atomsci/ddm/examples/tutorials/04_application_trained_model.ipynb

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    "# Application of a Trained Model\n",
    "\n",
    "In this tutorial we will show you how to use a trained model to make predictions for a new set of compounds. As an example, we will take the model trained in \"**Tutorial 3: Train a Simple Regression Model**\", which predicts $pK_i$ values for inhibition of SLC6A3, and apply it to the test subset compounds from the original training dataset. Since we know the actual $pK_i$ values for these compounds, we will then plot the predicted values against the actual values to see how well the model performs on this compound set. \n",
    "\n",
    "This tutorial focuses on these **[AMPL](https://github.com/ATOMScience-org/AMPL)** functions:\n",
    "\n",
    "* [predict_from_model_file](https://ampl.readthedocs.io/en/latest/pipeline.html#pipeline.predict_from_model.predict_from_model_file)\n",
    "* [plot_pred_vs_actual_from_df](https://ampl.readthedocs.io/en/latest/pipeline.html#pipeline.perf_plots.plot_pred_vs_actual_from_df)\n"
   ]
  },
  {
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     "text": [
      "Skipped loading some Jax models, missing a dependency. No module named 'haiku'\n",
      "/opt/anaconda3/envs/atom-dev/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# ignore sklearn future warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore',category=FutureWarning)\n",
    "\n",
    "from atomsci.ddm.pipeline import predict_from_model as pfm\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e40e28-2c53-4174-87a9-bb69d09b4684",
   "metadata": {},
   "source": [
    "## Creating The Test Dataset\n",
    "First, create a test set by selecting the test data from the curated dataset.\n",
    "Here we are using the pre-featurized dataset to save time."
   ]
  },
  {
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   "id": "7ce795fa-06d8-4099-9d41-8092f6c637a5",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compound_id</th>\n",
       "      <th>base_rdkit_smiles</th>\n",
       "      <th>avg_pKi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL17157</td>\n",
       "      <td>CC(C)(C)c1ccc(C(O)CCCN2CCC(C(O)(c3ccccc3)c3ccc...</td>\n",
       "      <td>6.692504</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CHEMBL3321789</td>\n",
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       "      <td>6.207608</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CHEMBL595638</td>\n",
       "      <td>CN1C2CCC1[C@@H](C(=O)OCc1cn(CCOC(=O)[C@H]3C4CC...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CHEMBL4447975</td>\n",
       "      <td>COc1cc(OC)c2c(c1)OC[C@@]1(C)NCC[C@@H]21</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CHEMBL1062</td>\n",
       "      <td>CC(=O)[C@@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@...</td>\n",
       "      <td>5.260744</td>\n",
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       "      compound_id                                  base_rdkit_smiles   avg_pKi\n",
       "3     CHEMBL17157  CC(C)(C)c1ccc(C(O)CCCN2CCC(C(O)(c3ccccc3)c3ccc...  6.692504\n",
       "7   CHEMBL3321789       OC1(c2ccc(Cl)cc2)CC2CCC(C1)N2CCCOc1ccc(F)cc1  6.207608\n",
       "13   CHEMBL595638  CN1C2CCC1[C@@H](C(=O)OCc1cn(CCOC(=O)[C@H]3C4CC...  7.795880\n",
       "28  CHEMBL4447975            COc1cc(OC)c2c(c1)OC[C@@]1(C)NCC[C@@H]21  5.000000\n",
       "41     CHEMBL1062  CC(=O)[C@@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@...  5.260744"
      ]
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     "execution_count": 2,
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     "output_type": "execute_result"
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   "source": [
    "split_file_path = 'dataset/SLC6A3_Ki_curated_train_valid_test_scaffold_c35aeaab-910c-4dcf-8f9f-04b55179aa1a.csv'\n",
    "curated_data_path = 'dataset/scaled_descriptors/SLC6A3_Ki_curated_with_rdkit_raw_descriptors.csv'\n",
