{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "15a936b1-4cb5-4ab0-aef6-5c7d46bc3638",
   "metadata": {},
   "source": [
    "# Battery Electrolyte Formulation "
   ]
  },
  {
   "attachments": {
    "b5252d9f-88f1-4bfa-8443-1e509ec4e19a.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "faaa6152-b587-486a-9960-8c007ca3e660",
   "metadata": {},
   "source": [
    "![image.png](attachment:b5252d9f-88f1-4bfa-8443-1e509ec4e19a.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f785a51-5af7-4a11-a5a6-fb7b71e373d0",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "403d85da-b26a-4ced-b7e4-c0ec74e86c0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../models\")\n",
    "sys.path.append(\"../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7610e70-8f79-4f75-9502-24fa8179d39a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import models.fm4m as fm4m\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.compose import TransformedTargetRegressor\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "240fbad8-6e5f-4c9f-873d-3274675f0154",
   "metadata": {},
   "source": [
    "### Load data "
   ]
  },
  {
   "attachments": {
    "a4a1be5e-081b-48c5-96d6-d02699a0780f.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "b1fd5b4e-ce5f-43e3-afb6-24c7258a6afc",
   "metadata": {},
   "source": [
    "![image.png](attachment:a4a1be5e-081b-48c5-96d6-d02699a0780f.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f09f7dd-f0e0-416d-9850-a9560254d648",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df  = pd.read_csv(f\"../data/lce/train.csv\").dropna()\n",
    "test_df  = pd.read_csv(f\"../data/lce/test.csv\").dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92825143-daba-4582-990e-61bc9b79f1d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>smi1</th>\n",
       "      <th>conc1</th>\n",
       "      <th>smi2</th>\n",
       "      <th>conc2</th>\n",
       "      <th>smi3</th>\n",
       "      <th>conc3</th>\n",
       "      <th>smi4</th>\n",
       "      <th>conc4</th>\n",
       "      <th>smi5</th>\n",
       "      <th>conc5</th>\n",
       "      <th>smi6</th>\n",
       "      <th>conc6</th>\n",
       "      <th>LCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C1COC(=O)O1</td>\n",
       "      <td>0.327</td>\n",
       "      <td>O=C(OCC)OCC</td>\n",
       "      <td>0.594</td>\n",
       "      <td>FS([N-]S(F)(=O)=O)(=O)=O.[Li+]</td>\n",
       "      <td>0.079</td>\n",
       "      <td>O</td>\n",
       "      <td>0.000</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C1COC(=O)O1</td>\n",
       "      <td>0.356</td>\n",
       "      <td>COC(=O)OC</td>\n",
       "      <td>0.566</td>\n",
       "      <td>FC(F)(F)COB(OCC(F)(F)F)OCC(F)(F)F</td>\n",
       "      <td>0.007</td>\n",
       "      <td>[Li+].F[P-](F)(F)(F)(F)F</td>\n",
       "      <td>0.072</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O=S1(=O)CCCC1</td>\n",
       "      <td>0.250</td>\n",
       "      <td>FS([N-]S(F)(=O)=O)(=O)=O.[Li+]</td>\n",
       "      <td>0.750</td>\n",
       "      <td>O</td>\n",
       "      <td>0.000</td>\n",
       "      <td>O</td>\n",
       "      <td>0.000</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C1COC(=O)O1</td>\n",
       "      <td>0.331</td>\n",
       "      <td>O=C(OCC)OCC</td>\n",
       "      <td>0.577</td>\n",
       "      <td>[Li+].F[P-](F)(F)(F)(F)F</td>\n",
       "      <td>0.092</td>\n",
       "      <td>O</td>\n",
       "      <td>0.000</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COCCOC</td>\n",
       "      <td>0.763</td>\n",
       "      <td>[Li+].C(F)(F)(F)S(=O)(=O)[N-]S(=O)(=O)C(F)(F)F</td>\n",
       "      <td>0.237</td>\n",
       "      <td>O</td>\n",
       "      <td>0.000</td>\n",
       "      <td>O</td>\n",
       "      <td>0.000</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            smi1  conc1                                            smi2  \\\n",
       "0    C1COC(=O)O1  0.327                                     O=C(OCC)OCC   \n",
       "1    C1COC(=O)O1  0.356                                       COC(=O)OC   \n",
       "2  O=S1(=O)CCCC1  0.250                  FS([N-]S(F)(=O)=O)(=O)=O.[Li+]   \n",
       "3    C1COC(=O)O1  0.331                                     O=C(OCC)OCC   \n",
       "4         COCCOC  0.763  [Li+].C(F)(F)(F)S(=O)(=O)[N-]S(=O)(=O)C(F)(F)F   \n",
       "\n",
       "   conc2                               smi3  conc3                      smi4  \\\n",
