notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e70fca3",
   "metadata": {},
   "source": [
    "# Example: Using Multiple Different Fingerprint Transformer\n",
    "\n",
    "In this notebook we will explore how to evaluate the performance of machine learning models depending on different fingerprint transformers (Featurization techniques). This is an example, that you easily could adapt for many different combinations of featurizers, optimizaiton and other modelling techniques.\n",
    "\n",
    "Following steps will happen:\n",
    "* Data Parsing\n",
    "* Pipeline Building\n",
    "* Training Phase\n",
    "* Analysis\n",
    "\n",
    "Authors: @VincentAlexanderScholz, @RiesBen\n",
    "\n",
    "## Imports:\n",
    "First we will import all the stuff that we will need for our work.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b705b5c9",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from rdkit.Chem import PandasTools\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from scikit_mol import fingerprints"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a4eb825",
   "metadata": {},
   "source": [
    "## Get Data:\n",
    "In this step we will check if the SLC6A4 data set is already present or needs to be downloaded.\n",
    "\n",
    "\n",
    "**WARNING:** The Dataset is a simple and very well selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34b2618a",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-11-24T09:27:56.850568Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 out of 200 SMILES failed in conversion\n"
     ]
    }
   ],
   "source": [
    "full_set = False\n",
    "\n",
    "# if not present download example data\n",
    "if full_set:\n",
    "    csv_file = \"SLC6A4_active_excape_export.csv\"\n",
    "    if not os.path.exists(csv_file):\n",
    "        import urllib.request\n",
    "\n",
    "        url = \"https://ndownloader.figshare.com/files/25747817\"\n",
    "        urllib.request.urlretrieve(url, csv_file)\n",
    "else:\n",
    "    csv_file = \"../tests/data/SLC6A4_active_excapedb_subset.csv\"\n",
    "\n",
    "# Parse Database\n",
    "data = pd.read_csv(csv_file)\n",
    "\n",
    "PandasTools.AddMoleculeColumnToFrame(data, smilesCol=\"SMILES\")\n",
    "print(f\"{data.ROMol.isna().sum()} out of {len(data)} SMILES failed in conversion\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8dba759",
   "metadata": {},
   "source": [
    "## Build Pipeline:\n",
    "In this stage we will build the Pipeline consisting of the featurization part (finger print transformers) and the model part (Ridge Regression).\n",
    "\n",
    "Note that the featurization in this section is an hyperparameter, living in `param_grid`, and the `\"fp_transformer\"` string is just a placeholder, being replaced during pipeline execution.\n",
    "\n",
    "This way we can define multiple different scenarios in `param_grid`, that allow us to rapidly explore different combinations of settings and methodologies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e06042cc",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-11-24T09:27:56.863371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'fp_transformer': [MorganFingerprintTransformer(),\n",
       "   AvalonFingerprintTransformer()],\n",
       "  'fp_transformer__fpSize': [256, 512, 1024, 2048, 4096],\n",
       "  'regressor__alpha': array([0.1  , 0.325, 0.55 , 0.775, 1.   ])},\n",
