Data Frame of top 10 features\n",
    "def plot_feature_importance(model):\n",
    "    importance = pd.DataFrame(\n",
    "        {\n",
    "            'Feature': model.feature_names_in_,\n",
    "            'Score': model.feature_importances_\n",
    "            }\n",
    "        )\n",
    "    importance.sort_values(by='Score', ascending=False, inplace=True)\n",
    "    top_importance = importance[:10]\n",
    "    print(top_importance)\n",
    "    plt.figure(figsize=(4,3))\n",
    "    sns.set(style=\"white\", palette=\"muted\", color_codes=True)\n",
    "    sns.barplot(\n",
    "        y='Feature', \n",
    "        x='Score', \n",
    "        data=top_importance, \n",
    "        orient='h', \n",
    "        color='b'\n",
    "    ).set(title='Feature importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ba98c7ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Feature     Score\n",
      "650    Bit_650  0.024431\n",
      "314    Bit_314  0.018437\n",
      "1750  Bit_1750  0.008360\n",
      "1791  Bit_1791  0.007739\n",
      "1199  Bit_1199  0.007477\n",
      "80      Bit_80  0.006722\n",
      "1349  Bit_1349  0.006430\n",
      "1917  Bit_1917  0.006234\n",
      "807    Bit_807  0.005912\n",
      "881    Bit_881  0.005503\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(model_rfc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34c86927",
   "metadata": {},
   "source": [
    "# GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360651e3",
   "metadata": {},
   "source": [
    "class sklearn.ensemble.GradientBoostingClassifier(*, loss='log_loss', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)\n",
    "- sample_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c588ddf6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance for:  GradientBoostingClassifier\n",
      "train set accuracy: 0.862\n",
      "train set f1 score: 0.522\n",
      "training set AUC score: 0.884\n",
      "\n",
      "test set accuracy: 0.856\n",
      "test set f1 score: 0.497\n",
      "test set AUC score: 0.842\n"
     ]
    }
   ],
   "source": [
    "gbc=GradientBoostingClassifier(random_state=2022)\n",
    "model_gbc = gbc.fit(X_train, y_train)\n",
    "report_loss_cf('GradientBoostingClassifier',model_gbc,X_train,y_train,X_test,y_test,graph=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "de9a963b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'friedman_mse',\n",
       " 'init': None,\n",
       " 'learning_rate': 0.1,\n",
       " 'loss': 'log_loss',\n",
       " 'max_depth': 3,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_iter_no_change': None,\n",
       " 'random_state': 2022,\n",
       " 'subsample': 1.0,\n",
       " 'tol': 0.0001,\n",
       " 'validation_fraction': 0.1,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b361ebe",
   "metadata": {},
   "source": [
    "# XGBClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe5d840",
   "metadata": {},
   "source": [
    "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "       colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
    "       max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,\n",
    "       n_estimators=100, n_jobs=1, nthread=None,\n",
    "       objective='multi:softprob', random_state=0, reg_alpha=0,\n",
    "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\n",
    "       subsample=1, verbosity=1)\n",
    "- In general, we tend to set scale_pos_weight proportionally to the imbalance. For example, if 20% of the images contain a cat, we would set scale_positive_weight to 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "484f0ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance for:  XGBClassifier\n",
      "train set accuracy: 0.941\n",
      "train set f1 score: 0.867\n",
      "training set AUC score: 0.987\n",
      "\n",
      "test set accuracy: 0.840\n",
      "test set f1 score: 0.639\n",
      "test set AUC score: 0.871\n"
     ]
    }
   ],
   "source": [
    "xgbc=XGBClassifier(scale_pos_weight=4, random_state=2022)\n",
    "model_xgbc = xgbc.fit(X_train, y_train)\n",
    "report_loss_cf('XGBClassifier', model_xgbc, X_train, y_train, X_test, y_test, graph=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "43bde5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'binary:logistic',\n",
       " 'use_label_encoder': False,\n",
       " 'base_score': 0.5,\n",
       " 'booster': 'gbtree',\n",
       " 'callbacks': None,\n",
       " 'colsample_bylevel': 1,\n",
       " 'colsample_bynode': 1,\n",
       " 'colsample_bytree': 1,\n",
       " 'early_stopping_rounds': None,\n",
       " 'enable_categorical': False,\n",
       " 'eval_metric': None,\n",
       " 'gamma': 0,\n",
       " 'gpu_id': -1,\n",
       " 'grow_policy': 'depthwise',\n",
       " 'importance_type': None,\n",
       " 'interaction_constraints': '',\n",
       " 'learning_rate': 0.300000012,\n",
       " 'max_bin': 256,\n",
       " 'max_cat_to_onehot': 4,\n",
       " 'max_delta_step': 0,\n",
       " 'max_depth': 6,\n",
       " 'max_leaves': 0,\n",
       " 'min_child_weight': 1,\n",
