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Improving the explainer dashboard summary plot.ipynb
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improving-the-explainer-dashboard-summary-plot.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Improving the explainer dashboard summary plot.ipynb",
"provenance": [],
"collapsed_sections": [
"P6cULsBCAPli"
],
"authorship_tag": "ABX9TyPLLMyy2gmS5jBgDgp0zS1D",
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/hugocool/8fec6bc6114a6ad1eac61ad8cee67989/improving-the-explainer-dashboard-summary-plot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "P6cULsBCAPli"
},
"source": [
"## installs"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4aEnXOEQAJb-",
"outputId": "ced721cf-5c49-4386-d8b1-1ed5a45c6ca9"
},
"source": [
"pip install explainerdashboard"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting explainerdashboard\n",
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" Building wheel for dash-html-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for dash-html-components: filename=dash_html_components-1.1.3-cp37-none-any.whl size=319488 sha256=cd037bbd734ac154af819e8a203c0047450b43ae41599ae088753dd57776c2cb\n",
" Stored in directory: /root/.cache/pip/wheels/07/f9/6c/f9b73a6ae1b7f347a92dc240293cebc267b370ba2a80added2\n",
" Building wheel for dash-table (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for dash-table: filename=dash_table-4.11.3-cp37-none-any.whl size=1827623 sha256=d0637bacadada818ac99c7910189a759c2d37ecdcdb8894bf756515a3d134abe\n",
" Stored in directory: /root/.cache/pip/wheels/51/9b/89/374be1bc250e28a15edbd657cf364f163e392ba5257bfd0e87\n",
"Successfully built dash-auth dtreeviz dash shap dash-renderer dash-core-components dash-html-components dash-table\n",
"Installing collected packages: chart-studio, brotli, flask-compress, flask-seasurf, dash-renderer, dash-core-components, dash-html-components, dash-table, dash, ua-parser, dash-auth, WTForms, flask-wtf, flask-simplelogin, ansi2html, jupyter-dash, waitress, oyaml, colour, dtreeviz, dash-bootstrap-components, slicer, shap, explainerdashboard\n",
"Successfully installed WTForms-2.3.3 ansi2html-1.6.0 brotli-1.0.9 chart-studio-1.1.0 colour-0.1.5 dash-1.20.0 dash-auth-1.4.1 dash-bootstrap-components-0.12.2 dash-core-components-1.16.0 dash-html-components-1.1.3 dash-renderer-1.9.1 dash-table-4.11.3 dtreeviz-1.3 explainerdashboard-0.3.5 flask-compress-1.10.1 flask-seasurf-0.3.0 flask-simplelogin-0.0.7 flask-wtf-0.15.1 jupyter-dash-0.4.0 oyaml-1.0 shap-0.39.0 slicer-0.0.7 ua-parser-0.10.0 waitress-2.0.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x8ctXGJFATWe"
},
"source": [
"## actual work"
]
},
{
"cell_type": "code",
"metadata": {
"id": "GENg9dDAEOUe"
},
"source": [
"from explainerdashboard.datasets import titanic_survive, titanic_names\n",
"\n",
"X_train, y_train, X_test, y_test = titanic_survive()\n",
"train_names, test_names = titanic_names()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IWOXtqycAY4E",
"outputId": "da761043-dd41-4113-b409-bfeb16daaf6f"
},
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from explainerdashboard import ClassifierExplainer, ExplainerDashboard\n",
"\n",
"explainer = ClassifierExplainer(RandomForestClassifier().fit(X_train, y_train), X_test, y_test,labels = ['Not Survived','Survived'])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Detected RandomForestClassifier model: Changing class type to RandomForestClassifierExplainer...\n",
"Note: model_output=='probability', so assuming that raw shap output of RandomForestClassifier is in probability space...\n",