       "0  0.594     FS([N-]S(F)(=O)=O)(=O)=O.[Li+]  0.079                         O   \n",
       "1  0.566  FC(F)(F)COB(OCC(F)(F)F)OCC(F)(F)F  0.007  [Li+].F[P-](F)(F)(F)(F)F   \n",
       "2  0.750                                  O  0.000                         O   \n",
       "3  0.577           [Li+].F[P-](F)(F)(F)(F)F  0.092                         O   \n",
       "4  0.237                                  O  0.000                         O   \n",
       "\n",
       "   conc4 smi5  conc5 smi6  conc6    LCE  \n",
       "0  0.000    O    0.0    O    0.0  1.155  \n",
       "1  0.072    O    0.0    O    0.0  1.046  \n",
       "2  0.000    O    0.0    O    0.0  1.569  \n",
       "3  0.000    O    0.0    O    0.0  0.886  \n",
       "4  0.000    O    0.0    O    0.0  1.367  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3e92440-92bc-4984-b0f3-ccfb4b394edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a list of smiles\n",
    "train_smiles_list = pd.concat([train_df[f'smi{i}'] for i in range(1, 7)]).unique().tolist()\n",
    "test_smiles_list = pd.concat([test_df[f'smi{i}'] for i in range(1, 7)]).unique().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7e1961-6832-4740-a1bf-5ae092f6ee8f",
   "metadata": {},
   "source": [
    "### List of available models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "182d3baf-b7bb-4b73-bfe1-5c3623441e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Model Name</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SMI-TED</td>\n",
       "      <td>SMILES based encoder decoder model</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SELFIES-TED</td>\n",
       "      <td>BART model for string based SELFIES modality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Molformer</td>\n",
       "      <td>MolFormer model for string based SMILES modality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MHG-GED</td>\n",
       "      <td>Molecular hypergraph model</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Model Name                                       Description\n",
       "0      SMI-TED                SMILES based encoder decoder model\n",
       "1  SELFIES-TED      BART model for string based SELFIES modality\n",
       "2    Molformer  MolFormer model for string based SMILES modality\n",
       "3      MHG-GED                        Molecular hypergraph model"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fm4m.avail_models()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e524cf-328f-4aca-94cb-7c9830404c09",
   "metadata": {},
   "source": [
    "### Get embeddings "
   ]
  },
  {
   "attachments": {
    "b3fdaf5f-f7d9-4253-a19d-07576c3db702.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "e471796b-22b9-4125-b316-d67c18f8093e",
   "metadata": {},
   "source": [
    "![image.png](attachment:b3fdaf5f-f7d9-4253-a19d-07576c3db702.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4e2ff0d-ecf8-4bd3-b64a-a8cdfcba0a75",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/indrapriyadarsinis/.pyenv/versions/3.10.8/envs/amd/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8d12a382b7a44ea90a1aa43c9bf29a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/56 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot encode [LI+].F[B-](F)(F)OC(C(F)(F)(F))(C(F)(F)(F))C(F)(F)(F) to selfies and embedding replaced by NaN\n",
      "Cannot encode [Li+].F[P-](F)(F)(F)(F)F to selfies and embedding replaced by NaN\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e084959951dd4831b9977c140fa2dfce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/32 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot encode [Li+].F[P-](F)(F)(F)(F)F to selfies and embedding replaced by NaN\n",
      "Cannot encode CSi(C)(C)([N+]).C(F)(F)(F)S(=O)(=O)[N-]S(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F to selfies and embedding replaced by NaN\n"
     ]
    }
   ],
   "source": [
    "model_type = \"SELFIES-TED\"\n",
    "train_emb, test_emb = fm4m.get_representation(train_smiles_list,test_smiles_list, model_type, return_tensor=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11c38733-d7a7-41ef-aca8-bb28a87a27ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_emb = [np.nan if row.isna().all() else row.dropna().tolist() for _, row in train_emb.iterrows()]\n",