       " 'missing': nan,\n",
       " 'monotone_constraints': '()',\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': 0,\n",
       " 'num_parallel_tree': 1,\n",
       " 'predictor': 'auto',\n",
       " 'random_state': 2022,\n",
       " 'reg_alpha': 0,\n",
       " 'reg_lambda': 1,\n",
       " 'sampling_method': 'uniform',\n",
       " 'scale_pos_weight': 4,\n",
       " 'subsample': 1,\n",
       " 'tree_method': 'exact',\n",
       " 'validate_parameters': 1,\n",
       " 'verbosity': None}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbc.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "306a0b84",
   "metadata": {},
   "source": [
    "# LGBMClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f6749f",
   "metadata": {},
   "source": [
    "classlightgbm.LGBMClassifier(boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0.0, min_child_weight=0.001, min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0, reg_lambda=0.0, random_state=None, n_jobs=None, importance_type='split', **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ca6e97a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance for:  LightGBMClassifier\n",
      "train set accuracy: 0.907\n",
      "train set f1 score: 0.798\n",
      "training set AUC score: 0.970\n",
      "\n",
      "test set accuracy: 0.834\n",
      "test set f1 score: 0.640\n",
      "test set AUC score: 0.873\n"
     ]
    }
   ],
   "source": [
    "light=LGBMClassifier(objective='binary',class_weight='balanced',random_state=2022)\n",
    "model_light = light.fit(X_train, y_train)\n",
    "report_loss_cf('LightGBMClassifier', model_light, X_train, y_train, X_test, y_test, graph=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3069f804",
   "metadata": {},
   "source": [
    "# CatBoostClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef13ec71",
   "metadata": {},
   "source": [
    "{'nan_mode': 'Min', 'eval_metric': 'Logloss', 'iterations': 1000, 'sampling_frequency': 'PerTree', 'leaf_estimation_method': 'Newton', 'grow_policy': 'SymmetricTree', 'penalties_coefficient': 1, 'boosting_type': 'Plain', 'model_shrink_mode': 'Constant', 'feature_border_type': 'GreedyLogSum', 'bayesian_matrix_reg': 0.10000000149011612, 'l2_leaf_reg': 3, 'random_strength': 1, 'rsm': 1, 'boost_from_average': False, 'model_size_reg': 0.5, 'pool_metainfo_options': {'tags': {}}, 'subsample': 1, 'use_best_model': False, 'class_names': [0, 1], 'random_seed': 0, 'depth': 6, 'posterior_sampling': False, 'border_count': 254, 'classes_count': 0, 'auto_class_weights': 'None', 'sparse_features_conflict_fraction': 0, 'leaf_estimation_backtracking': 'AnyImprovement', 'best_model_min_trees': 1, 'model_shrink_rate': 0, 'min_data_in_leaf': 1, 'loss_function': 'Logloss', 'learning_rate': 0.0028669999446719885, 'score_function': 'Cosine', 'task_type': 'CPU', 'leaf_estimation_iterations': 10, 'bootstrap_type': 'MVS', 'max_leaves': 64}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "16ad3745",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiaofwu-m1/opt/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance for:  CatBoostClassifier\n",
      "train set accuracy: 0.914\n",
      "train set f1 score: 0.811\n",
      "training set AUC score: 0.970\n",
      "\n",
      "test set accuracy: 0.835\n",
      "test set f1 score: 0.639\n",
      "test set AUC score: 0.876\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiaofwu-m1/opt/anaconda3/lib/python3.9/site-packages/catboost/core.py:1411: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  self._init_pool(data, label, cat_features, text_features, embedding_features, embedding_features_data, pairs, weight,\n"
     ]
    }
   ],
   "source": [
    "cat=CatBoostClassifier(auto_class_weights='Balanced', verbose=False)\n",
    "model_cat = cat.fit(X_train, y_train)\n",
    "report_loss_cf('CatBoostClassifier', model_cat, X_train, y_train, X_test, y_test, graph=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b323cd",
   "metadata": {},
   "source": [
    "# Support Vector Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b1def1",
   "metadata": {},
   "source": [
    "class sklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b6e74482",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance for:  Support Vector Classifier\n",
      "train set accuracy: 0.910\n",
      "train set f1 score: 0.810\n",
      "training set AUC score: 0.960\n",
      "\n",
      "test set accuracy: 0.785\n",
      "test set f1 score: 0.568\n",
      "test set AUC score: 0.820\n"
     ]
    }
   ],
   "source": [
    "#very slow\n",
    "svc=SVC(kernel='linear', class_weight='balanced',probability=True)\n",
    "model_svc = svc.fit(X_train, y_train)\n",
    "report_loss_cf('Support Vector Classifier', model_svc, X_train, y_train, X_test, y_test, graph=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c982223",
   "metadata": {},
   "source": [
    "# BernoulliNB Naive Bayes Classifier\n",
    "- good for binary classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2980a63",
   "metadata": {},
   "source": [