"Generating self.shap_explainer = shap.TreeExplainer(model)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3wN848Kr5gnY"
},
"source": [
"import shap\n",
"from xgboost import XGBClassifier\n",
"\n",
"shap_explainer = shap.TreeExplainer(XGBClassifier().fit(X_train, y_train),data=X_train)\n",
"\n",
"\n",
"shap_values = shap_explainer.shap_values(X_train,y_train.values)\n",
"# shap.summary_plot(shap_values, x_test)\n",
"# shap.summary_plot(shap_values, x_train, plot_type=\"bar\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 851
},
"id": "u4aC_BZM5_NW",
"outputId": "f9589855-e333-4d5c-d671-49f49c6b00b1"
},
"source": [
"shap.summary_plot(shap_values,plot_type='layered_violin')"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "AttributeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-120-42f3c8e9005d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mplot_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'layered_violin'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/shap/plots/_beeswarm.py\u001b[0m in \u001b[0;36msummary_legacy\u001b[0;34m(shap_values, features, feature_names, max_display, plot_type, color, axis_color, title, alpha, show, sort, color_bar, plot_size, layered_violin_max_num_bins, class_names, class_inds, color_bar_label, cmap, auto_size_plot, use_log_scale)\u001b[0m\n\u001b[1;32m 767\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mplot_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"layered_violin\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# courtesy of @kodonnell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 768\u001b[0m \u001b[0mnum_x_points\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 769\u001b[0;31m bins = np.linspace(0, features.shape[0], layered_violin_max_num_bins + 1).round(0).astype(\n\u001b[0m\u001b[1;32m 770\u001b[0m 'int') # the indices of the feature data corresponding to each bin\n\u001b[1;32m 771\u001b[0m \u001b[0mshap_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshap_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'shape'"
]
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x684 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zVl6ZeRXCFhp"
},
"source": [
"from scipy.stats import gaussian_kde"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"id": "1LFnRG3xlR_a",
"outputId": "8451e7ea-4403-4f0f-ac57-08103cdacf11"
},
"source": [
"\n",
"\n",
"pos_label = 1\n",
"cols = explainer.get_importances_df(kind='shap', topx=8, pos_label=pos_label)\\\n",
" ['Feature'].values.tolist()\n",
"\n",
"X_ = explainer.X_merged[cols]\n",
"shap_values_df = explainer.get_shap_values_df(pos_label)[cols]\n",
"shap_values_df\n",
" # cols, \n",
" # idxs=explainer.idxs[plot_idxs],\n",
" # highlight_index=highlight_index,\n",
" # title=title,\n",
" # na_fill=explainer.na_fill,\n",
" # max_cat_colors=max_cat_colors)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sex_male</th>\n",
" <th>Sex_female</th>\n",
" <th>PassengerClass</th>\n",
" <th>Fare</th>\n",
" <th>Deck_Unkown</th>\n",
" <th>Age</th>\n",
" <th>No_of_siblings_plus_spouses_on_board</th>\n",
" <th>No_of_parents_plus_children_on_board</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.131631</td>\n",
" <td>-0.087189</td>\n",
" <td>0.069521</td>\n",
" <td>0.094781</td>\n",
" <td>0.121638</td>\n",
" <td>-0.030428</td>\n",
" <td>0.026642</td>\n",
" <td>0.008099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.159378</td>\n",
" <td>0.176795</td>\n",
" <td>0.106275</td>\n",
" <td>0.074754</td>\n",
" <td>0.065378</td>\n",
" <td>0.008175</td>\n",
" <td>-0.005023</td>\n",
" <td>0.018440</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.035477</td>\n",
" <td>-0.043302</td>\n",
" <td>-0.064163</td>\n",
" <td>0.028878</td>\n",