    "test_emb = [np.nan if row.isna().all() else row.dropna().tolist() for _, row in test_emb.iterrows()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e434d3a-5f0c-4281-945c-ee4966f4cc15",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dict = dict(zip(train_smiles_list, train_emb))\n",
    "test_dict = dict(zip(test_smiles_list, test_emb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8ef20f22-c869-4efc-80d4-d3e9f46ccc3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_with_list(value, my_dict):\n",
    "    return my_dict.get(value, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed4b3722-ada4-4f73-ab70-59e706ec61fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the smiles string with its embeddings\n",
    "df_train_emb = train_df.applymap(lambda x: replace_with_list(x, train_dict))\n",
    "df_test_emb = test_df.applymap(lambda x: replace_with_list(x, test_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "136d553b-b432-46b9-8b42-23e8c6367982",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Drop rows with NaN and reset index\n",
    "df_train_emb = df_train_emb.dropna().reset_index(drop=True)\n",
    "df_test_emb = df_test_emb.dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4fac1e2f-b16d-4520-b315-990b04a77258",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smi1</th>\n",
       "      <th>conc1</th>\n",
       "      <th>smi2</th>\n",
       "      <th>conc2</th>\n",
       "      <th>smi3</th>\n",
       "      <th>conc3</th>\n",
       "      <th>smi4</th>\n",
       "      <th>conc4</th>\n",
       "      <th>smi5</th>\n",
       "      <th>conc5</th>\n",
       "      <th>smi6</th>\n",
       "      <th>conc6</th>\n",
       "      <th>LCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[1.0849336385726929, 0.19316737353801727, 0.67...</td>\n",
       "      <td>0.327</td>\n",
       "      <td>[0.7829928994178772, -0.0945846438407898, 0.11...</td>\n",
       "      <td>0.594</td>\n",
       "      <td>[0.3032551407814026, 0.4908856451511383, 0.533...</td>\n",
       "      <td>0.079</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.8958781361579895, 0.5856224298477173, 0.356...</td>\n",
       "      <td>0.250</td>\n",
       "      <td>[0.3032551407814026, 0.4908856451511383, 0.533...</td>\n",
       "      <td>0.750</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.000</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1.1227182149887085, 0.053022246807813644, 0.2...</td>\n",
       "      <td>0.763</td>\n",
       "      <td>[-0.2496337890625, 0.28777679800987244, 0.4349...</td>\n",
       "      <td>0.237</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.000</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1.1227182149887085, 0.053022246807813644, 0.2...</td>\n",
       "      <td>0.200</td>\n",
       "      <td>[0.3811344802379608, 0.23158609867095947, -0.0...</td>\n",
       "      <td>0.600</td>\n",
       "      <td>[0.3032551407814026, 0.4908856451511383, 0.533...</td>\n",
       "      <td>0.200</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.884566605091095, 0.3425617516040802, 0.2125...</td>\n",
       "      <td>0.873</td>\n",
       "      <td>[0.3032551407814026, 0.4908856451511383, 0.533...</td>\n",
       "      <td>0.127</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.000</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                smi1  conc1  \\\n",
       "0  [1.0849336385726929, 0.19316737353801727, 0.67...  0.327   \n",
       "1  [0.8958781361579895, 0.5856224298477173, 0.356...  0.250   \n",
       "2  [1.1227182149887085, 0.053022246807813644, 0.2...  0.763   \n",
       "3  [1.1227182149887085, 0.053022246807813644, 0.2...  0.200   \n",
       "4  [0.884566605091095, 0.3425617516040802, 0.2125...  0.873   \n",
       "\n",
       "                                                smi2  conc2  \\\n",
       "0  [0.7829928994178772, -0.0945846438407898, 0.11...  0.594   \n",
       "1  [0.3032551407814026, 0.4908856451511383, 0.533...  0.750   \n",
       "2  [-0.2496337890625, 0.28777679800987244, 0.4349...  0.237   \n",
       "3  [0.3811344802379608, 0.23158609867095947, -0.0...  0.600   \n",
       "4  [0.3032551407814026, 0.4908856451511383, 0.533...  0.127   \n",
       "\n",
       "                                                smi3  conc3  \\\n",
       "0  [0.3032551407814026, 0.4908856451511383, 0.533...  0.079   \n",
       "1  [0.9481081962585449, -0.008839528076350689, 0....  0.000   \n",
       "2  [0.9481081962585449, -0.008839528076350689, 0....  0.000   \n",