    "class sklearn.naive_bayes.BernoulliNB(*, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f93b7174",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance for:  BernoulliNB Naive Bayes Classifier\n",
      "train set accuracy: 0.824\n",
      "train set f1 score: 0.607\n",
      "training set AUC score: 0.856\n",
      "\n",
      "test set accuracy: 0.799\n",
      "test set f1 score: 0.564\n",
      "test set AUC score: 0.823\n"
     ]
    }
   ],
   "source": [
    "bnbc=BernoulliNB()\n",
    "model_bnbc = bnbc.fit(X_train, y_train)\n",
    "report_loss_cf('BernoulliNB Naive Bayes Classifier', model_bnbc, X_train, y_train, X_test, y_test, graph=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d9afd4b",
   "metadata": {},
   "source": [
    "# KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29980c84",
   "metadata": {},
   "source": [
    "class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9a6d54d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance for:  KNeighborsClassifier\n",
      "train set accuracy: 0.996\n",
      "train set f1 score: 0.990\n",
      "training set AUC score: 1.000\n",
      "\n",
      "test set accuracy: 0.859\n",
      "test set f1 score: 0.561\n",
      "test set AUC score: 0.819\n"
     ]
    }
   ],
   "source": [
    "knc=KNeighborsClassifier(weights='distance')\n",
    "model_knc = knc.fit(X_train, y_train)\n",
    "report_loss_cf('KNeighborsClassifier', model_knc, X_train, y_train, X_test, y_test, graph=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1179932",
   "metadata": {},
   "source": [
    "|Method|Train f1|Train AUC|Train accuracy|Test f1|Test AUC|Test accuracy|\n",
    "|----------|:---:|:--------:|:---------:|:------:|:--:|:----:|\n",
    "|CatBoost Classifier|0.806|0.971|0.911|0.647|0.872|0.843|\n",
    "|XGBoost Classifier|0.866|0.987|0.941|0.645|0.860|0.848|\n",
    "|LightGBM Classifier|0.802|0.972|0.908|0.634|0.870|0.834|\n",
    "|Logistic Regression|0.785|0.967|0.898|0.605|0.839|0.816|\n",
    "|RandomForest Classifier|0.991|0.999|0.997|0.577|0.873|0.868|\n",
    "|Support Vector Classifier|0.808|0.962|0.910|0.577|0.816|0.799|\n",
    "|BernoulliNB Naive Bayes Classifier|0.617|0.856|0.832|0.573|0.830|0.813|\n",
    "|KNeighbors Classifier|0.991|1.000|0.997|0.544|0.816|0.858|\n",
    "|GradientBoosting Classifier|0.531|0.888|0.865|0.483|0.846|0.854|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c585e55b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29/29 [06:19<00:00, 13.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
      "Model                                                                           \n",
      "NearestCentroid                    0.79               0.75     0.75      0.80   \n",
      "XGBClassifier                      0.88               0.74     0.74      0.87   \n",
      "BernoulliNB                        0.80               0.74     0.74      0.81   \n",
      "LinearDiscriminantAnalysis         0.84               0.74     0.74      0.84   \n",
      "RidgeClassifierCV                  0.85               0.74     0.74      0.85   \n",
      "RidgeClassifier                    0.85               0.74     0.74      0.85   \n",
      "LGBMClassifier                     0.87               0.73     0.73      0.86   \n",
      "LogisticRegression                 0.81               0.72     0.72      0.81   \n",
      "Perceptron                         0.81               0.72     0.72      0.81   \n",
      "ExtraTreesClassifier               0.87               0.72     0.72      0.86   \n",
      "BaggingClassifier                  0.86               0.72     0.72      0.85   \n",
      "PassiveAggressiveClassifier        0.80               0.72     0.72      0.81   \n",
      "DecisionTreeClassifier             0.82               0.71     0.71      0.82   \n",
      "SGDClassifier                      0.81               0.71     0.71      0.81   \n",
      "LinearSVC                          0.79               0.70     0.70      0.80   \n",
      "RandomForestClassifier             0.87               0.70     0.70      0.85   \n",
      "SVC                                0.87               0.69     0.69      0.85   \n",
      "ExtraTreeClassifier                0.81               0.69     0.69      0.81   \n",
      "KNeighborsClassifier               0.85               0.67     0.67      0.83   \n",
      "AdaBoostClassifier                 0.84               0.67     0.67      0.82   \n",
      "GaussianNB                         0.58               0.66     0.66      0.62   \n",
      "QuadraticDiscriminantAnalysis      0.82               0.61     0.61      0.79   \n",
      "LabelSpreading                     0.81               0.53     0.53      0.74   \n",
      "LabelPropagation                   0.81               0.53     0.53      0.74   \n",
      "CalibratedClassifierCV             0.80               0.52     0.52      0.73   \n",
      "DummyClassifier                    0.80               0.50     0.50      0.71   \n",
      "\n",
      "                               Time Taken  \n",
      "Model                                      \n",