" <td>-0.034981</td>\n",
" <td>0.100922</td>\n",
" <td>-0.161384</td>\n",
" <td>0.064769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.118693</td>\n",
" <td>0.108073</td>\n",
" <td>-0.097260</td>\n",
" <td>0.027819</td>\n",
" <td>-0.020410</td>\n",
" <td>0.039610</td>\n",
" <td>0.042301</td>\n",
" <td>-0.012520</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.194148</td>\n",
" <td>0.167847</td>\n",
" <td>0.108307</td>\n",
" <td>0.014717</td>\n",
" <td>-0.022565</td>\n",
" <td>0.030616</td>\n",
" <td>0.016032</td>\n",
" <td>-0.002526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>-0.104228</td>\n",
" <td>-0.086666</td>\n",
" <td>-0.035351</td>\n",
" <td>0.002721</td>\n",
" <td>-0.023231</td>\n",
" <td>0.010540</td>\n",
" <td>0.000140</td>\n",
" <td>-0.017352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196</th>\n",
" <td>-0.054357</td>\n",
" <td>-0.055615</td>\n",
" <td>-0.018502</td>\n",
" <td>0.030121</td>\n",
" <td>-0.021786</td>\n",
" <td>0.175009</td>\n",
" <td>0.086138</td>\n",
" <td>0.119470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td>-0.092641</td>\n",
" <td>-0.092314</td>\n",
" <td>0.047979</td>\n",
" <td>-0.116913</td>\n",
" <td>0.118328</td>\n",
" <td>0.043654</td>\n",
" <td>-0.007395</td>\n",
" <td>-0.011423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>-0.053335</td>\n",
" <td>-0.050930</td>\n",
" <td>-0.060711</td>\n",
" <td>-0.073282</td>\n",
" <td>-0.031547</td>\n",
" <td>-0.045197</td>\n",
" <td>-0.009037</td>\n",
" <td>-0.012128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>-0.111305</td>\n",
" <td>-0.103023</td>\n",
" <td>-0.046282</td>\n",
" <td>-0.005438</td>\n",
" <td>-0.046517</td>\n",
" <td>-0.002324</td>\n",
" <td>0.014312</td>\n",
" <td>-0.014192</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>200 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Sex_male ... No_of_parents_plus_children_on_board\n",
"0 0.131631 ... 0.008099\n",
"1 0.159378 ... 0.018440\n",
"2 -0.035477 ... 0.064769\n",
"3 0.118693 ... -0.012520\n",
"4 0.194148 ... -0.002526\n",
".. ... ... ...\n",
"195 -0.104228 ... -0.017352\n",
"196 -0.054357 ... 0.119470\n",
"197 -0.092641 ... -0.011423\n",
"198 -0.053335 ... -0.012128\n",
"199 -0.111305 ... -0.014192\n",
"\n",
"[200 rows x 8 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ciGvj8Vkn4QG"
},
"source": [
"min_shap = shap_values_df.min().min()\n",
"max_shap = shap_values_df.max().max()\n",
"shap_range = max_shap - min_shap\n",
"min_shap = min_shap - 0.01 * shap_range\n",
"max_shap = max_shap + 0.01 * shap_range\n",
"\n",
"fig = make_subplots(rows=8, cols=1, shared_xaxes=True)\n",
"\n",
"display_columns = shap_values_df.columns\n",
"\n",
"for i, col in enumerate(display_columns):\n",
" if is_numeric_dtype(X_[col]):\n",
" # numerical feature get a single bluered plot\n",
" shap_col = shap_values_df[col]\n",
" shaps = shap_col.values\n",
" shap_min, shap_max = np.min(shaps), np.max(shaps)\n",
" rng = shap_max - shap_min\n",
" xs = np.linspace(np.min(shaps) - rng * 0.2, np.max(shaps) + rng * 0.2, 100)\n",
" # if np.std(shaps) < (global_high - global_low) / 100:\n",
" # ds = gaussian_kde(shaps + np.random.randn(len(shaps)) * (global_high - global_low) / 100)(xs)\n",
" # else:\n",
" ds = gaussian_kde(shaps)(xs)\n",
" ds /= np.max(ds) * 3"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SC34a1--BW6P"
},
"source": [
" ## layered violins\n",
" num_x_points = 200\n",
" bins = np.linspace(0, features.shape[0], layered_violin_max_num_bins + 1).round(0).astype(\n",
" 'int') # the indices of the feature data corresponding to each bin\n",
" shap_min, shap_max = np.min(shap_values), np.max(shap_values)\n",