       " {'fp_transformer': [RDKitFingerprintTransformer(),\n",
       "   AtomPairFingerprintTransformer(),\n",
       "   MACCSKeysFingerprintTransformer()],\n",
       "  'regressor__alpha': array([0.1  , 0.325, 0.55 , 0.775, 1.   ])}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "regressor = Ridge()\n",
    "optimization_pipe = Pipeline(\n",
    "    [\n",
    "        (\n",
    "            \"fp_transformer\",\n",
    "            \"fp_transformer\",\n",
    "        ),  # this is a placeholder for different transformers\n",
    "        (\"regressor\", regressor),\n",
    "    ]\n",
    ")\n",
    "\n",
    "param_grid = [  # Here pass different Options and Approaches\n",
    "    {\n",
    "        \"fp_transformer\": [\n",
    "            fingerprints.MorganFingerprintTransformer(),\n",
    "            fingerprints.AvalonFingerprintTransformer(),\n",
    "        ],\n",
    "        \"fp_transformer__fpSize\": [2**x for x in range(8, 13)],\n",
    "    },\n",
    "    {\n",
    "        \"fp_transformer\": [\n",
    "            fingerprints.RDKitFingerprintTransformer(),\n",
    "            fingerprints.AtomPairFingerprintTransformer(),\n",
    "            fingerprints.MACCSKeysFingerprintTransformer(),\n",
    "        ],\n",
    "    },\n",
    "]\n",
    "\n",
    "global_options = {\n",
    "    \"regressor__alpha\": np.linspace(0.1, 1, 5),\n",
    "}\n",
    "\n",
    "[params.update(global_options) for params in param_grid]\n",
    "\n",
    "param_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521aa24a",
   "metadata": {},
   "source": [
    "## Train Model\n",
    "In this section, the combinatorial approaches are trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1cf66df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-24T09:27:56.866817Z",
     "iopub.status.busy": "2024-11-24T09:27:56.866251Z",
     "iopub.status.idle": "2024-11-24T09:28:28.265183Z",
     "shell.execute_reply": "2024-11-24T09:28:28.264595Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime: 31.39\n"
     ]
    }
   ],
   "source": [
    "# Split Data\n",
    "mol_list_train, mol_list_test, y_train, y_test = train_test_split(\n",
    "    data.ROMol, data.pXC50, random_state=0\n",
    ")\n",
    "\n",
    "# Define Search Process\n",
    "grid = GridSearchCV(optimization_pipe, n_jobs=1, param_grid=param_grid)\n",
    "\n",
    "# Train\n",
    "t0 = time()\n",
    "grid.fit(mol_list_train, y_train.values)\n",
    "t1 = time()\n",
    "\n",
    "print(f\"Runtime: {t1-t0:0.2F}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55aa1549",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "Now let's investigate our results from the training stage. Which one is the best finger print method for this data set? Which parameters are optimal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f80006f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-24T09:28:28.268630Z",
     "iopub.status.busy": "2024-11-24T09:28:28.268131Z",
     "iopub.status.idle": "2024-11-24T09:28:28.320208Z",
     "shell.execute_reply": "2024-11-24T09:28:28.319598Z"
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   },
   "outputs": [
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       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_fp_transformer</th>\n",
       "      <th>param_fp_transformer__fpSize</th>\n",
       "      <th>param_regressor__alpha</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