       "3  [0.3032551407814026, 0.4908856451511383, 0.533...  0.200   \n",
       "4  [0.9481081962585449, -0.008839528076350689, 0....  0.000   \n",
       "\n",
       "                                                smi4  conc4  \\\n",
       "0  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "1  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "2  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "3  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "4  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "\n",
       "                                                smi5  conc5  \\\n",
       "0  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "1  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "2  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "3  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "4  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "\n",
       "                                                smi6  conc6    LCE  \n",
       "0  [0.9481081962585449, -0.008839528076350689, 0....    0.0  1.155  \n",
       "1  [0.9481081962585449, -0.008839528076350689, 0....    0.0  1.569  \n",
       "2  [0.9481081962585449, -0.008839528076350689, 0....    0.0  1.367  \n",
       "3  [0.9481081962585449, -0.008839528076350689, 0....    0.0  2.301  \n",
       "4  [0.9481081962585449, -0.008839528076350689, 0....    0.0  1.489  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of train data\n",
    "df_train_emb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4d5cf34d-8afc-49ab-8ff3-aba23919b800",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>[0.884566605091095, 0.3425617516040802, 0.2125...</td>\n",
       "      <td>0.733</td>\n",
       "      <td>[0.3032551407814026, 0.4908856451511383, 0.533...</td>\n",
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       "      <th>1</th>\n",
       "      <td>[1.1299402713775635, 0.20541700720787048, 0.44...</td>\n",
       "      <td>0.299</td>\n",
       "      <td>[0.30258023738861084, 0.26763060688972473, 0.1...</td>\n",
       "      <td>0.598</td>\n",
       "      <td>[0.3032551407814026, 0.4908856451511383, 0.533...</td>\n",
       "      <td>0.103</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1.1227182149887085, 0.053022246807813644, 0.2...</td>\n",
       "      <td>0.507</td>\n",
       "      <td>[0.4529896676540375, 0.33911606669425964, 0.01...</td>\n",
       "      <td>0.399</td>\n",
       "      <td>[0.3032551407814026, 0.4908856451511383, 0.533...</td>\n",
       "      <td>0.095</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1.0849336385726929, 0.19316737353801727, 0.67...</td>\n",
       "      <td>0.425</td>\n",
       "      <td>[0.593877375125885, 0.1412603259086609, 0.0104...</td>\n",
       "      <td>0.481</td>\n",
       "      <td>[0.3032551407814026, 0.4908856451511383, 0.533...</td>\n",
       "      <td>0.094</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.000</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9481081962585449, -0.008839528076350689, 0....</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.8958781361579895, 0.5856224298477173, 0.356...</td>\n",
       "      <td>0.359</td>\n",
       "      <td>[0.17440025508403778, 0.29212361574172974, 0.1...</td>\n",
       "      <td>0.504</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "                                                smi1  conc1  \\\n",
       "0  [0.884566605091095, 0.3425617516040802, 0.2125...  0.733   \n",
       "1  [1.1299402713775635, 0.20541700720787048, 0.44...  0.299   \n",
       "2  [1.1227182149887085, 0.053022246807813644, 0.2...  0.507   \n",
       "3  [1.0849336385726929, 0.19316737353801727, 0.67...  0.425   \n",
       "4  [0.8958781361579895, 0.5856224298477173, 0.356...  0.359   \n",
       "\n",
       "                                                smi2  conc2  \\\n",
       "0  [0.3032551407814026, 0.4908856451511383, 0.533...  0.267   \n",
       "1  [0.30258023738861084, 0.26763060688972473, 0.1...  0.598   \n",
       "2  [0.4529896676540375, 0.33911606669425964, 0.01...  0.399   \n",
       "3  [0.593877375125885, 0.1412603259086609, 0.0104...  0.481   \n",
       "4  [0.17440025508403778, 0.29212361574172974, 0.1...  0.504   \n",
       "\n",
       "                                                smi3  conc3  \\\n",
       "0  [0.9481081962585449, -0.008839528076350689, 0....  0.000   \n",