    "\n",
    "split_data = pd.read_csv(split_file_path)\n",
    "\n",
    "curated_data = pd.read_csv(curated_data_path)\n",
    "\n",
    "test_ids=split_data[split_data.subset == 'test'].cmpd_id.unique()\n",
    "test_data = curated_data[curated_data.compound_id.isin(test_ids)]\n",
    "\n",
    "# show most useful columns \n",
    "test_data[['compound_id', 'base_rdkit_smiles', 'avg_pKi']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8be89936-dbb5-4f31-8e06-4627805821a6",
   "metadata": {
    "tags": []
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   "source": [
    "## Performing Predictions\n",
    "Next, load a pretrained model from a model tarball file and run \n",
    "predictions on compounds in the test set. If the original model \n",
    "`response_col` was `avg_pKi`, the returned data frame will \n",
    "contain columns `avg_pKi_actual`, `avg_pKi_pred`, and \n",
    "`avg_pKi_std`. The predictions of $pK_i$ are in the column, \n",
    "`avg_pKi_pred`. The `avg_pKi_std` column contains uncertainity estimates (standard deviations) for the predictions.\n",
    "\n",
    "Here we set the `is_featurized` parameter to true, since we're using\n",
    "the pre-featurized dataset.\n",
    "\n",
    "> **Note:** *For the purposes of this tutorial, the following model has been altered to work on every file system. In general, to run a model that was trained on a different machine, you need to provide the path to the local copy of the training dataset as an additional parameter called `external_training_data`.*"
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standardizing SMILES strings for 273 compounds.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:atomsci.ddm.utils.model_version_utils:dataset/SLC6A3_models/SLC6A3_Ki_curated_model_9ff5a924-ef49-407c-a4d4-868a1288a67e.tar.gz, 1.6.1\n",
      "INFO:atomsci.ddm.utils.model_version_utils:Version compatible check: dataset/SLC6A3_models/SLC6A3_Ki_curated_model_9ff5a924-ef49-407c-a4d4-868a1288a67e.tar.gz version = \"1.6\", AMPL version = \"1.6\"\n",
      "/Users/rwilfong/Downloads/2024_LLNL/fork_ampl/AMPL/atomsci/ddm/pipeline/transformations.py:250: RuntimeWarning: invalid value encountered in divide\n",
      "  X = np.nan_to_num((X - self.X_means) * X_weight / self.X_stds)\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compound_id</th>\n",
       "      <th>base_rdkit_smiles</th>\n",
       "      <th>avg_pKi_actual</th>\n",
       "      <th>avg_pKi_pred</th>\n",
       "      <th>avg_pKi_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL17157</td>\n",
       "      <td>CC(C)(C)c1ccc(C(O)CCCN2CCC(C(O)(c3ccccc3)c3ccc...</td>\n",
       "      <td>6.692504</td>\n",
       "      <td>7.741641</td>\n",
       "      <td>1.289527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL3321789</td>\n",
       "      <td>OC1(c2ccc(Cl)cc2)CC2CCC(C1)N2CCCOc1ccc(F)cc1</td>\n",
       "      <td>6.207608</td>\n",
       "      <td>6.607851</td>\n",
       "      <td>1.069817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL595638</td>\n",
       "      <td>CN1C2CCC1[C@@H](C(=O)OCc1cn(CCOC(=O)[C@H]3C4CC...</td>\n",
       "      <td>7.795880</td>\n",
       "      <td>6.784137</td>\n",
       "      <td>1.271238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL4447975</td>\n",
       "      <td>COc1cc(OC)c2c(c1)OC[C@@]1(C)NCC[C@@H]21</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.080245</td>\n",
       "      <td>1.321997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL1062</td>\n",
       "      <td>CC(=O)[C@@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@...</td>\n",
       "      <td>5.260744</td>\n",
       "      <td>6.304104</td>\n",
       "      <td>1.517846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     compound_id                                  base_rdkit_smiles  \\\n",
       "0    CHEMBL17157  CC(C)(C)c1ccc(C(O)CCCN2CCC(C(O)(c3ccccc3)c3ccc...   \n",