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       "      <td>0.001311</td>\n",
       "      <td>MorganFingerprintTransformer()</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\n",
       "      <td>0.017975</td>\n",
       "      <td>0.394682</td>\n",
       "      <td>0.524598</td>\n",
       "      <td>0.542116</td>\n",
       "      <td>0.310238</td>\n",
       "      <td>0.357922</td>\n",
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       "      <td>0.010119</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.002832</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>MorganFingerprintTransformer()</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.325</td>\n",
       "      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\n",
       "      <td>0.078758</td>\n",
       "      <td>0.449548</td>\n",
       "      <td>0.554241</td>\n",
       "      <td>0.572363</td>\n",
       "      <td>0.330543</td>\n",
       "      <td>0.397090</td>\n",
       "      <td>0.181071</td>\n",
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       "      <td>0.000429</td>\n",
       "      <td>0.003310</td>\n",
       "      <td>0.000967</td>\n",
       "      <td>MorganFingerprintTransformer()</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.550</td>\n",
       "      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\n",
       "      <td>0.128221</td>\n",
       "      <td>0.490253</td>\n",
       "      <td>0.575230</td>\n",
       "      <td>0.593237</td>\n",
       "      <td>0.344076</td>\n",
       "      <td>0.426203</td>\n",
       "      <td>0.173061</td>\n",
       "      <td>23</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.010192</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>0.002859</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>MorganFingerprintTransformer()</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.775</td>\n",
       "      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\n",
       "      <td>0.169585</td>\n",
       "      <td>0.521723</td>\n",
       "      <td>0.590890</td>\n",
       "      <td>0.608380</td>\n",
       "      <td>0.353866</td>\n",
       "      <td>0.448889</td>\n",
       "      <td>0.166100</td>\n",
       "      <td>22</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.010103</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.002868</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>MorganFingerprintTransformer()</td>\n",
       "      <td>256.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>{'fp_transformer': MorganFingerprintTransforme...</td>\n",
       "      <td>0.204831</td>\n",
       "      <td>0.546774</td>\n",
       "      <td>0.603010</td>\n",
       "      <td>0.619752</td>\n",
       "      <td>0.361324</td>\n",
       "      <td>0.467138</td>\n",
       "      <td>0.160060</td>\n",
       "      <td>21</td>\n",
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       "      <th>60</th>\n",
       "      <td>0.100754</td>\n",
       "      <td>0.006501</td>\n",
       "      <td>0.025367</td>\n",
       "      <td>0.001743</td>\n",
       "      <td>MACCSKeysFingerprintTransformer()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.100</td>\n",
       "      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\n",
       "      <td>-1.649022</td>\n",
       "      <td>-1.943461</td>\n",
       "      <td>-0.602509</td>\n",
       "      <td>-0.418328</td>\n",