      "NearestCentroid                      1.22  \n",
      "XGBClassifier                       30.72  \n",
      "BernoulliNB                          1.76  \n",
      "LinearDiscriminantAnalysis          14.80  \n",
      "RidgeClassifierCV                   12.99  \n",
      "RidgeClassifier                      3.05  \n",
      "LGBMClassifier                       3.30  \n",
      "LogisticRegression                   2.17  \n",
      "Perceptron                           2.41  \n",
      "ExtraTreesClassifier                 9.96  \n",
      "BaggingClassifier                   25.06  \n",
      "PassiveAggressiveClassifier          3.29  \n",
      "DecisionTreeClassifier               4.03  \n",
      "SGDClassifier                        8.99  \n",
      "LinearSVC                           25.47  \n",
      "RandomForestClassifier               8.19  \n",
      "SVC                                 82.88  \n",
      "ExtraTreeClassifier                  1.09  \n",
      "KNeighborsClassifier                 5.54  \n",
      "AdaBoostClassifier                   6.46  \n",
      "GaussianNB                           1.46  \n",
      "QuadraticDiscriminantAnalysis       16.28  \n",
      "LabelSpreading                      13.36  \n",
      "LabelPropagation                    11.34  \n",
      "CalibratedClassifierCV              80.19  \n",
      "DummyClassifier                      1.01  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import lazypredict\n",
    "from lazypredict.Supervised import LazyClassifier\n",
    "clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)\n",
    "models, predictions = clf.fit(X_train, X_test, y_train, y_test)\n",
    "\n",
    "print(models)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6872c56",
   "metadata": {},
   "source": [
    "# <font color='red'>Best choices are catBoost,  XGBoost, and LightGBM Classifier. We need to tune these three model. But need to consider 1) catBoost is not very popular? 2) XGBoost takes significant longer time to train than LightGBM. 3) If similar performance, lightGBM should be chosen as the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55ff03a",
   "metadata": {},
   "source": [
    "# LightGBM Classifier. We need to tune the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279c1b7c",
   "metadata": {},
   "source": [
    "classlightgbm.LGBMClassifier(boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0.0, min_child_weight=0.001, min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0, reg_lambda=0.0, random_state=None, n_jobs=None, importance_type='split', **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9e93284e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boosting_type': 'gbdt',\n",
       " 'class_weight': 'balanced',\n",
       " 'colsample_bytree': 1.0,\n",
       " 'importance_type': 'split',\n",
       " 'learning_rate': 0.1,\n",
       " 'max_depth': -1,\n",
       " 'min_child_samples': 20,\n",
       " 'min_child_weight': 0.001,\n",
       " 'min_split_gain': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': -1,\n",
       " 'num_leaves': 31,\n",
       " 'objective': 'binary',\n",
       " 'random_state': 2022,\n",
       " 'reg_alpha': 0.0,\n",
       " 'reg_lambda': 0.0,\n",
       " 'silent': 'warn',\n",
       " 'subsample': 1.0,\n",
       " 'subsample_for_bin': 200000,\n",
       " 'subsample_freq': 0}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "light.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "371952de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model performance for:  LightGBMClassifier\n",
      "train set accuracy: 0.907\n",
      "train set f1 score: 0.798\n",
      "training set AUC score: 0.970\n",
      "\n",
      "test set accuracy: 0.834\n",
      "test set f1 score: 0.640\n",
      "test set AUC score: 0.873\n"
     ]
    }
   ],
   "source": [
    "light=LGBMClassifier(class_weight='balanced', objective='binary')\n",
    "model_light = light.fit(X_train, y_train)\n",
    "report_loss_cf('LightGBMClassifier', model_light, X_train, y_train, X_test, y_test, graph=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1c7759",
   "metadata": {},
   "source": [
    "Strategy for overfitting\n",
    "\n",
    "1. Grow Shallower Trees:\n",
    "    - Decrease max_depth\n",
    "    - Decrease num_leaves\n",
    "    - Increase min_gain_to_split\n",
    "    - Increase min_data_in_leaf and min_sum_hessian_in_leaf\n",
    "2. Grow Less Trees\n",
    "    - Decrease num_iterations\n",
    "    - Use Early Stopping\n",
    "    - Consider Fewer Splits\n",
    "    - Enable Feature Pre-Filtering When Creating Dataset\n",
    "    - Decrease max_bin or max_bin_by_feature When Creating Dataset\n",
    "    - Increase min_data_in_bin When Creating Dataset\n",
    "    - Decrease feature_fraction\n",
    "    - Decrease max_cat_threshold\n",
    "3. Use Less Data\n",
    "    - Use Bagging"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae6b8cb",
   "metadata": {},
   "source": [
    "### In general, for Better Accuracy: \n",
    "\n",
    "- Use large max_bin (may be slower)\n",
