2-tutorial-get-result.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LR-L2_AtomPairs2D</th>\n",
       "      <th>LR-L2_AtomPairs2DCount</th>\n",
       "      <th>LR-L2_CDK</th>\n",
       "      <th>LR-L2_CDKextended</th>\n",
       "      <th>LR-L2_CDKgraphonly</th>\n",
       "      <th>LR-L2_EState</th>\n",
       "      <th>LR-L2_KlekotaRoth</th>\n",
       "      <th>LR-L2_KlekotaRothCount</th>\n",
       "      <th>LR-L2_MACCS</th>\n",
       "      <th>LR-L2_PubChem</th>\n",
       "      <th>...</th>\n",
       "      <th>kNN_CDK</th>\n",
       "      <th>kNN_CDKextended</th>\n",
       "      <th>kNN_CDKgraphonly</th>\n",
       "      <th>kNN_EState</th>\n",
       "      <th>kNN_KlekotaRoth</th>\n",
       "      <th>kNN_KlekotaRothCount</th>\n",
       "      <th>kNN_MACCS</th>\n",
       "      <th>kNN_PubChem</th>\n",
       "      <th>kNN_Substructure</th>\n",
       "      <th>kNN_SubstructureCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LigandID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHEMBL3971505</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEMBL4114320</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEMBL4106855</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 99 columns</p>\n",
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      "text/plain": [
       "               LR-L2_AtomPairs2D  LR-L2_AtomPairs2DCount  LR-L2_CDK  \\\n",
       "LigandID                                                              \n",
       "CHEMBL3971505                  1                       1          1   \n",
       "CHEMBL4114320                  1                       1          1   \n",
       "CHEMBL4106855                  1                       1          1   \n",
       "CHEMBL3654689                  1                       1          0   \n",
       "CHEMBL4107477                  0                       0          1   \n",
       "\n",
       "               LR-L2_CDKextended  LR-L2_CDKgraphonly  LR-L2_EState  \\\n",
       "LigandID                                                             \n",
       "CHEMBL3971505                  1                   1             1   \n",
       "CHEMBL4114320                  1                   1             1   \n",
       "CHEMBL4106855                  0                   1             1   \n",
       "CHEMBL3654689                  0                   0             0   \n",
       "CHEMBL4107477                  0                   0             0   \n",
       "\n",
       "               LR-L2_KlekotaRoth  LR-L2_KlekotaRothCount  LR-L2_MACCS  \\\n",
       "LigandID                                                                \n",
       "CHEMBL3971505                  1                       1            1   \n",
       "CHEMBL4114320                  1                       1            1   \n",
       "CHEMBL4106855                  1                       1            1   \n",
       "CHEMBL3654689                  1                       1            0   \n",
       "CHEMBL4107477                  0                       0            1   \n",
       "\n",
       "               LR-L2_PubChem  ...  kNN_CDK  kNN_CDKextended  kNN_CDKgraphonly  \\\n",
       "LigandID                      ...                                               \n",
       "CHEMBL3971505              1  ...        1                1                 1   \n",
       "CHEMBL4114320              1  ...        1                1                 1   \n",
       "CHEMBL4106855              1  ...        0                0                 0   \n",
       "CHEMBL3654689              1  ...        1                0                 0   \n",
       "CHEMBL4107477              0  ...        1                0                 0   \n",
       "\n",
       "               kNN_EState  kNN_KlekotaRoth  kNN_KlekotaRothCount  kNN_MACCS  \\\n",
       "LigandID                                                                      \n",
       "CHEMBL3971505           1                1                     1          1   \n",
       "CHEMBL4114320           1                1                     1          1   \n",
       "CHEMBL4106855           0                1                     1          1   \n",
       "CHEMBL3654689           0                0                     1          1   \n",
       "CHEMBL4107477           0                0                     0          0   \n",