       "      <td>-0.752525</td>\n",
       "      <td>-1.073169</td>\n",
       "      <td>0.606987</td>\n",
       "      <td>65</td>\n",
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       "      <th>61</th>\n",
       "      <td>0.118554</td>\n",
       "      <td>0.022440</td>\n",
       "      <td>0.036584</td>\n",
       "      <td>0.023911</td>\n",
       "      <td>MACCSKeysFingerprintTransformer()</td>\n",
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       "      <td>-0.813087</td>\n",
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       "      <td>0.003831</td>\n",
       "      <td>-0.314764</td>\n",
       "      <td>-0.456461</td>\n",
       "      <td>0.372595</td>\n",
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       "      <td>0.097552</td>\n",
       "      <td>0.001638</td>\n",
       "      <td>0.025571</td>\n",
       "      <td>0.001753</td>\n",
       "      <td>MACCSKeysFingerprintTransformer()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.550</td>\n",
       "      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\n",
       "      <td>-0.657588</td>\n",
       "      <td>-0.505782</td>\n",
       "      <td>-0.045940</td>\n",
       "      <td>0.124510</td>\n",
       "      <td>-0.171340</td>\n",
       "      <td>-0.251228</td>\n",
       "      <td>0.289700</td>\n",
       "      <td>62</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.098300</td>\n",
       "      <td>0.001744</td>\n",
       "      <td>0.025552</td>\n",
       "      <td>0.001695</td>\n",
       "      <td>MACCSKeysFingerprintTransformer()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.775</td>\n",
       "      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\n",
       "      <td>-0.468371</td>\n",
       "      <td>-0.356825</td>\n",
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       "      <td>0.182939</td>\n",
       "      <td>-0.087318</td>\n",
       "      <td>-0.138587</td>\n",
       "      <td>0.242115</td>\n",
       "      <td>59</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.098103</td>\n",
       "      <td>0.001473</td>\n",
       "      <td>0.025320</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>MACCSKeysFingerprintTransformer()</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000</td>\n",
       "      <td>{'fp_transformer': MACCSKeysFingerprintTransfo...</td>\n",
       "      <td>-0.339715</td>\n",
       "      <td>-0.266652</td>\n",
       "      <td>0.092180</td>\n",
       "      <td>0.218357</td>\n",
       "      <td>-0.028878</td>\n",
       "      <td>-0.064942</td>\n",
       "      <td>0.210919</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.011822      0.001013         0.003596        0.001311   \n",
       "1        0.010119      0.000152         0.002832        0.000070   \n",
       "2        0.010302      0.000429         0.003310        0.000967   \n",
       "3        0.010192      0.000159         0.002859        0.000089   \n",
       "4        0.010103      0.000126         0.002868        0.000119   \n",
       "..            ...           ...              ...             ...   \n",
       "60       0.100754      0.006501         0.025367        0.001743   \n",
       "61       0.118554      0.022440         0.036584        0.023911   \n",
       "62       0.097552      0.001638         0.025571        0.001753   \n",
       "63       0.098300      0.001744         0.025552        0.001695   \n",