    "- Use small learning_rate with large num_iterations\n",
    "- Use large num_leaves (may cause over-fitting)\n",
    "- Use bigger training data\n",
    "- Try dart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e6c37d",
   "metadata": {},
   "source": [
    "### In general, deal with Over-fitting: \n",
    "- Use small max_bin\n",
    "- Use small num_leaves\n",
    "- Use min_data_in_leaf and min_sum_hessian_in_leaf\n",
    "- Use bagging by set bagging_fraction and bagging_freq\n",
    "- Use feature sub-sampling by set feature_fraction\n",
    "- Use bigger training data\n",
    "- Try lambda_l1, lambda_l2 and min_gain_to_split for regularization\n",
    "- Try max_depth to avoid growing deep tree\n",
    "- Try extra_trees\n",
    "- Try increasing path_smooth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9f1329e8",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sp_randint' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6 screen.ipynb Cell 73\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# https://dagshub.com/nipdep/How-to-Win-a-Data-Science-Competition-Learn-from-Top-Kagglers/src/master/final_project/srcipts/lightgbm-hyperparameter-optimisation-lb-0-761.ipynb\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39m# Set up HyperParameter search\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# We use random search, which is more flexible and more efficient than a grid search\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m param_test \u001b[39m=\u001b[39m{\u001b[39m'\u001b[39m\u001b[39mnum_leaves\u001b[39m\u001b[39m'\u001b[39m: sp_randint(\u001b[39m6\u001b[39m, \u001b[39m50\u001b[39m), \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m              \u001b[39m'\u001b[39m\u001b[39mmin_child_samples\u001b[39m\u001b[39m'\u001b[39m: sp_randint(\u001b[39m100\u001b[39m, \u001b[39m500\u001b[39m), \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m              \u001b[39m'\u001b[39m\u001b[39mmin_child_weight\u001b[39m\u001b[39m'\u001b[39m: [\u001b[39m1e-5\u001b[39m, \u001b[39m1e-3\u001b[39m, \u001b[39m1e-2\u001b[39m, \u001b[39m1e-1\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m1e1\u001b[39m, \u001b[39m1e2\u001b[39m, \u001b[39m1e3\u001b[39m, \u001b[39m1e4\u001b[39m],\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m              \u001b[39m'\u001b[39m\u001b[39msubsample\u001b[39m\u001b[39m'\u001b[39m: sp_uniform(loc\u001b[39m=\u001b[39m\u001b[39m0.2\u001b[39m, scale\u001b[39m=\u001b[39m\u001b[39m0.8\u001b[39m), \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m              \u001b[39m'\u001b[39m\u001b[39mcolsample_bytree\u001b[39m\u001b[39m'\u001b[39m: sp_uniform(loc\u001b[39m=\u001b[39m\u001b[39m0.4\u001b[39m, scale\u001b[39m=\u001b[39m\u001b[39m0.6\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m              \u001b[39m'\u001b[39m\u001b[39mreg_alpha\u001b[39m\u001b[39m'\u001b[39m: [\u001b[39m0\u001b[39m, \u001b[39m1e-1\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m, \u001b[39m5\u001b[39m, \u001b[39m7\u001b[39m, \u001b[39m10\u001b[39m, \u001b[39m50\u001b[39m, \u001b[39m100\u001b[39m],\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m              \u001b[39m'\u001b[39m\u001b[39mreg_lambda\u001b[39m\u001b[39m'\u001b[39m: [\u001b[39m0\u001b[39m, \u001b[39m1e-1\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m5\u001b[39m, \u001b[39m10\u001b[39m, \u001b[39m20\u001b[39m, \u001b[39m50\u001b[39m, \u001b[39m100\u001b[39m]}\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m clf \u001b[39m=\u001b[39m lgb\u001b[39m.\u001b[39mLGBMClassifier(max_depth\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, random_state\u001b[39m=\u001b[39m\u001b[39m314\u001b[39m, silent\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, metric\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mNone\u001b[39m\u001b[39m'\u001b[39m, n_jobs\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m, n_estimators\u001b[39m=\u001b[39m\u001b[39m5000\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m gs \u001b[39m=\u001b[39m RandomizedSearchCV(\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m     estimator\u001b[39m=\u001b[39mclf, param_distributions\u001b[39m=\u001b[39mparam_test, \n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m     n_iter\u001b[39m=\u001b[39mn_HP_points_to_test,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m     random_state\u001b[39m=\u001b[39m\u001b[39m314\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/xiaofwu-m1/Desktop/GitHub/ML-chemical-screen-P450/P450_2d6%20screen.ipynb#Y132sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m     verbose\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sp_randint' is not defined"
     ]
    }
   ],
   "source": [
    "# https://dagshub.com/nipdep/How-to-Win-a-Data-Science-Competition-Learn-from-Top-Kagglers/src/master/final_project/srcipts/lightgbm-hyperparameter-optimisation-lb-0-761.ipynb\n",
    "# Set up HyperParameter search\n",
    "# We use random search, which is more flexible and more efficient than a grid search\n",
    "\n",
    "param_test ={'num_leaves': sp_randint(6, 50), \n",
