16. Machine Learning Explainability/tutorial/03. partial-plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1783e318",
   "metadata": {
    "papermill": {
     "duration": 0.011439,
     "end_time": "2021-11-09T00:04:45.709146",
     "exception": false,
     "start_time": "2021-11-09T00:04:45.697707",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Partial Dependence Plots\n",
    "\n",
    "While feature importance shows what variables most affect predictions, partial dependence plots show *how* a feature affects predictions.\n",
    "\n",
    "This is useful to answer questions like:\n",
    "\n",
    "* Controlling for all other house features, what impact do longitude and latitude have on home prices? To restate this, how would similarly sized houses be priced in different areas?\n",
    "\n",
    "* Are predicted health differences between two groups due to differences in their diets, or due to some other factor?\n",
    "\n",
    "If you are familiar with linear or logistic regression models, partial dependence plots can be interpreted similarly to the coefficients in those models.  Though, partial dependence plots on sophisticated models can capture more complex patterns than coefficients from simple models.  If you aren't familiar with linear or logistic regressions, don't worry about this comparison.\n",
    "\n",
    "We will show a couple examples, explain the interpretation of these plots, and then review the code to create these plots.\n",
    "\n",
    "# How it Works\n",
    "\n",
    "Like permutation importance, **partial dependence plots are calculated after a model has been fit.**  The model is fit on real data that has not been artificially manipulated in any way.  \n",
    "\n",
    "In our soccer example, teams may differ in many ways. How many passes they made, shots they took, goals they scored, etc. At first glance, it seems difficult to disentangle the effect of these features.\n",
    "\n",
    "To see how partial plots separate out the effect of each feature, we start by considering a single row of data. For example, that row of data might represent a team that had the ball 50% of the time, made 100 passes, took 10 shots and scored 1 goal.\n",
    "\n",
    "We will use the fitted model to predict our outcome (probability their player won \"man of the match\"). But we **repeatedly alter the value for one variable** to make a series of predictions.  We could predict the outcome if the team had the ball only 40% of the time. We then predict with them having the ball 50% of the time.  Then predict again for 60%.  And so on.  We trace out predicted outcomes (on the vertical axis) as we move from small values of ball possession to large values (on the horizontal axis).\n",
    "\n",
    "In this description, we used only a single row of data.  Interactions between features may cause the plot for a single row to be atypical.  So, we repeat that mental experiment with multiple rows from the original dataset, and we plot the average predicted outcome on the vertical axis.  \n",
    "\n",
    "# Code Example\n",
    "\n",
    "Model building isn't our focus, so we won't focus on the data exploration or model building code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f6fa199",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-09T00:04:45.735590Z",
     "iopub.status.busy": "2021-11-09T00:04:45.734334Z",
     "iopub.status.idle": "2021-11-09T00:04:47.075835Z",
     "shell.execute_reply": "2021-11-09T00:04:47.075081Z"
    },
    "papermill": {
     "duration": 1.356403,
     "end_time": "2021-11-09T00:04:47.076037",
     "exception": false,
     "start_time": "2021-11-09T00:04:45.719634",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "data = pd.read_csv('../input/fifa-2018-match-statistics/FIFA 2018 Statistics.csv')\n",
    "y = (data['Man of the Match'] == \"Yes\")  # Convert from string \"Yes\"/\"No\" to binary\n",
    "feature_names = [i for i in data.columns if data[i].dtype in [np.int64]]\n",
    "X = data[feature_names]\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)\n",
    "tree_model = DecisionTreeClassifier(random_state=0, max_depth=5, min_samples_split=5).fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c046156",
   "metadata": {
    "papermill": {
     "duration": 0.012385,
     "end_time": "2021-11-09T00:04:47.099146",
     "exception": false,
     "start_time": "2021-11-09T00:04:47.086761",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Our first example uses a decision tree, which you can see below. In practice, you'll use more sophistated models for real-world applications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5202351",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-09T00:04:47.125337Z",
     "iopub.status.busy": "2021-11-09T00:04:47.124578Z",
     "iopub.status.idle": "2021-11-09T00:04:48.075319Z",
     "shell.execute_reply": "2021-11-09T00:04:48.075850Z"
    },
    "papermill": {
     "duration": 0.966528,
     "end_time": "2021-11-09T00:04:48.076059",
     "exception": false,
     "start_time": "2021-11-09T00:04:47.109531",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.49.0 (20210829.0051)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"982pt\" height=\"581pt\"\n",
