{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick Start\n",
    "\n",
    "This quick start tutorial will demonstrate the basic usage of dnamite. For more detailed usage see the user guides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why Use dnamite?\n",
    "\n",
    "Given a set of $p$ features $X$, dnamite trains additive models with the form $f(X) = \\sum_j f_j (X_j)$. \n",
    "Such additive models maintain similar structure to linear models but allow each feature function (also known as *shape function*) $f_j$ to be nonlinear thus improving predictive accuracy.\n",
    "By maintaining additive structure, a trained dnamite model can directly describe its predictions via shape functions, and can summarize the importance of each feature via feature importance scores. \n",
    "Therefore, dnamite is suitable when both accuracy and interpretability are important.\n",
    "For more details see the [Why dnamite User Guide](https://dnamite.readthedocs.io/en/latest/notebooks/user_guides/why_dnamite.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression\n",
    "\n",
    "We'll start by importing some packages and reading in the California Housing dataset, a standard regression dataset. The task is to predict the median house value for a given district in California.\n",
    "No data preprocessing is requires as dnamite can handle missing values and categorical features natively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "data = fetch_california_housing(as_frame=True)\n",
    "X, y = data[\"data\"], data[\"target\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fit a dnamite model, we use `DNAMiteRegressor`. Binary classification is very similar but with `DNAMiteBinaryClassifier`. The only required input parameter is `n_features`, which should be set to the number of features in our training dataset. We pass two additional optional parameters: 1) `device`, which allows for GPU training if available, and 2) `num_pairs`, which asks the model to include a set number of pairwise interaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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: black;\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: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\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: 1ex;\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>DNAMiteRegressor(random_state=672)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DNAMiteRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DNAMiteRegressor(random_state=672)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DNAMiteRegressor(random_state=672)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dnamite.models import DNAMiteRegressor\n",
    "\n",
    "model = DNAMiteRegressor()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first check that our fitted model has reasonable predictive accuracy by comparing to a black-box ML model from scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DNAMite RMSE: 0.578846313081703\n",
      "HistGBR RMSE: 0.474020920829243\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(X_test)\n",
    "print(f\"DNAMite RMSE: {np.sqrt(np.mean((preds - y_test)**2))}\")\n",
    "\n",
    "# Compare to an sklearn model\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "gbr = HistGradientBoostingRegressor()\n",
    "gbr.fit(X_train, y_train)\n",
    "preds = gbr.predict(X_test)\n",
    "print(f\"HistGBR RMSE: {np.sqrt(np.mean((preds - y_test)**2))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now start interpreting our model. First we can look at the top feature importances from the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_feature_importances()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Latitude and longitude are important terms in the model, which makes sense for predicting house prices in a state like California. The median income of the district is also an important predictor.\n",
    "\n",
    "Now we can plot shape functions for some of the more important terms in the model. \n",
    "Since the trained model is additive, these plots directly describe how each feature contributes to the final prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_shape_function([\"MedInc\", \"AveOccup\", \"AveRooms\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, median income and average number of rooms are positively correlated with house price. Meanwhile, it's perhaps less expected that average number of occupants is negatively correlated with house price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also add feature interactions to dnamite models. Setting the `pairs_list` parameter in the fit functions sets a fixed list of interactions to add to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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: 1ex;\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-2 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-2 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-2 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-2 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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DNAMiteRegressor(random_state=344)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DNAMiteRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DNAMiteRegressor(random_state=344)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DNAMiteRegressor(random_state=344)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DNAMiteRegressor()\n",
    "model.fit(X_train, y_train, pairs_list=[[\"Latitude\", \"Longitude\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then visualize the fitted interaction using a heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_pair_shape_function(\"Latitude\", \"Longitude\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The latitude/latitude interaction plot shows a few spots of lower and higher median house values. For example, the lightest spot around (34, -118.5) corresponds to Los Angeles. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "jss (3.9.6)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}