       "64       0.098103      0.001473         0.025320        0.001673   \n",
       "\n",
       "                 param_fp_transformer  param_fp_transformer__fpSize  \\\n",
       "0      MorganFingerprintTransformer()                         256.0   \n",
       "1      MorganFingerprintTransformer()                         256.0   \n",
       "2      MorganFingerprintTransformer()                         256.0   \n",
       "3      MorganFingerprintTransformer()                         256.0   \n",
       "4      MorganFingerprintTransformer()                         256.0   \n",
       "..                                ...                           ...   \n",
       "60  MACCSKeysFingerprintTransformer()                           NaN   \n",
       "61  MACCSKeysFingerprintTransformer()                           NaN   \n",
       "62  MACCSKeysFingerprintTransformer()                           NaN   \n",
       "63  MACCSKeysFingerprintTransformer()                           NaN   \n",
       "64  MACCSKeysFingerprintTransformer()                           NaN   \n",
       "\n",
       "    param_regressor__alpha                                             params  \\\n",
       "0                    0.100  {'fp_transformer': MorganFingerprintTransforme...   \n",
       "1                    0.325  {'fp_transformer': MorganFingerprintTransforme...   \n",
       "2                    0.550  {'fp_transformer': MorganFingerprintTransforme...   \n",
       "3                    0.775  {'fp_transformer': MorganFingerprintTransforme...   \n",
       "4                    1.000  {'fp_transformer': MorganFingerprintTransforme...   \n",
       "..                     ...                                                ...   \n",
       "60                   0.100  {'fp_transformer': MACCSKeysFingerprintTransfo...   \n",
       "61                   0.325  {'fp_transformer': MACCSKeysFingerprintTransfo...   \n",
       "62                   0.550  {'fp_transformer': MACCSKeysFingerprintTransfo...   \n",
       "63                   0.775  {'fp_transformer': MACCSKeysFingerprintTransfo...   \n",
       "64                   1.000  {'fp_transformer': MACCSKeysFingerprintTransfo...   \n",
       "\n",
       "    split0_test_score  split1_test_score  split2_test_score  \\\n",
       "0            0.017975           0.394682           0.524598   \n",
       "1            0.078758           0.449548           0.554241   \n",
       "2            0.128221           0.490253           0.575230   \n",
       "3            0.169585           0.521723           0.590890   \n",
       "4            0.204831           0.546774           0.603010   \n",
       "..                ...                ...                ...   \n",
       "60          -1.649022          -1.943461          -0.602509   \n",
       "61          -0.969593          -0.813087          -0.188690   \n",
       "62          -0.657588          -0.505782          -0.045940   \n",
       "63          -0.468371          -0.356825           0.036642   \n",
       "64          -0.339715          -0.266652           0.092180   \n",
       "\n",
       "    split3_test_score  split4_test_score  mean_test_score  std_test_score  \\\n",
       "0            0.542116           0.310238         0.357922        0.190209   \n",
       "1            0.572363           0.330543         0.397090        0.181071   \n",
