Showing an error when calling molpipeline.experimental.explainability.SHAPTreeExplainer

_molpipeline_shap_error.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "914d5607-4d80-4b07-b823-02795ef98423",
   "metadata": {},
   "outputs": [],
   "source": [
    "from molpipeline import Pipeline\n",
    "from molpipeline.mol2mol import (\n",
    "    ElementFilter,\n",
    "    SaltRemover,\n",
    ")\n",
    "from molpipeline.any2mol import AutoToMol\n",
    "from molpipeline.mol2any import MolToMorganFP\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from molpipeline.experimental.explainability import SHAPTreeExplainer\n",
    "from molpipeline.experimental.explainability import (\n",
    "    structure_heatmap_shap,\n",
    ")\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4998386c-a17b-4f70-8876-7032f4ce4c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"biogen_logS.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb0bac2c-2a02-4a66-9c0a-ee9f449f9641",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f659cc77-34a1-4835-8f35-8e9c0b67486e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.SMILES\n",
    "y = train.logS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a7d635db-5cf9-4f70-85bb-0242bb0176c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "      (\"auto2mol\", AutoToMol()),                                     # reading molecules\n",
    "      (\"morgan2_2048\", MolToMorganFP(n_bits=2048, radius=2)),        # fingerprints and featurization\n",
    "      (\"RandomForestRegressor\", RandomForestRegressor())             # machine learning model\n",
    "    ],\n",
    "    n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b259f651-3361-4b18-8f07-d5e43aa54636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(n_jobs=4,\n",
       "         steps=[(&#x27;auto2mol&#x27;, AutoToMol()), (&#x27;morgan2_2048&#x27;, MolToMorganFP()),\n",
       "                (&#x27;RandomForestRegressor&#x27;, RandomForestRegressor())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(n_jobs=4,\n",
       "         steps=[(&#x27;auto2mol&#x27;, AutoToMol()), (&#x27;morgan2_2048&#x27;, MolToMorganFP()),\n",
       "                (&#x27;RandomForestRegressor&#x27;, RandomForestRegressor())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>AutoToMol</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>AutoToMol()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MolToMorganFP</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>MolToMorganFP()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(n_jobs=4,\n",
       "         steps=[('auto2mol', AutoToMol()), ('morgan2_2048', MolToMorganFP()),\n",
       "                ('RandomForestRegressor', RandomForestRegressor())])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X=X,y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "29181115-d444-4f5f-a61d-bc929aedb34f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pipeline.predict(test.SMILES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "819da5c8-fddc-4e93-ae83-6309ab8b5b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='logS'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=test.logS,y=pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a97f81d3-abef-4dde-b907-3c5dfb96d237",
   "metadata": {},
   "outputs": [
    {
     "ename": "ExplainerError",
     "evalue": "Additivity check failed in TreeExplainer! Please ensure the data matrix you passed to the explainer is the same shape that the model was trained on. If your data shape is correct then please report this on GitHub. Consider retrying with the feature_perturbation='interventional' option. This check failed because for one of the samples the sum of the SHAP values was -9711888870972.101562, while the model output was 1.417868. If this difference is acceptable you can set check_additivity=False to disable this check.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mExplainerError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m explainer \u001b[38;5;241m=\u001b[39m SHAPTreeExplainer(pipeline)\n\u001b[0;32m----> 2\u001b[0m explanations \u001b[38;5;241m=\u001b[39m \u001b[43mexplainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexplain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniforge3/envs/rdkit_2025_01/lib/python3.11/site-packages/molpipeline/experimental/explainability/explainer.py:308\u001b[0m, in \u001b[0;36mSHAPExplainerAdapter.explain\u001b[0;34m(self, X, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m    307\u001b[0m \u001b[38;5;66;03m# compute the shap values for the features\u001b[39;00m\n\u001b[0;32m--> 308\u001b[0m feature_weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexplainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshap_values\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeature_vector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    309\u001b[0m feature_weights \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(feature_weights)\u001b[38;5;241m.\u001b[39msqueeze()\n\u001b[1;32m    311\u001b[0m atom_weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/miniforge3/envs/rdkit_2025_01/lib/python3.11/site-packages/shap/explainers/_tree.py:633\u001b[0m, in \u001b[0;36mTreeExplainer.shap_values\u001b[0;34m(self, X, y, tree_limit, approximate, check_additivity, from_call)\u001b[0m\n\u001b[1;32m    631\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_shap_output(phi, flat_output)\n\u001b[1;32m    632\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_additivity \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mmodel_output \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 633\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massert_additivity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    635\u001b[0m \u001b[38;5;66;03m# This statements handles the case of multiple outputs\u001b[39;00m\n\u001b[1;32m    636\u001b[0m \u001b[38;5;66;03m# e.g. a multi-class classification problem, multi-target regression problem\u001b[39;00m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;66;03m# in this case the output shape corresponds to [num_samples, num_features, num_outputs]\u001b[39;00m\n\u001b[1;32m    638\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mlist\u001b[39m):\n",
      "File \u001b[0;32m~/miniforge3/envs/rdkit_2025_01/lib/python3.11/site-packages/shap/explainers/_tree.py:817\u001b[0m, in \u001b[0;36mTreeExplainer.assert_additivity\u001b[0;34m(self, phi, model_output)\u001b[0m\n\u001b[1;32m    815\u001b[0m         check_sum(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexpected_value[i] \u001b[38;5;241m+\u001b[39m phi[i]\u001b[38;5;241m.\u001b[39msum(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m), model_output[:, i])\n\u001b[1;32m    816\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 817\u001b[0m     \u001b[43mcheck_sum\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexpected_value\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mphi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_output\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniforge3/envs/rdkit_2025_01/lib/python3.11/site-packages/shap/explainers/_tree.py:811\u001b[0m, in \u001b[0;36mTreeExplainer.assert_additivity.<locals>.check_sum\u001b[0;34m(sum_val, model_output)\u001b[0m\n\u001b[1;32m    805\u001b[0m     err_msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Consider retrying with the feature_perturbation=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minterventional\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m option.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    806\u001b[0m err_msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    807\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m This check failed because for one of the samples the sum of the SHAP values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    808\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m was \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_val[ind]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124mf\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, while the model output was \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_output[ind]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124mf\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. If this\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    809\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m difference is acceptable you can set check_additivity=False to disable this check.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    810\u001b[0m )\n\u001b[0;32m--> 811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ExplainerError(err_msg)\n",
      "\u001b[0;31mExplainerError\u001b[0m: Additivity check failed in TreeExplainer! Please ensure the data matrix you passed to the explainer is the same shape that the model was trained on. If your data shape is correct then please report this on GitHub. Consider retrying with the feature_perturbation='interventional' option. This check failed because for one of the samples the sum of the SHAP values was -9711888870972.101562, while the model output was 1.417868. If this difference is acceptable you can set check_additivity=False to disable this check."
     ]
    }
   ],
   "source": [
    "explainer = SHAPTreeExplainer(pipeline)\n",
    "explanations = explainer.explain(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ee68cdd-f7f9-4a4b-b5f3-12179a8c1318",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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biogen_logS.csv