       " viewBox=\"0.00 0.00 982.00 581.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 577)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-577 978,-577 978,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"455,-573 297,-573 297,-505 455,-505 455,-573\"/>\n",
       "<text text-anchor=\"middle\" x=\"376\" y=\"-557.8\" font-family=\"Times,serif\" font-size=\"14.00\">Goal Scored &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"376\" y=\"-542.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"376\" y=\"-527.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 96</text>\n",
       "<text text-anchor=\"middle\" x=\"376\" y=\"-512.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [48, 48]</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"366.5,-469 223.5,-469 223.5,-401 366.5,-401 366.5,-469\"/>\n",
       "<text text-anchor=\"middle\" x=\"295\" y=\"-453.8\" font-family=\"Times,serif\" font-size=\"14.00\">On&#45;Target &lt;= 7.5</text>\n",
       "<text text-anchor=\"middle\" x=\"295\" y=\"-438.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.142</text>\n",
       "<text text-anchor=\"middle\" x=\"295\" y=\"-423.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 26</text>\n",
       "<text text-anchor=\"middle\" x=\"295\" y=\"-408.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [24, 2]</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M349.7,-504.88C342.7,-496.07 335.05,-486.43 327.75,-477.24\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"330.4,-474.95 321.44,-469.3 324.92,-479.31 330.4,-474.95\"/>\n",
       "<text text-anchor=\"middle\" x=\"318.59\" y=\"-490.44\" font-family=\"Times,serif\" font-size=\"14.00\">True</text>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>6</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"531.5,-469 384.5,-469 384.5,-401 531.5,-401 531.5,-469\"/>\n",
       "<text text-anchor=\"middle\" x=\"458\" y=\"-453.8\" font-family=\"Times,serif\" font-size=\"14.00\">Attempts &lt;= 24.0</text>\n",
       "<text text-anchor=\"middle\" x=\"458\" y=\"-438.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.451</text>\n",
       "<text text-anchor=\"middle\" x=\"458\" y=\"-423.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 70</text>\n",
       "<text text-anchor=\"middle\" x=\"458\" y=\"-408.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [24, 46]</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>0&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M402.62,-504.88C409.71,-496.07 417.46,-486.43 424.85,-477.24\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"427.69,-479.29 431.23,-469.3 422.24,-474.9 427.69,-479.29\"/>\n",
       "<text text-anchor=\"middle\" x=\"433.93\" y=\"-490.45\" font-family=\"Times,serif\" font-size=\"14.00\">False</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>2</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"226,-365 88,-365 88,-297 226,-297 226,-365\"/>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\">Passes &lt;= 634.0</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-334.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.077</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-319.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 25</text>\n",
       "<text text-anchor=\"middle\" x=\"157\" y=\"-304.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [24, 1]</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>1&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M250.2,-400.88C237.42,-391.44 223.38,-381.06 210.16,-371.29\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"212.17,-368.43 202.05,-365.3 208.01,-374.06 212.17,-368.43\"/>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>5</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"357.5,-357.5 244.5,-357.5 244.5,-304.5 357.5,-304.5 357.5,-357.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"301\" y=\"-342.3\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"301\" y=\"-327.3\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"middle\" x=\"301\" y=\"-312.3\" font-family=\"Times,serif\" font-size=\"14.00\">value = [0, 1]</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>1&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M296.95,-400.88C297.58,-390.22 298.27,-378.35 298.91,-367.52\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"302.41,-367.71 299.5,-357.52 295.42,-367.3 302.41,-367.71\"/>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>3</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"122,-253.5 0,-253.5 0,-200.5 122,-200.5 122,-253.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"61\" y=\"-238.3\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"61\" y=\"-223.3\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 23</text>\n",
       "<text text-anchor=\"middle\" x=\"61\" y=\"-208.3\" font-family=\"Times,serif\" font-size=\"14.00\">value = [23, 0]</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>2&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M125.83,-296.88C114.96,-285.34 102.78,-272.39 91.93,-260.86\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"94.42,-258.4 85.02,-253.52 89.32,-263.2 94.42,-258.4\"/>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>4</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"253.5,-253.5 140.5,-253.5 140.5,-200.5 253.5,-200.5 253.5,-253.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"197\" y=\"-238.3\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"197\" y=\"-223.3\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"middle\" x=\"197\" y=\"-208.3\" font-family=\"Times,serif\" font-size=\"14.00\">value = [1, 1]</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>2&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M169.99,-296.88C174.26,-286 179.01,-273.86 183.33,-262.85\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"186.6,-264.11 186.99,-253.52 180.08,-261.55 186.6,-264.11\"/>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>7</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"517.5,-365 386.5,-365 386.5,-297 517.5,-297 517.5,-365\"/>\n",