" x_points = np.linspace(shap_min, shap_max, num_x_points)\n",
"\n",
" # loop through each feature and plot:\n",
" for pos, ind in enumerate(feature_order):\n",
" # decide how to handle: if #unique < layered_violin_max_num_bins then split by unique value, otherwise use bins/percentiles.\n",
" # to keep simpler code, in the case of uniques, we just adjust the bins to align with the unique counts.\n",
" feature = features[:, ind]\n",
" unique, counts = np.unique(feature, return_counts=True)\n",
" if unique.shape[0] <= layered_violin_max_num_bins:\n",
" order = np.argsort(unique)\n",
" thesebins = np.cumsum(counts[order])\n",
" thesebins = np.insert(thesebins, 0, 0)\n",
" else:\n",
" thesebins = bins\n",
" nbins = thesebins.shape[0] - 1\n",
" # order the feature data so we can apply percentiling\n",
" order = np.argsort(feature)\n",
" # x axis is located at y0 = pos, with pos being there for offset\n",
" y0 = np.ones(num_x_points) * pos\n",
" # calculate kdes:\n",
" ys = np.zeros((nbins, num_x_points))\n",
" for i in range(nbins):\n",
" # get shap values in this bin:\n",
" shaps = shap_values[order[thesebins[i]:thesebins[i + 1]], ind]\n",
" # if there's only one element, then we can't\n",
" if shaps.shape[0] == 1:\n",
" warnings.warn(\n",
" \"not enough data in bin #%d for feature %s, so it'll be ignored. Try increasing the number of records to plot.\"\n",
" % (i, feature_names[ind]))\n",
" # to ignore it, just set it to the previous y-values (so the area between them will be zero). Not ys is already 0, so there's\n",
" # nothing to do if i == 0\n",
" if i > 0:\n",
" ys[i, :] = ys[i - 1, :]\n",
" continue\n",
" # save kde of them: note that we add a tiny bit of gaussian noise to avoid singular matrix errors\n",
" ys[i, :] = gaussian_kde(shaps + np.random.normal(loc=0, scale=0.001, size=shaps.shape[0]))(x_points)\n",
" # scale it up so that the 'size' of each y represents the size of the bin. For continuous data this will\n",
" # do nothing, but when we've gone with the unqique option, this will matter - e.g. if 99% are male and 1%\n",
" # female, we want the 1% to appear a lot smaller.\n",
" size = thesebins[i + 1] - thesebins[i]\n",
" bin_size_if_even = features.shape[0] / nbins\n",
" relative_bin_size = size / bin_size_if_even\n",
" ys[i, :] *= relative_bin_size\n",
" # now plot 'em. We don't plot the individual strips, as this can leave whitespace between them.\n",
" # instead, we plot the full kde, then remove outer strip and plot over it, etc., to ensure no\n",
" # whitespace\n",
" ys = np.cumsum(ys, axis=0)\n",
" width = 0.8\n",
" scale = ys.max() * 2 / width # 2 is here as we plot both sides of x axis\n",
" for i in range(nbins - 1, -1, -1):\n",
" y = ys[i, :] / scale\n",
" c = pl.get_cmap(color)(i / (\n",
" nbins - 1)) if color in pl.cm.datad else color # if color is a cmap, use it, otherwise use a color\n",
" pl.fill_between(x_points, pos - y, pos + y, facecolor=c)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "XaMnoPfdCKjr"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pandas as pd\n",
"from pandas.api.types import is_numeric_dtype\n",
"\n",
"import plotly.graph_objs as go\n",
"from plotly.subplots import make_subplots\n",
"\n",
"from sklearn.metrics import (classification_report, confusion_matrix,\n",
" precision_recall_curve, roc_curve, \n",
" roc_auc_score, average_precision_score)\n",
"\n",
"def plotly_shap_scatter_plot(X, shap_values_df, display_columns=None, title=\"Shap values\", \n",
" idxs=None, highlight_index=None, na_fill=-999, round=3, max_cat_colors=5):\n",
" \"\"\"Generate a shap values summary plot where features are ranked from\n",
" highest mean absolute shap value to lowest, with point clouds shown\n",