    "             'min_child_samples': sp_randint(100, 500), \n",
    "             'min_child_weight': [1e-5, 1e-3, 1e-2, 1e-1, 1, 1e1, 1e2, 1e3, 1e4],\n",
    "             'subsample': sp_uniform(loc=0.2, scale=0.8), \n",
    "             'colsample_bytree': sp_uniform(loc=0.4, scale=0.6),\n",
    "             'reg_alpha': [0, 1e-1, 1, 2, 5, 7, 10, 50, 100],\n",
    "             'reg_lambda': [0, 1e-1, 1, 5, 10, 20, 50, 100]}\n",
    "\n",
    "clf = lgb.LGBMClassifier(max_depth=-1, random_state=314, silent=True, metric='None', n_jobs=4, n_estimators=5000)\n",
    "gs = RandomizedSearchCV(\n",
    "    estimator=clf, param_distributions=param_test, \n",
    "    n_iter=n_HP_points_to_test,\n",
    "    scoring='roc_auc',\n",
    "    cv=3,\n",
    "    refit=True,\n",
    "    random_state=314,\n",
    "    verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d46b2a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Setting the range for class weights\n",
    "weights = np.linspace(0.0,0.99,200)\n",
    "\n",
    "#Creating a dictionary grid for grid search\n",
    "param_test ={'num_leaves': sp_randint(6, 50), \n",
    "             'min_child_samples': sp_randint(100, 500), \n",
    "             'min_child_weight': [1e-5, 1e-3, 1e-2, 1e-1, 1, 1e1, 1e2, 1e3, 1e4],\n",
    "             'subsample': sp_uniform(loc=0.2, scale=0.8), \n",
    "             'colsample_bytree': sp_uniform(loc=0.4, scale=0.6),\n",
    "             'reg_alpha': [0, 1e-1, 1, 2, 5, 7, 10, 50, 100],\n",
    "             'reg_lambda': [0, 1e-1, 1, 5, 10, 20, 50, 100]}\n",
    "\n",
    "gscv = GridSearchCV(estimator= LGBMClassifier(class_weight='balanced',objective='binary'), \n",
    "                          param_grid= param_grid,\n",
    "                          cv=StratifiedKFold(), \n",
    "                          n_jobs=-1, \n",
    "                          scoring='f1')\n",
    "gscv.fit(X_train, y_train)\n",
    "gscv.best_params_\n",
    "#Ploting the score for different values of weight\n",
    "# sns.set_style('whitegrid')\n",
    "# plt.figure(figsize=(6,4))\n",
    "# weigh_data = pd.DataFrame({ 'score': gscv.cv_results_['mean_test_score'], \n",
    "#                            'weight': (1- weights)})\n",
    "# sns.lineplot(weigh_data['weight'], weigh_data['score'])\n",
    "# plt.xlabel('Weight for class 1')\n",
    "# plt.ylabel('F1 score')\n",
    "# plt.xticks([round(i/10,1) for i in range(0,11,1)])\n",
    "# plt.title('Scoring for different class weights', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e5e9d8",
   "metadata": {},
   "source": [
    "# Understanding LightGBM Parameters (and How to Tune Them)(lightGBM usually out-perform XGBoost with larger dataset, i.e. over 10,000 rows)\n",
    "\n",
    "https://neptune.ai/blog/lightgbm-parameters-guide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6314b60",
   "metadata": {},
   "source": [
    "## If someone asks you what is the main difference between LightGBM and XGBoost? You can easily say, their difference is in how they are implemented. (lightgbm uses leaf-wise tree growth algorithm whileXGBoost uses depth-wise tree growth. Leaf-wise method allows the trees to converge faster but the chance of over-fitting increases. )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e20418d5",
   "metadata": {},
   "source": [
    "## <font color='red'>The most important lightgbm parameters - Summary of content below \n",
    "    \n",
    "- <font color='red'>objective: Set it binary or multiclass for classification\n",
    "- <font color='red'>metric: default is null, meaning metric corresponding to specified objective will be used\n",
    "- <font color='red'>boosting: gbdt, rf, dart, goss. default gbdt works fine most of time\n",
    "- <font color='red'>lambda_l1: default is 0. [0, ∞]\n",
    "- <font color='red'>bagging_fraction(subsample): default is 1. [0, 1]. randomly select part of data without resampling\n",
    "- <font color='red'>bagging_freq: default is 0. [0, ∞]. 0 means disable bagging; k means perform bagging at every k iteration. To enable bagging, bagging_fraction should be set to value smaller than 1.0 as well.\n",
    "- <font color='red'>num_leaves: default is 31. [1, ∞], max number of leaves in one tree.\n",
    "- <font color='red'>feature_fraction: default is 1.0. [0,1],if you set it to 0.8, LightGBM will select 80% of features\n",
    "- <font color='red'>max_depth: default is -1. [-1, ∞]. \n",
    "- <font color='red'>max_bin: default is 255. [2, ∞]\n",
    "- <font color='red'>num_iterations: default is 100. [1, ∞], number of boosting iterations\n",
    "- <font color='red'>learning_rate: default is 0.1. Typical: 0.05\n",
    "- <font color='red'>early_stopping_round: default is 0. [0, ∞], model performance, number of iterations, training time.\n",
    "- <font color='red'>categorical_feature: handle categorical features\n",
    "- <font color='red'>bagging_freq: default is 0. [0, ∞]. 0 means disable bagging; k means perform bagging at every k iteration. to enable bagging, bagging_fraction should be set to value smaller than 1.0 as well.\n",