       "<text text-anchor=\"middle\" x=\"452\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\">Corners &lt;= 4.5</text>\n",
       "<text text-anchor=\"middle\" x=\"452\" y=\"-334.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.43</text>\n",
       "<text text-anchor=\"middle\" x=\"452\" y=\"-319.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 67</text>\n",
       "<text text-anchor=\"middle\" x=\"452\" y=\"-304.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [21, 46]</text>\n",
       "</g>\n",
       "<!-- 6&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>6&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M456.05,-400.88C455.58,-392.78 455.06,-383.98 454.56,-375.47\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"458.04,-375.08 453.96,-365.3 451.05,-375.49 458.04,-375.08\"/>\n",
       "</g>\n",
       "<!-- 20 -->\n",
       "<g id=\"node21\" class=\"node\">\n",
       "<title>20</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"648.5,-357.5 535.5,-357.5 535.5,-304.5 648.5,-304.5 648.5,-357.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"592\" y=\"-342.3\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"592\" y=\"-327.3\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"middle\" x=\"592\" y=\"-312.3\" font-family=\"Times,serif\" font-size=\"14.00\">value = [3, 0]</text>\n",
       "</g>\n",
       "<!-- 6&#45;&gt;20 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>6&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M501.51,-400.88C517.11,-389.01 534.66,-375.65 550.12,-363.88\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"552.64,-366.36 558.47,-357.52 548.4,-360.79 552.64,-366.36\"/>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>8</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"486.5,-261 339.5,-261 339.5,-193 486.5,-193 486.5,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"413\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\">Attempts &lt;= 12.5</text>\n",
       "<text text-anchor=\"middle\" x=\"413\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.499</text>\n",
       "<text text-anchor=\"middle\" x=\"413\" y=\"-215.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 31</text>\n",
       "<text text-anchor=\"middle\" x=\"413\" y=\"-200.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [15, 16]</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>7&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M439.34,-296.88C436.17,-288.6 432.73,-279.6 429.41,-270.91\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"432.57,-269.39 425.73,-261.3 426.03,-271.89 432.57,-269.39\"/>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>13</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"767.5,-261 504.5,-261 504.5,-193 767.5,-193 767.5,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"636\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\">Distance Covered (Kms) &lt;= 101.5</text>\n",
       "<text text-anchor=\"middle\" x=\"636\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.278</text>\n",
       "<text text-anchor=\"middle\" x=\"636\" y=\"-215.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 36</text>\n",
       "<text text-anchor=\"middle\" x=\"636\" y=\"-200.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [6, 30]</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>7&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M511.74,-296.88C529.5,-287.04 549.11,-276.17 567.39,-266.03\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"569.2,-269.03 576.25,-261.12 565.8,-262.91 569.2,-269.03\"/>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>9</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"357,-157 199,-157 199,-89 357,-89 357,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"278\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\">Goal Scored &lt;= 3.5</text>\n",
       "<text text-anchor=\"middle\" x=\"278\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.488</text>\n",
       "<text text-anchor=\"middle\" x=\"278\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 26</text>\n",
       "<text text-anchor=\"middle\" x=\"278\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [15, 11]</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>8&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M369.17,-192.88C356.79,-183.53 343.2,-173.26 330.37,-163.57\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"332.16,-160.54 322.07,-157.3 327.94,-166.12 332.16,-160.54\"/>\n",
       "</g>\n",