       "1  [0.3032551407814026, 0.4908856451511383, 0.533...  0.103   \n",
       "2  [0.3032551407814026, 0.4908856451511383, 0.533...  0.095   \n",
       "3  [0.3032551407814026, 0.4908856451511383, 0.533...  0.094   \n",
       "4  [-0.24963372945785522, 0.28777676820755005, 0....  0.133   \n",
       "\n",
       "                                                smi4  conc4  \\\n",
       "0  [0.9481081962585449, -0.008839528076350689, 0....  0.000   \n",
       "1  [0.9481081962585449, -0.008839528076350689, 0....  0.000   \n",
       "2  [0.9481081962585449, -0.008839528076350689, 0....  0.000   \n",
       "3  [0.9481081962585449, -0.008839528076350689, 0....  0.000   \n",
       "4  [-0.29585251212120056, 0.6863532662391663, 0.7...  0.004   \n",
       "\n",
       "                                                smi5  conc5  \\\n",
       "0  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "1  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "2  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "3  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "4  [0.9481081962585449, -0.008839528076350689, 0....    0.0   \n",
       "\n",
       "                                                smi6  conc6    LCE  \n",
       "0  [0.9481081962585449, -0.008839528076350689, 0....    0.0  1.629  \n",
       "1  [0.9481081962585449, -0.008839528076350689, 0....    0.0  2.056  \n",
       "2  [0.9481081962585449, -0.008839528076350689, 0....    0.0  2.268  \n",
       "3  [0.9481081962585449, -0.008839528076350689, 0....    0.0  1.602  \n",
       "4  [0.9481081962585449, -0.008839528076350689, 0....    0.0  2.000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of test data\n",
    "df_test_emb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc73c68-6096-4efd-8bf1-c34e1d83cef4",
   "metadata": {},
   "source": [
    "### Construct feature vector for the formulation"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "9971e381-e00f-449e-8990-461ccc8843d2",
   "metadata": {},
   "source": [
    "![image.png](attachment:770a77a4-f5de-4ba2-b34b-49d8a2e5fdd5.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cedf22b3-6571-4daa-91a4-fd56ca75f3f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct feature vector by scaling the representation by their corresponding composition and add\n",
    "def build_feature_vector(df, smi_cols, conc_cols):\n",
    "    components = [df[smi].apply(pd.Series).mul(df[conc], axis=0) for smi, conc in zip(smi_cols, conc_cols)]\n",
    "    return sum(components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99de17fa-ce29-4fea-a644-5d7520945eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of columns to process\n",
    "smi_cols = [f'smi{i}' for i in range(1, 7)]\n",
    "conc_cols = [f'conc{i}' for i in range(1, 7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "df921201-b3e4-44cf-bea8-ad1679d7e5f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train data processing\n",
    "x_train = build_feature_vector(df_train_emb, smi_cols, conc_cols)\n",
    "y_train = pd.DataFrame(df_train_emb[\"LCE\"], columns=[\"LCE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aa30d2c9-1966-4c67-8e3f-f5907adf931e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test data processing\n",
    "X_test = build_feature_vector(df_test_emb, smi_cols, conc_cols)\n",
    "y_test = pd.DataFrame(df_test_emb[\"LCE\"], columns=[\"LCE\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aebb78d1-83f7-488b-948a-4ec7b4a89b09",
   "metadata": {},
   "source": [
    "### Downstream Model : SVR Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eb54c164-945a-4341-9909-87f6bc1f7911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE score : 0.27617842231700973\n"
     ]
    }
   ],
   "source": [
    "regressor = SVR(kernel=\"rbf\", degree=3, C=5, gamma=\"scale\", epsilon=0.01)\n",
    "model = TransformedTargetRegressor(regressor=regressor,\n",
    "                                   transformer=MinMaxScaler(feature_range=(-1, 1))\n",
    "                                   ).fit(x_train, y_train)\n",
    "\n",
    "y_prob = model.predict(X_test)\n",
    "RMSE_score = mean_squared_error(y_test, y_prob, squared=False)\n",
    "print(f\"RMSE score : {RMSE_score}\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c2ca099-db32-426b-9e9a-cb76a2286fb8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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