       "2            0.593237           0.344076         0.426203        0.173061   \n",
       "3            0.608380           0.353866         0.448889        0.166100   \n",
       "4            0.619752           0.361324         0.467138        0.160060   \n",
       "..                ...                ...              ...             ...   \n",
       "60          -0.418328          -0.752525        -1.073169        0.606987   \n",
       "61           0.003831          -0.314764        -0.456461        0.372595   \n",
       "62           0.124510          -0.171340        -0.251228        0.289700   \n",
       "63           0.182939          -0.087318        -0.138587        0.242115   \n",
       "64           0.218357          -0.028878        -0.064942        0.210919   \n",
       "\n",
       "    rank_test_score  \n",
       "0                25  \n",
       "1                24  \n",
       "2                23  \n",
       "3                22  \n",
       "4                21  \n",
       "..              ...  \n",
       "60               65  \n",
       "61               64  \n",
       "62               62  \n",
       "63               59  \n",
       "64               57  \n",
       "\n",
       "[65 rows x 16 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_training_stats = pd.DataFrame(grid.cv_results_)\n",
    "df_training_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6041579",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-24T09:28:28.324000Z",
     "iopub.status.busy": "2024-11-24T09:28:28.323259Z",
     "iopub.status.idle": "2024-11-24T09:28:28.574471Z",
     "shell.execute_reply": "2024-11-24T09:28:28.573892Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Best Fingerprint Method / Performance\n",
    "res_dict = {}\n",
    "for i, row in df_training_stats.iterrows():\n",
    "    fp_name = row[\"param_fp_transformer\"]\n",
    "    if (\n",
    "        fp_name in res_dict\n",
    "        and row[\"mean_test_score\"] > res_dict[fp_name][\"mean_test_score\"]\n",
    "    ):\n",
    "        res_dict[fp_name] = row.to_dict()\n",
    "    elif not fp_name in res_dict:\n",
    "        res_dict[fp_name] = row.to_dict()\n",
    "\n",
    "df = pd.DataFrame(list(res_dict.values()))\n",
    "df = df.sort_values(by=\"mean_test_score\")\n",
    "\n",
    "# plot test score vs. approach\n",
    "plt.figure(figsize=[14, 5])\n",
    "plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\n",
    "plt.xticks(range(len(df)), df.param_fp_transformer, rotation=90, fontsize=14)\n",
    "plt.ylabel(\"mean score\", fontsize=14)\n",
    "plt.title(\"Best Model of Fingerprint Transformer Type\", fontsize=18)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3ee14366",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-24T09:28:28.576983Z",
     "iopub.status.busy": "2024-11-24T09:28:28.576761Z",
     "iopub.status.idle": "2024-11-24T09:28:28.913143Z",
     "shell.execute_reply": "2024-11-24T09:28:28.912512Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ftb64Whcd62qRIkXM1KlTre393r17TY8ePUxgYKD1oddVv7zvvvus/c2sWbOsdfHcuXPmm2++MVWrVjWSTPfu3TN9XVfajqdf5gEBAebuu++2lvnZs2fN+++/b30gS/9BzcGxTXXsuwYPHmz16fPnzzt9QMqvfXNW+6KsZDckutIy3Ldvn+nVq5f59ttvrWHGXNpmTJ8+3ZQrV85IMk8//XSGeeT2td90003W9mzt2rXWMUlycrL5+++/zeuvv25effXVDNOl/zLJldOnT5sqVaoYSaZ8+fJmwYIF1vZyy5Yt1rofFBTkMrzKzr40/WsvUaKEuemmm8wff/xhjLm07r399tvWB+4XX3zRhIWFmR49eljr1D///GOGDh1qPceiRYsy1PHjjz8au91u/P39zaBBg8zu3btNWlqaSUtLM9u2bTN33323