       "<!-- 12 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>12</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"488.5,-149.5 375.5,-149.5 375.5,-96.5 488.5,-96.5 488.5,-149.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"432\" y=\"-134.3\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"432\" y=\"-119.3\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"432\" y=\"-104.3\" font-family=\"Times,serif\" font-size=\"14.00\">value = [0, 5]</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>8&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M419.17,-192.88C421.16,-182.22 423.37,-170.35 425.38,-159.52\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"428.86,-159.99 427.25,-149.52 421.97,-158.71 428.86,-159.99\"/>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"252,-53 130,-53 130,0 252,0 252,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"191\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.454</text>\n",
       "<text text-anchor=\"middle\" x=\"191\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 23</text>\n",
       "<text text-anchor=\"middle\" x=\"191\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [15, 8]</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>9&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M247.57,-88.95C239.15,-79.8 230.01,-69.87 221.58,-60.71\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"224.04,-58.22 214.69,-53.24 218.89,-62.96 224.04,-58.22\"/>\n",
       "</g>\n",
       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>11</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"383.5,-53 270.5,-53 270.5,0 383.5,0 383.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"327\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"327\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"middle\" x=\"327\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [0, 3]</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>9&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M295.14,-88.95C299.6,-80.35 304.41,-71.06 308.92,-62.37\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"312.16,-63.73 313.66,-53.24 305.94,-60.5 312.16,-63.73\"/>\n",
       "</g>\n",
       "<!-- 14 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>14</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"689,-157 545,-157 545,-89 689,-89 689,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"617\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\">Off&#45;Target &lt;= 7.5</text>\n",
       "<text text-anchor=\"middle\" x=\"617\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.49</text>\n",
       "<text text-anchor=\"middle\" x=\"617\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 7</text>\n",
       "<text text-anchor=\"middle\" x=\"617\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [3, 4]</text>\n",
       "</g>\n",
       "<!-- 13&#45;&gt;14 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>13&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M629.83,-192.88C628.3,-184.69 626.65,-175.79 625.05,-167.19\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"628.47,-166.49 623.2,-157.3 621.59,-167.77 628.47,-166.49\"/>\n",
       "</g>\n",
       "<!-- 17 -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>17</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"864.5,-157 707.5,-157 707.5,-89 864.5,-89 864.5,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"786\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\">Free Kicks &lt;= 10.5</text>\n",
       "<text text-anchor=\"middle\" x=\"786\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.185</text>\n",
       "<text text-anchor=\"middle\" x=\"786\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 29</text>\n",
       "<text text-anchor=\"middle\" x=\"786\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [3, 26]</text>\n",
       "</g>\n",
       "<!-- 13&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>13&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M684.7,-192.88C698.79,-183.3 714.29,-172.76 728.85,-162.86\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"730.99,-165.64 737.29,-157.12 727.05,-159.85 730.99,-165.64\"/>\n",
       "</g>\n",
       "<!-- 15 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>15</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"552.5,-53 439.5,-53 439.5,0 552.5,0 552.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"496\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"496\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"middle\" x=\"496\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [0, 4]</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;15 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>14&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M574.68,-88.95C562.5,-79.43 549.23,-69.07 537.12,-59.62\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"538.99,-56.63 528.95,-53.24 534.68,-62.15 538.99,-56.63\"/>\n",
       "</g>\n",
       "<!-- 16 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>16</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"683.5,-53 570.5,-53 570.5,0 683.5,0 683.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"627\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"627\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"middle\" x=\"627\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [3, 0]</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>14&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M620.5,-88.95C621.38,-80.62 622.33,-71.65 623.22,-63.2\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"626.7,-63.55 624.28,-53.24 619.74,-62.81 626.7,-63.55\"/>\n",
       "</g>\n",