" for each feature. \n",
" Args:\n",
" \n",
" X (pd.DataFrame): dataframe of input features\n",
" shap_values_df (pd.DataFrame): dataframe shap_values with same columns as X\n",
" display_columns (List[str]): list of feature to be displayed. If None\n",
" default to all columns in X.\n",
" title (str, optional): Title to display above graph. \n",
" Defaults to \"Shap values\".\n",
" idxs (List[str], optional): List of identifiers for each row in X. \n",
" Defaults to None.\n",
" highlight_index ({str, int}, optional): Index to highlight in graph. \n",
" Defaults to None.\n",
" na_fill (int, optional): Fill value used to fill missing values, \n",
" will be colored grey in the graph.. Defaults to -999.\n",
" round (int, optional): rounding to apply to floats. Defaults to 3.\n",
" index_name (str): identifier for idxs. Defaults to \"index\".\n",
" Returns:\n",
" Plotly fig\n",
" \"\"\"\n",
" # assert matching_cols(X, shap_values_df), \"X and shap_values_df should have matching columns!\"\n",
" if display_columns is None:\n",
" display_columns = X.columns.tolist()\n",
" if idxs is not None:\n",
" assert len(idxs)==X.shape[0]\n",
" idxs = pd.Index(idxs).astype(str)\n",
" else:\n",
" idxs = X.index.astype(str)\n",
" index_name = idxs.name\n",
" \n",
" length = len(X)\n",
" if highlight_index is not None:\n",
" if isinstance(highlight_index, int):\n",
" assert highlight_index >=0 and highlight_index < len(X), \\\n",
" \"if highlight_index is int, then should be between 0 and {len(X)}!\"\n",
" highlight_idx = highlight_index\n",
" highlight_index = idxs[highlight_idx]\n",
" elif isinstance(highlight_index, str):\n",
" assert str(highlight_index) in idxs, f\"{highlight_index} not found in idxs!\"\n",
" highlight_idx = np.where(idxs == str(highlight_index))[0].item()\n",
" else:\n",
" raise ValueError(\"Please pass either int or str highlight_index!\")\n",
" \n",
" # make sure that columns are actually in X:\n",
" display_columns = [col for col in display_columns if col in X.columns] \n",
" min_shap = shap_values_df.min().min()\n",
" max_shap = shap_values_df.max().max()\n",
" shap_range = max_shap - min_shap\n",
" min_shap = min_shap - 0.01 * shap_range\n",
" max_shap = max_shap + 0.01 * shap_range\n",
"\n",
" fig = make_subplots(rows=len(display_columns), cols=1, \n",
" subplot_titles=display_columns, shared_xaxes=True)\n",
" \n",
"\n",
" \n",
" for i, col in enumerate(display_columns):\n",
" if is_numeric_dtype(X[col]):\n",
" # numerical feature get a single bluered plot\n",
" shap_col = shap_values_df[col]\n",
" shaps = shap_col.values\n",
" shap_min, shap_max = np.min(shaps), np.max(shaps)\n",
" rng = shap_max - shap_min\n",
" xs = np.linspace(np.min(shaps) - rng * 0.2, np.max(shaps) + rng * 0.2, 100)\n",
" if np.std(shaps) < (max_shap - min_shap) / 100:\n",
" ds = gaussian_kde(shaps + np.random.randn(len(shaps)) * (max_shap - min_shap) / 100)(xs)\n",
" else:\n",
" ds = gaussian_kde(shaps)(xs)\n",
" ds /= np.max(ds) * 3\n",
" fig.add_trace(go.Scattergl(\n",
" x=shap_col,\n",
" y=ds, #np.random.rand(length),\n",
" mode='markers',\n",
" marker=dict(\n",
" size=5,\n",
" color=X[col].replace({na_fill:np.nan}),\n",
" colorscale='Bluered',\n",
" showscale=True,\n",
" opacity=0.3,\n",
" colorbar=dict(\n",
" title=\"feature value <br> (red is high)\", \n",
" showticklabels=False),\n",
" ),\n",
" name=col,\n",
" showlegend=False,\n",
" opacity=0.8,\n",
" hoverinfo=\"text\",\n",
" text=[f\"{index_name}={i}<br>{col}={value}<br>shap={shap:.{round}f}\" \n",
" for i, shap, value in zip(idxs, shap_values_df[col], X[col].replace({na_fill:np.nan}))],\n",
" ),\n",
" row=i+1, col=1);\n",
" else: \n",