    "- <font color='red'>min_data_in_leaf: default is 20. min_data. can be used to deal with over-fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "048a3653",
   "metadata": {},
   "source": [
    "### According to lightGBM documentation, when facing  overfitting you may want to do the following parameter tuning: \n",
    "\n",
    "- Use small max_bin\n",
    "- Use small num_leaves\n",
    "- Use min_data_in_leaf and min_sum_hessian_in_leaf\n",
    "- Use bagging by set bagging_fraction and bagging_freq\n",
    "- Use feature sub-sampling by set feature_fraction\n",
    "- Use bigger training data\n",
    "- Try lambda_l1, lambda_l2 and min_gain_to_split for regularization\n",
    "- Try max_depth to avoid growing deep tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b522690a",
   "metadata": {},
   "source": [
    "### Gradient Boosting methods (default is OK)\n",
    "- lgbm gbdt (gradient boosted decision trees): default, stable and reliable\n",
    "- Lgbm dart: better accuracy\n",
    "- Lgbm goss: converge faster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5280aa7",
   "metadata": {},
   "source": [
    "### Regularization Parameters:\n",
    "\n",
    "- **Lambda_l1 (and lambda_l2)**: control to l1/l2 and along with min_gain_to_split are used to combat over-fitting.\n",
    "- **num_leaves**: urely num_leaves is one of the most important parameters that controls the complexity of the model. Large num_leaves increases accuracy on the training set and also the chance of getting hurt by overfitting. According to the documentation, one simple way is that num_leaves = 2^(max_depth). **It is necessary to tune num_leaves with the max_depth together.**\n",
    "- **subsample**: With subsample (or bagging_fraction)  you can specify the percentage of rows used per tree building iteration. That means some rows will be randomly selected for fitting each learner (tree). This improved generalization but also speed of training. **I suggest using smaller subsample values for the baseline models and later increase this value when you are done with other experiments (different feature selections, different tree architecture).**\n",
    "- **feature_fraction**: Feature fraction or sub_feature deals with column sampling, LightGBM will randomly select a subset of features on each iteration (tree). For example, if you set it to 0.6, LightGBM will select 60% of features before training each tree. There are two usage for this feature:\n",
    "    - Can be used to speed up training\n",
    "    - Can be used to deal with overfitting\n",
    "- **max_depth**: This parameter control max depth of each trained tree. If you use a large value of max_depth, your model will likely be over fit  to the train set. It will have impact on: \n",
    "    - The best value for the num_leaves parameter\n",
    "    - Model Performance\n",
    "    - Training Time\n",
    "- **max_bin**: Binning is a technique for representing data in a discrete view(histogram). Lightgbm uses a histogram based algorithm to find the optimal split point while creating a weak learner. Therefore, each continuous numeric feature (e.g. number of views for a video) should be split into discrete bins. If you define max_bin 255 that means we can have a maximum of 255 unique values per feature. Then Small max_bin causes faster speed and large value improves accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edae63a8",
   "metadata": {},
   "source": [
    "### Training parameters\n",
    "\n",
    "Training time! When you want to train your model with lightgbm, Some typical issues that may come up when you train lightgbm models are:\n",
    "\n",
    "- Training is a time-consuming process\n",
    "- Dealing with Computational Complexity (CPU/GPU RAM constraints)\n",
    "- Dealing with categorical features\n",
    "- Having an unbalanced dataset\n",
    "- The need for custom metrics\n",
    "- Adjustments that need to be made for Classification or Regression problems\n",
    "- In this section, we will try to explain those points in detail.\n",
    "\n",
    "In this section, we will try to explain those points in detail."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a494854",
   "metadata": {},
   "source": [
    "- **num_iterations**: Num_iterations specifies the number of boosting iterations (trees to build). The more trees you build the more accurate your model can be at the cost of:\n",
    "    - Longer training time\n",
    "    - Higher chance of overfitting\n",
    "    - Start with a lower number of trees to build a baseline and increase it later when you want to squeeze the last % out of your model.\n",
    "    - It is recommended to use smaller learning_rate with larger num_iterations. Also, you should use early_stopping_rounds if you go for higher num_iterations to stop your training when it is not learning anything useful.\n",
    "- **early_stopping_rounds**: This parameter will stop training if the validation metric is not improving after the last early stopping round. That should be defined in pair with a number of iterations. If you set it too large you increase the chance of overfitting (but your model can be better). The rule of thumb is to have it at 10% of your num_iterations.\n",