Fbjs2bPHadrsbjOzw1FjWFiYqVSpkvnxxx+t9+qXX34x1113nVXHvn37MkzviW3BlVwpJDp48KB55plnrDaffvpplssiq+2Aqy9ZHapVq2YkmZtvvtn8/vvv1vBz586ZrVu3mlGjRmWoL7vPfbkPPvjAmm7OnDlO43JzrAFkFyERkIn0H+qzSvq3bt1qTZOdHVCtWrVcfqv37bffWm0u31GPGTPGOmDYvn17hmmPHj3q9E3I5Tui0aNHW8GH44PG5b766isjXfoW5cKFC07jHM+b2UGEg+PgL7MPUsb874CjZMmS5ty5c07jHAcUksy3336b6XyyExJldRDq+Na5evXqGcZ5OyS60jK+ktdee81IMjfddFOGcblZNr/88osVkFy+flzJlUIixwfa6tWrO324d9i0aZN1ZsFrr73mNC79utCsWbMMAYVD+tfuCEXSu3jxovXBRJIZO3ZshjaJiYnWmWb//ve/ncbt3r3b+Pn5mSJFimR6NuDp06etb8e//vprp3Hp+87bb7/tcvrsuvxgul+/fkaSGT58uNUmLS3NREdHG0lm9erVxpisQ6LatWtbHxZdLeP026+5c+c6jUu/7O+4445M63Z86KxTp44VaqT3008/Wc/jKiS6kvzqG8Y4r4fh4eGZ7i8+/PBDa5rshERBQUFOoafDkSNHrDMyPvnkE6dxK1eutGq5fD015lL42rZt20w/dK1YscL6AJL+bL/09u3bZ/WFzZs3O43L7nY8/TKPiYlx+WHmo48+stqsW7fOaVz6bWpcXFym8zEm//bN+R0SXWkZXsn69eut7fbl+9vcvnbHGTFr1qzJUU1XColefvllI106SyP9B3GH06dPW9uuLl26ZBifnX1p+tdet25dl9ub+++/32rToUMHl2c/t27d2kgyDz74oNPw1NRUU6NGDSPJfPDBBy5rMMaYbt26GUnmySefdBqe3W1mdjieJzAw0OXZuAkJCaZkyZJGknnsscecxnlqW3Al6d+vy89ITn+2aOvWrc2XX36Z6fNkZzuQWZCTkJBgtcnJlzbuhEQ//vijdcwzcuTIDM+Xm2MNILu4JhGQDY7ba7p6XOn36Jd75plnXN7qvHPnztZtcB13/3CYO3euJKlHjx6qXr16hmkjIiL06KOPZjpPx7VW4uLiMr0FZ/fu3RUaGqpjx45p48aNLtvcf//9qlChQqbzcShSpIieffZZl+OGDx8uSTpx4oQWLVrksk3dunXVtWvXK87nSl588UWXw2+77TZJ0o4dOzK9oK+3ZHcZZ6ZLly6SpLVr1yo1NTXTdjldNo5bwqakpOj48eNu13e5U6dO6YcffpAkPffccy5vUd2wYUPdcccdki7dPjkzzz33nPz8/K44z+effz7DMD8/P+uil8HBwXrqqacytAkNDbXuEvbbb785jZsxY4ZSU1PVqVMn1a9f3+V8ixcvru7du0uS9ZovFx4erocffviKryEnHnjgAUmXrgFijJF06RoX8fHxqlmzplq1apXl9L/99pv+/PNPSZfWG1fLuGvXrmrWrJmkrN+jIUOGuBx+4sQJ/fTTT5IuvY9BQUEZ2rRt21atW7fOstas5FffuNzJkycz3V+kv8ZJdtx1110u7wBVunTpTNdFx/4iOjpavXr1yjCt3W7P9DVK/9tf9OrVSxUrVnTZpkKFCtb1eDJbl3OyHX/xxRdlt2c8JI2NjbW2h7Nnz3Y5rd1u1+DBg7M1n6y4s2/Ob7ndFzZp0kRlypRRUlKStmzZkmk7d167Y59w6NAht+tzxXHdt7vuukvXXntthvHFixfXoEGDJEnff/+9EhMTXT5PdvelTz/9tMvtTceOHa3/P//88y6vgehoc3kfXLFihbZv366IiAj169cv03n37t1bUuZ9SMp8m5lTd999t2rXrp1heJkyZfTII49IUoZr7nljW3Al6W95n5CQ4LQtPnr0qA4cOGDt5/JS8eLFrW1UXq/z6f3xxx+6++67dfHiRd17770aMWKE0/i8OtYAroSQCMgGc+msO5cPx4Xysqt58+Yuh/v7+6t06dKSLn1gckhJSdEff/whSYqJicn0eTO7he6BAwes21o/+OCDioqKcvkoW7asdaG7zG6Dff3112f94v5fkyZNFBoa6nJcjRo1rAO3DRs25Go+WSlZsqTLQE2SypUrZ/0/Ly9Emxey89oTEhI0YsQItWzZUqVKlZK/v791gcM6depIunQR38xemzvLplq1aqpVq5YuXLig5s2b65VXXtGWLVuy/LCdHZs2bbIO6Nq3b59puw4dOki6dDCeWTCbnWVXsmRJVatWzeW4yMhISVKdOnVUtGjRLNtcvmxXr14tSfrxxx8z7WNRUVGaPn26pMz7WNOmTa0PZXmlZcuWqlWrlvbs2WNdtDgnF6x29FN/f/8st0GO9yizfl2kSBE1atTI5bjNmzdb64E72zkHb/SNyy1dujTT/YWr8DErme0v0teTfn8hXepTknTjjTe6/GArXeor/v7+Lsc51uWpU6dmuS4vXrxYUu73F/7+/pmGf3a73XrPM1uvqlevrjJlymRrXlnJ6b7ZE7KzDFNSUjR58mTdfPPNKleunIKCgpwuenvkyBFJcrqpwOXcee233nqrJKlPnz565plntHz58lx/6ZKSkmIFLtnZH6SlpVnr++Wyu/45wu3LObb10qXtclZtMtsfJCYmqly5cpn2of79+0vKvA9ltc3MqXbt2l1x3PHjx7V79+4Mr8NT24LsmD59utM29eLFi9q/f78mT56shIQEDRw4UPfee2+eB0VFihSxvkjq1KmThg8frl9++SXTi3a7IyEhQV26dFFiYqJatWpl7afTy6tjDeBKXB8hAMg3xYsXz3Sc46A9/YfgEydOWB/E039IuVz58uVdDj948KD1/2PHjmWrxswO9LJ7IJ5ZLenH79+/3zp4dXc+WcnOcpaU4zPB8tuVXvvatWt1yy236NSpU9Ywxx1TbDabUlNTrfc5KSlJERERGZ7DnWXj5+en2bNn6/bbb9fu3bv1/PPP6/nnn1dISIhatWqlO+64Q3369HF5JlBW0q8DWa03jmDx4sWLOnHihNMBvEN21pvsvPac9lHpf/0sKSkpW2eL5LaP5VRsbKwGDx6s6dOnq1mzZvrqq6/k5+dnfZOdFcd7FBER4fIbdwfHe5RZvy5VqpTLs0WkS98AO7iznZO81zfykzvromNZZrUcg4KCFBERocOHD2cY51iXT58+neldBdPL7bp8pfXK8Z7n5/5Ccm9Z57crvbYjR46offv2Tmf5BAcHKyIiwjrj7+jRo0pLS8tyu+TOa3/11Ve1Y8cOLV26VBMmTNCECRPk5+enBg0aqEuXLnrooYeueCxwufTHO9nZH0i5Xy8ye+3p+/uV2mS2P7hw4YISEhKuWMO5c+dcDs9qm5lTWS3P9OOOHDli3e3L09sCd/j5+al8+fJ6+OGHVa