" color_cats = list(X[col].value_counts().index[:max_cat_colors])\n",
" n_color_cats = len(color_cats)\n",
" colors = ['#636EFA', '#EF553B', '#00CC96', '#AB63FA', '#FFA15A', \n",
" '#19D3F3', '#FF6692', '#B6E880', '#FF97FF', '#FECB52']\n",
" colors = colors * (1+int(n_color_cats / len(colors)))\n",
" colors = colors[:n_color_cats]\n",
"\n",
" shap_col = shap_values_df[col]\n",
" shaps = shap_col.values\n",
" shap_min, shap_max = np.min(shaps), np.max(shaps)\n",
" rng = shap_max - shap_min\n",
" xs = np.linspace(np.min(shaps) - rng * 0.2, np.max(shaps) + rng * 0.2, 100)\n",
" if np.std(shaps) < (max_shap - min_shap) / 100:\n",
" ds = gaussian_kde(shaps + np.random.randn(len(shaps)) * (max_shap - min_shap) / 100)(xs)\n",
" else:\n",
" ds = gaussian_kde(shaps)(xs)\n",
" \n",
" ds /= np.max(ds) * 3\n",
" for cat, color in zip(color_cats, colors):\n",
" fig.add_trace(go.Scattergl(\n",
" x=shap_values_df[col][X[col]==cat],\n",
" y=np.random.rand((X[col]==cat).sum()),\n",
" mode='markers',\n",
" marker=dict(\n",
" size=5,\n",
" showscale=False,\n",
" opacity=0.3,\n",
" color=color,\n",
" ),\n",
" name=cat,\n",
" showlegend=False,\n",
" opacity=0.8,\n",
" hoverinfo=\"text\",\n",
" text=[f\"{index_name}={i}<br>{col}={cat}<br>shap={shap:.{round}f}\" \n",
" for i, shap in zip(idxs[X[col]==cat], \n",
" shap_values_df[col][X[col]==cat])],\n",
" ),\n",
" row=i+1, col=1)\n",
" if X[col].nunique() > max_cat_colors:\n",
" fig.add_trace(go.Scattergl(\n",
" x=shap_values_df[col][~X[col].isin(color_cats)],\n",
" y=np.random.rand((~X[col].isin(color_cats)).sum()),\n",
" mode='markers',\n",
" marker=dict(\n",
" size=5,\n",
" showscale=False,\n",
" opacity=0.3,\n",
" color=\"grey\",\n",
" ),\n",
" name=\"Other\",\n",
" showlegend=False,\n",
" opacity=0.8,\n",
" hoverinfo=\"text\",\n",
" text=[f\"{index_name}={i}<br>{col}={col_val}<br>shap={shap:.{round}f}\" \n",
" for i, shap, col_val in zip(idxs[~X[col].isin(color_cats)], \n",
" shap_values_df[col][~X[col].isin(color_cats)],\n",
" X[col][~X[col].isin(color_cats)])],\n",
" ),\n",
" row=i+1, col=1)\n",
" \n",
" if highlight_index is not None:\n",
" fig.add_trace(\n",
" go.Scattergl(\n",
" x=[shap_values_df[col].iloc[highlight_idx]], \n",
" y=[0], \n",
" mode='markers',\n",
" marker=dict(\n",
" color='LightSkyBlue',\n",
" size=20,\n",
" opacity=0.5,\n",
" line=dict(\n",
" color='MediumPurple',\n",
" width=4\n",
" )\n",
" ),\n",
" name = f\"{index_name} {highlight_index}\",\n",
" text=f\"index={highlight_index}<br>{col}={X[col].iloc[highlight_idx]}<br>shap={shap_values_df[col].iloc[highlight_idx]:.{round}f}\",\n",
" hoverinfo=\"text\",\n",
" showlegend=False,\n",
" ), row=i+1, col=1)\n",
" fig.update_xaxes(showgrid=False, zeroline=False, \n",
" range=[min_shap, max_shap], row=i+1, col=1)\n",
" fig.update_yaxes(showgrid=False, zeroline=False, \n",
" showticklabels=False, row=i+1, col=1)\n",
" \n",
" fig.update_layout(title=title + \"<br>\",\n",
" height=100+len(display_columns)*50,\n",
" margin=go.layout.Margin(\n",
" l=50,\n",
" r=50,\n",
" b=50,\n",
" t=100,\n",
" pad=4\n",
" ),\n",
" hovermode='closest',\n",
" plot_bgcolor = '#fff',)\n",
" return fig\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rUuBFhHcdfWz"
},
"source": [
"shaps = shap_values[:, i]\n",
"shap_min, shap_max = np.min(shaps), np.max(shaps)\n",
"rng = shap_max - shap_min\n",
"xs = np.linspace(np.min(shaps) - rng * 0.2, np.max(shaps) + rng * 0.2, 100)\n",
"if np.std(shaps) < (global_high - global_low) / 100:\n",
" ds = gaussian_kde(shaps + np.random.randn(len(shaps)) * (global_high - global_low) / 100)(xs)\n",
"else:\n",
" ds = gaussian_kde(shaps)(xs)\n",
"ds /= np.max(ds) * 3"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9eZUQzekCghL"