       "<!-- 18 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>18</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"833.5,-53 720.5,-53 720.5,0 833.5,0 833.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"777\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.48</text>\n",
       "<text text-anchor=\"middle\" x=\"777\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"777\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [2, 3]</text>\n",
       "</g>\n",
       "<!-- 17&#45;&gt;18 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>17&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M782.85,-88.95C782.06,-80.62 781.2,-71.65 780.4,-63.2\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"783.88,-62.86 779.45,-53.24 776.92,-63.52 783.88,-62.86\"/>\n",
       "</g>\n",
       "<!-- 19 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>19</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"974,-53 852,-53 852,0 974,0 974,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"913\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.08</text>\n",
       "<text text-anchor=\"middle\" x=\"913\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 24</text>\n",
       "<text text-anchor=\"middle\" x=\"913\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [1, 23]</text>\n",
       "</g>\n",
       "<!-- 17&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>17&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M830.42,-88.95C843.33,-79.34 857.4,-68.87 870.2,-59.34\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"872.48,-62.01 878.41,-53.24 868.3,-56.4 872.48,-62.01\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f79f2714390>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "import graphviz\n",
    "\n",
    "tree_graph = tree.export_graphviz(tree_model, out_file=None, feature_names=feature_names)\n",
    "graphviz.Source(tree_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d62a1dd0",
   "metadata": {
    "papermill": {
     "duration": 0.011042,
     "end_time": "2021-11-09T00:04:48.098841",
     "exception": false,
     "start_time": "2021-11-09T00:04:48.087799",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As guidance to read the tree:\n",
    "- Leaves with children show their splitting criterion on the top\n",
    "- The pair of values at the bottom show the count of False values and True values for the target respectively, of data points in that node of the tree."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c343065",
   "metadata": {
    "papermill": {
     "duration": 0.011185,
     "end_time": "2021-11-09T00:04:48.121509",
     "exception": false,
     "start_time": "2021-11-09T00:04:48.110324",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "Here is the code to create the Partial Dependence Plot using the [PDPBox library](https://pdpbox.readthedocs.io/en/latest/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "02baa7a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-09T00:04:48.148297Z",
     "iopub.status.busy": "2021-11-09T00:04:48.147528Z",
     "iopub.status.idle": "2021-11-09T00:04:48.687440Z",
     "shell.execute_reply": "2021-11-09T00:04:48.687940Z"
    },
    "papermill": {
     "duration": 0.555313,
     "end_time": "2021-11-09T00:04:48.688137",
     "exception": false,
     "start_time": "2021-11-09T00:04:48.132824",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from pdpbox import pdp, get_dataset, info_plots\n",
    "\n",
    "# Create the data that we will plot\n",
    "pdp_goals = pdp.pdp_isolate(model=tree_model, dataset=val_X, model_features=feature_names, feature='Goal Scored')\n",
    "\n",
    "# plot it\n",
    "pdp.pdp_plot(pdp_goals, 'Goal Scored')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e597fa",
   "metadata": {
    "papermill": {
     "duration": 0.012808,
     "end_time": "2021-11-09T00:04:48.714457",
     "exception": false,
     "start_time": "2021-11-09T00:04:48.701649",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "A few items are worth pointing out as you interpret this plot\n",
    "- The y axis is interpreted as **change in the prediction** from what it would be predicted at the baseline or leftmost value.\n",
    "- A blue shaded area indicates level of confidence\n",
    "\n",
    "From this particular graph, we see that scoring a goal substantially increases your chances of winning \"Man of The Match.\"  But extra goals beyond that appear to have little impact on predictions.\n",
    "\n",
    "Here is another example plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd389634",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-09T00:04:48.744519Z",
     "iopub.status.busy": "2021-11-09T00:04:48.743745Z",
     "iopub.status.idle": "2021-11-09T00:04:49.159210Z",
     "shell.execute_reply": "2021-11-09T00:04:49.160083Z"
    },
    "papermill": {
     "duration": 0.432662,
     "end_time": "2021-11-09T00:04:49.160351",
     "exception": false,
     "start_time": "2021-11-09T00:04:48.727689",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_to_plot = 'Distance Covered (Kms)'\n",
    "pdp_dist = pdp.pdp_isolate(model=tree_model, dataset=val_X, model_features=feature_names, feature=feature_to_plot)\n",
    "\n",
    "pdp.pdp_plot(pdp_dist, feature_to_plot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afdd5ce3",
   "metadata": {
    "papermill": {