5cOXXr1k2zZ89W586ds7V/y4mPPvpI3bp106+//qoxY8ZozJgxCgwMVNOmTXXbbbfpwQcfVMmSJd167nPnzum2227Tnj17VKVKFc2bN8/ldjGvjjWAK+FMIqAAyexb4aykP9Pjzz//zPKsKMcjs9ttZuenPHnBU/PxRVm99osXL6pnz546deqUGjRooO+++06nT5/WP//8o4SEBB0+fFg///yz1T6vv0mrX7++tm3bpi+//FIPPfSQrr32Wp07d06LFy/WY489plq1ann85xjpeXO9cfSzwYMHZ6uPZXbr3/x6Dffff7/8/Pz09ddfa/LkyTp37pw6deqksmXL5sv8XMnua3NnO+ftvuGL3FmO0v/W5ffffz9b6/Llty53YH+Re1d6bU8//bR+//13lSpVStOmTdOhQ4d07tw5HT16VIcPH9bhw4etsDCv1/kSJUrop59+0sqVKzVo0CDr7LSNGzdq9OjRqlGjRpY/Pc1vvrA/aN68ebb6UGbvjbfX7YK2LejatasVSH366ad5/vyVKlXSpk2btHDhQg0cOFCNGzdWWlqaVq9erUGDBql69erWz6Zzwhij3r1765dfflFYWJjmz59vncF3ubw61gCuhJAI8HElS5a0drDpzwq63IEDB1wOj4qKsv7vqdNOM6vl8vH5+e1SYbR27Vrt2bNHfn5+mj9/vjp37pzhG05XZwbkpcDAQN1xxx364IMP9Pvvv+vo0aOaPHmySpYsqX379qlPnz45er7060BWP4dwjPP393f7m7r85Ohnvnpqd9myZdWpUyedO3dOw4YNk5S9n5pJ/3uPjh07puTk5EzbOd4jd/p1+gNid7ZzvtA3fIVjWWa1HJOTkzM9s9TT6/KxY8ey/MkG+wvXLly4oK+++kqS9O677yo2NtZpfy/J6ey5/HLDDTfolVde0apVq3Tq1Cl98803uu6663Tu3Dk98MAD2TqTxiH98U529geSb64Xvrg/yOq4LP249MvTF1/HlVSuXFmSnH42l5fsdrs6duyot956Sxs2bNCJEyc0a9YsVapUSSdPntS9996b45+gvfDCC/riiy/k5+enOXPmWD+NdqUgvic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",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Best Fingerprint Method / Performance\n",
    "from collections import defaultdict\n",
    "\n",
    "res_dict = defaultdict(list)\n",
    "for i, row in df_training_stats.iterrows():\n",
    "    fp_name = row[\"param_fp_transformer\"]\n",
    "    if \"Morgan\" in str(fp_name):\n",
    "        res_dict[fp_name].append(row)\n",
    "\n",
    "for fp_type, rows in res_dict.items():\n",
    "    df = pd.DataFrame(rows)\n",
    "    df = df.sort_values(by=\"mean_test_score\")\n",
    "\n",
    "    # plot test score vs. approach\n",
    "    xlabels = map(\n",
    "        lambda x: \"_\".join(x),\n",
    "        zip(\n",
    "            df.param_fp_transformer__fpSize.astype(str),\n",
    "            df.param_regressor__alpha.astype(str),\n",
    "        ),\n",
    "    )\n",
    "\n",
    "    plt.figure(figsize=[14, 5])\n",
    "    plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\n",
    "    plt.xticks(range(len(df)), xlabels, rotation=90, fontsize=14)\n",
    "    plt.ylabel(\"mean score\", fontsize=14)\n",
    "    plt.xlabel(\"bitsize_alpha\", fontsize=14)\n",
    "\n",
    "    plt.title(\n",
    "        \"Fingerprint Transformer \" + str(fp_type).split(\"(\")[0] + \" per Bitsize\",\n",
    "        fontsize=18,\n",
    "    )\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20f7e139",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-24T09:28:28.915675Z",
     "iopub.status.busy": "2024-11-24T09:28:28.915430Z",
     "iopub.status.idle": "2024-11-24T09:28:29.314170Z",
     "shell.execute_reply": "2024-11-24T09:28:29.313570Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ALL test score vs. approach\n",
    "df = df_training_stats.sort_values(by=\"mean_test_score\")\n",
    "\n",
    "plt.figure(figsize=[16, 9])\n",
    "plt.bar(range(len(df)), df.mean_test_score, yerr=df.std_test_score)\n",
    "plt.ylabel(\"mean score\", fontsize=14)\n",
    "plt.xticks(range(len(df))[::5], df.param_fp_transformer[::5], rotation=90, fontsize=14)\n",
    "plt.title(\"test score vs. approach\", fontsize=18)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f407671c",
   "metadata": {},
   "source": [
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  }
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