       "1  CHEMBL3321789       OC1(c2ccc(Cl)cc2)CC2CCC(C1)N2CCCOc1ccc(F)cc1   \n",
       "2   CHEMBL595638  CN1C2CCC1[C@@H](C(=O)OCc1cn(CCOC(=O)[C@H]3C4CC...   \n",
       "3  CHEMBL4447975            COc1cc(OC)c2c(c1)OC[C@@]1(C)NCC[C@@H]21   \n",
       "4     CHEMBL1062  CC(=O)[C@@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@...   \n",
       "\n",
       "   avg_pKi_actual  avg_pKi_pred  avg_pKi_std  \n",
       "0        6.692504      7.741641     1.289527  \n",
       "1        6.207608      6.607851     1.069817  \n",
       "2        7.795880      6.784137     1.271238  \n",
       "3        5.000000      6.080245     1.321997  \n",
       "4        5.260744      6.304104     1.517846  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dir = 'dataset/SLC6A3_models/SLC6A3_Ki_curated_model_9ff5a924-ef49-407c-a4d4-868a1288a67e.tar.gz'\n",
    "input_df = test_data\n",
    "id_col = 'compound_id'\n",
    "smiles_col = 'base_rdkit_smiles'\n",
    "response_col = 'avg_pKi'\n",
    "\n",
    "# loads a pretrained model from a model tarball file and runs predictions on \n",
    "# compounds in an input data frame\n",
    "pred_df = pfm.predict_from_model_file(model_path = model_dir, \n",
    "                                      input_df = test_data,\n",
    "                                      id_col = id_col ,\n",
    "                                      smiles_col = smiles_col, \n",
    "                                      response_col = response_col,\n",
    "                                      is_featurized=True)\n",
    "                                      \n",
    "\n",
    "# show most useful columns\n",
    "pred_df[['compound_id', 'base_rdkit_smiles', 'avg_pKi_actual','avg_pKi_pred', 'avg_pKi_std']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "635c5ef0-343d-4dfd-85c9-5e8214dace4b",
   "metadata": {},
   "source": [
    "## Evaluating Prediction Performance\n",
    "Then, calculate the $R^2$ score and compare it with the expected test $R^2$\n",
    "score of 0.426594, reported in **Tutorial 3, \"Train a Simple Regression Model\"**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd242dac-5d73-44f0-9aa9-74225590ce16",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.426594"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_value = pred_df['avg_pKi_actual']\n",
    "predicted_value = pred_df['avg_pKi_pred']\n",
    "r2 = np.round(r2_score(actual_value, predicted_value), 6)\n",
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42bbaac8-4748-4cae-afcd-39921a9719f6",
   "metadata": {},
   "source": [
    "## Visualizing Prediction Results\n",
    "We can visualize the results in a scatter plot of predicted values vs. actual values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5dbeadd-60d9-48c2-b094-c49b7aa47ff8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from atomsci.ddm.pipeline import perf_plots as pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ded536af-2555-4399-bef0-af9e6ecbc91b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plots predicted vs actual values from a trained regression model for a given \n",
    "# dataframe\n",
    "pp.plot_pred_vs_actual_from_df(pred_df, \n",
    "                               actual_col='avg_pKi_actual', \n",
    "                               pred_col='avg_pKi_pred', \n",
    "                               label='Prediction of Test Set');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab69461b-e58f-4755-b623-82e501308649",
   "metadata": {},
   "source": [
    "In **Tutorial 5, \"Hyperparameter Optimization\"**, we will move beyond a single model and learn to optimize model hyperparameters by training many models.\n",
    "\n",
    "If you have specific feedback about a tutorial, please complete the **[AMPL Tutorial Evaluation](https://forms.gle/pa9sHj4MHbS5zG7A6)**."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "atomsci-env-1.6.1",
   "language": "python",
   "name": "atomsci-env-1.6.1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}