       "\n",
       "               kNN_PubChem  kNN_Substructure  kNN_SubstructureCount  \n",
       "LigandID                                                             \n",
       "CHEMBL3971505            1                 1                      1  \n",
       "CHEMBL4114320            1                 1                      1  \n",
       "CHEMBL4106855            0                 1                      1  \n",
       "CHEMBL3654689            0                 0                      0  \n",
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       "\n",
       "[5 rows x 99 columns]"
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "x_test_select = pd.read_csv('x_test_select.csv', index_col = 'LigandID')\n",
    "x_test_select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/home/tarapong/anaconda3/envs/mapk-1.2.2/lib/python3.11/site-packages/sklearn/base.py:432: UserWarning: X has feature names, but RandomForestClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StackMAPK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LigandID</th>\n",
       "      <th></th>\n",
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      "text/plain": [
       "               StackMAPK\n",
       "LigandID                \n",
       "CHEMBL3971505          1\n",
       "CHEMBL4114320          1\n",
       "CHEMBL4106855          1\n",
       "CHEMBL3654689          0\n",
       "CHEMBL4107477          0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = 'Stack'\n",
    "fp = 'All'\n",
    "from joblib import load\n",
    "Model = load('models/'+name+fp+'.joblib')\n",
    "y_test_predict  = Model.predict(x_test_select)\n",
    "y_test_predict  = pd.DataFrame(y_test_predict, index=x_test_select.index, columns=['StackMAPK'])\n",
    "y_test_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StackMAPK</th>\n",
       "      <th>AD_status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LigandID</th>\n",
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       "      <th>CHEMBL3971505</th>\n",
       "      <td>1</td>\n",
       "      <td>within_AD</td>\n",
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       "      <th>CHEMBL4114320</th>\n",
       "      <td>1</td>\n",
       "      <td>within_AD</td>\n",
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       "    <tr>\n",
       "      <th>CHEMBL4106855</th>\n",
       "      <td>1</td>\n",
       "      <td>outside_AD</td>\n",
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       "      <th>CHEMBL3654689</th>\n",
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      "text/plain": [
       "               StackMAPK   AD_status\n",
       "LigandID                            \n",
       "CHEMBL3971505          1   within_AD\n",
       "CHEMBL4114320          1   within_AD\n",
       "CHEMBL4106855          1  outside_AD\n",
       "CHEMBL3654689          0  outside_AD\n",
       "CHEMBL4107477          0  outside_AD"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#AD\n",
    "x_train = pd.read_csv('Stack_All_train.csv', index_col = 'LigandID')\n",
    "#Compute Applicability Domain\n",
    "def nearest_neighbor_AD_predict(x_test, k, z=0.5):\n",
    "    from sklearn.neighbors import NearestNeighbors\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    nn = NearestNeighbors(n_neighbors = k, algorithm='brute', metric='euclidean').fit(x_train)\n",
    "    distance, index = nn.kneighbors(x_train)\n",
    "    #calculate mean and sd of distance in train set\n",
    "    di = np.mean(distance, axis = 1)\n",
    "    dk = round(np.mean(di),2)\n",
    "    sk = round(np.std(di),2)\n",
    "    distance, index = nn.kneighbors(x_test)\n",
    "    di = np.mean(distance, axis = 1)\n",
    "    AD_status = []\n",
    "    for i in range(len(di)):\n",
    "        if di[i] < dk + (z * sk):\n",
    "            AD_status.append('within_AD')\n",
    "        else:\n",
    "            AD_status.append('outside_AD')\n",
    "\n",
    "    # Create DataFrame with index from x_test and the respective status\n",
    "    df = pd.DataFrame(AD_status,  index=x_test.index, columns=['AD_status'])\n",
    "    return df\n",
    "test_ad = nearest_neighbor_AD_predict(x_test_select, 4, z=0.5)\n",
    "\n",
    "#Combine\n",
    "y_test_predict = pd.concat([y_test_predict, test_ad], axis=1)\n",
    "y_test_predict\n"
   ]
  }
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