    "- **lightgbm categorical_feature**: One of the advantages of using lightgbm is that it can handle categorical features very well.  \n",
    "- **lightgbm is_unbalance vs scale_pos_weight**: One of the problems you may face in the binary classification problems is how to deal with the unbalanced datasets. Obviously, you need to balance positive/negative samples but how exactly can you do that in lightgbm? There are two parameters in lightgbm that allow you to deal with this issue is_unbalance and scale_pos_weight, but what is the difference between them and How to use them?\n",
    "    - When you set Is_unbalace: True, the algorithm will try to Automatically balance the weight of the dominated label (with the pos/neg fraction in train set)\n",
    "    - If you want change scale_pos_weight (it is by default 1 which mean assume both positive and negative label are equal) in case of unbalance dataset you can use following formula(based on this issue on lightgbm repository) to set it correctly: sample_pos_weight = number of negative samples / number of positive samples\n",
    "- **lgbm feval**: Sometimes you want to define a custom evaluation function to measure the performance of your model you need to create a feval function. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "644b6189",
   "metadata": {},
   "source": [
    "### classification params vs regression params\n",
    "\n",
    "Note for Classification:\n",
    "\n",
    "- objective: Set it binary or multiclass\n",
    "- metric: Binary_logloss or AUC or etc.\n",
    "- is_unbalance: True or False\n",
    "- scale_pos_weight: used only in binary and multi class applications\n",
    "- num_class: used only in multi-class classification application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "103c1e58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "186656e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "313fbe69",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1203c75",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12693d4a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fed92d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def morgan_fingerprint(smiles:str,radius:int=2,num_bits:int=2048,use_counts:bool = False):\n",
    "#     \"\"\"\n",
    "#     Generates a morgan fingerprint for a smiles string.\n",
    "\n",
    "#     :param smiles: A smiles string for a molecule.\n",
    "#     :param radius: The radius of the fingerprint.\n",
    "#     :param num_bits: The number of bits to use in the fingerprint.\n",
    "#     :param use_counts: Whether to use counts or just a bit vector for the fingerprint\n",
    "#     :return: A 1-D numpy array containing the morgan fingerprint.\n",
    "#     \"\"\"\n",
    "#     if type(smiles) == str:\n",
    "#         mol = Chem.MolFromSmiles(smiles)\n",
    "#     else:\n",
    "#         mol = smiles\n",
    "#     if use_counts:\n",
    "#         fp_vect = AllChem.GetHashedMorganFingerprint(mol, radius, nBits=num_bits)\n",
    "#     else:\n",
    "#         fp_vect = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=num_bits)\n",
    "        \n",
    "#     fp = np.zeros((1,))\n",
    "#     DataStructs.ConvertToNumpyArray(fp_vect, fp)\n",
    "\n",
    "#     return fp "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9e4dc3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def calcFingerprints(smiles):\n",
    "#     m1 = Chem.MolFromSmiles(smiles)\n",
    "#     fp = AllChem.GetMorganFingerprintAsBitVect(m1,2, nBits=2048)\n",
    "#     binary = fp.ToBitString()\n",
    "    \n",
    "#     return list(binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1f9ca79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# class Fingerprint_Transformer(object):\n",
    "    \n",
    "#     def fit(self, X, y=None):\n",
    "#         pass\n",
    "    \n",
    "#     def transform(self, X, y=None):\n",
    "        \n",
    "#     fp_name = [f'Bit_{i}' for i in range(2048)]\n",
    "# fp_cmpd = []\n",
    "# for smiles in df.SMILES.tolist():\n",
    "#     cmpd = Chem.MolFromSmiles(smiles)\n",
    "#     fp = AllChem.GetMorganFingerprintAsBitVect(cmpd,2, nBits=2048)\n",
    "#     binary = fp.ToBitString()\n",
    "#     fp_cmpd.append(list(binary))    \n",
    "        \n",
    "#   df_fp = pd.DataFrame(fp_cmpd, index = df.SMILES, columns=fp_name)\n",
    "# df_fp.shape      \n",
    "        \n",
    "#         return pd.DataFrame(res)\n",
    "    \n",
    "#     def fit_transform(self, X, y=None):\n",
    "#         self.fit(X)\n",
    "#         return self.transform(X) \n",
    "    \n",
    "#     def calcFingerprints(smiles):\n",
    "#         m1 = Chem.MolFromSmiles(smiles)\n",
    "#         fp = AllChem.GetMorganFingerprintAsBitVect(m1,2, nBits=2048)\n",
    "#         binary = fp.ToBitString()\n",
    "#     return list(binary)"
   ]
  }
 ],
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