     "duration": 0.01478,
     "end_time": "2021-11-09T00:04:49.194697",
     "exception": false,
     "start_time": "2021-11-09T00:04:49.179917",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This graph seems too simple to represent reality. But that's because the model is so simple. You should be able to see from the decision tree above that this is representing exactly the model's structure.\n",
    "\n",
    "You can easily compare the structure or implications of different models. Here is the same plot with a Random Forest model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a3fb78f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-09T00:04:49.231953Z",
     "iopub.status.busy": "2021-11-09T00:04:49.230846Z",
     "iopub.status.idle": "2021-11-09T00:04:49.887327Z",
     "shell.execute_reply": "2021-11-09T00:04:49.887826Z"
    },
    "papermill": {
     "duration": 0.678597,
     "end_time": "2021-11-09T00:04:49.888064",
     "exception": false,
     "start_time": "2021-11-09T00:04:49.209467",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build Random Forest model\n",
    "rf_model = RandomForestClassifier(random_state=0).fit(train_X, train_y)\n",
    "\n",
    "pdp_dist = pdp.pdp_isolate(model=rf_model, dataset=val_X, model_features=feature_names, feature=feature_to_plot)\n",
    "\n",
    "pdp.pdp_plot(pdp_dist, feature_to_plot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8899eeca",
   "metadata": {
    "papermill": {
     "duration": 0.016151,
     "end_time": "2021-11-09T00:04:49.921116",
     "exception": false,
     "start_time": "2021-11-09T00:04:49.904965",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This model thinks you are more likely to win *Man of the Match* if your players run a total of 100km over the course of the game. Though running much more causes lower predictions.\n",
    "\n",
    "In general, the smooth shape of this curve seems more plausible than the step function from the Decision Tree model.  Though this dataset is small enough that we would be careful in how we interpret any model.\n",
    "\n",
    "# 2D Partial Dependence Plots\n",
    "If you are curious about interactions between features, 2D partial dependence plots are also useful. An example may clarify this.  \n",
    "\n",
    "We will again use the Decision Tree model for this graph.  It will create an extremely simple plot, but you should be able to match what you see in the plot to the tree itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13e93cce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-09T00:04:49.965885Z",
     "iopub.status.busy": "2021-11-09T00:04:49.964778Z",
     "iopub.status.idle": "2021-11-09T00:04:50.841593Z",
     "shell.execute_reply": "2021-11-09T00:04:50.842132Z"
    },
    "papermill": {
     "duration": 0.905004,
     "end_time": "2021-11-09T00:04:50.842333",
     "exception": false,
     "start_time": "2021-11-09T00:04:49.937329",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x684 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Similar to previous PDP plot except we use pdp_interact instead of pdp_isolate and pdp_interact_plot instead of pdp_isolate_plot\n",
    "features_to_plot = ['Goal Scored', 'Distance Covered (Kms)']\n",
    "inter1  =  pdp.pdp_interact(model=tree_model, dataset=val_X, model_features=feature_names, features=features_to_plot)\n",
    "\n",
    "pdp.pdp_interact_plot(pdp_interact_out=inter1, feature_names=features_to_plot, plot_type='contour')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "171519f6",
   "metadata": {
    "papermill": {
     "duration": 0.018966,
     "end_time": "2021-11-09T00:04:50.879477",
     "exception": false,
     "start_time": "2021-11-09T00:04:50.860511",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This graph shows predictions for any combination of Goals Scored and Distance covered. \n",
    "\n",
    "For example, we see the highest predictions when a team scores at least 1 goal and they run a total distance close to 100km.  If they score 0 goals, distance covered doesn't matter. Can you see this by tracing through the decision tree with 0 goals?\n",
    "\n",
    "But distance can impact predictions if they score goals. Make sure you can see this from the 2D partial dependence plot. Can you see this pattern in the decision tree too?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fbd1349",
   "metadata": {
    "papermill": {
     "duration": 0.017261,
     "end_time": "2021-11-09T00:04:50.915060",
     "exception": false,
     "start_time": "2021-11-09T00:04:50.897799",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Your Turn\n",
    "**[Test your understanding](https://www.kaggle.com/kernels/fork/1637380)** on conceptual questions and a short coding challenge."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81198f0a",
   "metadata": {
    "papermill": {
     "duration": 0.018052,
     "end_time": "2021-11-09T00:04:50.951503",
     "exception": false,
     "start_time": "2021-11-09T00:04:50.933451",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/machine-learning-explainability/discussion) to chat with other learners.*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 15.897918,
   "end_time": "2021-11-09T00:04:51.780969",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-11-09T00:04:35.883051",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}