},
"source": [
"def plot_importances_detailed(explainer, highlight_index=None, topx=None, max_cat_colors=5, \n",
" plot_sample=None, pos_label=None):\n",
" \"\"\"Plot barchart of mean absolute shap value.\n",
" \n",
" Displays all individual shap value for each feature in a horizontal\n",
" scatter chart in descending order by mean absolute shap value.\n",
" Args:\n",
" highlight_index (str or int): index to highlight\n",
" topx(int, optional): Only display topx most important features, \n",
" defaults to None\n",
" max_cat_colors (int, optional): for categorical features, maximum number\n",
" of categories to label with own color. Defaults to 5. \n",
" plot_sample (int, optional): Instead of all points only plot a random\n",
" sample of points. Defaults to None (=all points)\n",
" pos_label: positive class (Default value = None)\n",
" Returns:\n",
" plotly.Fig\n",
" \"\"\"\n",
" plot_idxs = explainer.get_idx_sample(plot_sample, highlight_index)\n",
" highlight_index = explainer.get_index(highlight_index)\n",
"\n",
" if explainer.is_classifier:\n",
" pos_label_str = explainer.labels[pos_label]\n",
" if explainer.model_output == 'probability':\n",
" if explainer.target:\n",
" title = f\"Impact of feature on predicted probability {explainer.target}={pos_label_str} <br> (SHAP values)\"\n",
" else:\n",
" title = f\"Impact of Feature on Prediction probability <br> (SHAP values)\"\n",
" elif explainer.model_output == 'logodds':\n",
" title = f\"Impact of Feature on predicted logodds <br> (SHAP values)\"\n",
" elif explainer.is_regression:\n",
" if explainer.target:\n",
" title = f\"Impact of Feature on Predicted {explainer.target} <br> (SHAP values)\"\n",
" else:\n",
" title = f\"Impact of Feature on Prediction<br> (SHAP values)\"\n",
"\n",
" cols = explainer.get_importances_df(kind='shap', topx=topx, pos_label=pos_label)\\\n",
" ['Feature'].values.tolist()\n",
"\n",
" return plotly_shap_scatter_plot(\n",
" explainer.X_merged[cols].iloc[plot_idxs],\n",
" explainer.get_shap_values_df(pos_label)[cols].iloc[plot_idxs],\n",
" cols, \n",
" idxs=explainer.idxs[plot_idxs],\n",
" highlight_index=highlight_index,\n",
" title=title,\n",
" na_fill=explainer.na_fill,\n",
" max_cat_colors=max_cat_colors)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 351
},
"id": "e_fbgAPB19JD",
"outputId": "0a5de33d-d078-4ebf-8acf-c63e19590983"
},
"source": [
" # make sure that columns are actually in X:\n",
" display_columns = [col for col in display_columns if col in X.columns] \n",
" min_shap = shap_values_df.min().min()\n",
" max_shap = shap_values_df.max().max()\n",
" shap_range = max_shap - min_shap\n",
" min_shap = min_shap - 0.01 * shap_range\n",
" max_shap = max_shap + 0.01 * shap_range\n",
"\n",
" fig = make_subplots(rows=len(display_columns), cols=1, \n",
" subplot_titles=display_columns, shared_xaxes=True)\n",
" \n",
"\n",
" \n",
" for i, col in enumerate(display_columns):\n",
" if is_numeric_dtype(X[col]):\n",
" # numerical feature get a single bluered plot\n",
" shap_col = shap_values_df[col]\n",
" shaps = shap_col.values\n",
" shap_min, shap_max = np.min(shaps), np.max(shaps)\n",
" rng = shap_max - shap_min\n",
" xs = np.linspace(np.min(shaps) - rng * 0.2, np.max(shaps) + rng * 0.2, 100)\n",
" if np.std(shaps) < (max_shap - min_shap) / 100:\n",
" ds = gaussian_kde(shaps + np.random.randn(len(shaps)) * (max_shap - min_shap) / 100)(xs)\n",
" else:\n",
" ds = gaussian_kde(shaps)(xs)\n",
" ds /= np.max(ds) * 3\n",
" fig.add_trace(go.Scattergl(\n",
" x=shap_col,\n",
" y=ds, #np.random.rand(length),\n",
" mode='markers',\n",
" marker=dict(\n",
" size=5,\n",
" color=X[col].replace({-999:np.nan}),\n",
" colorscale='Bluered',\n",
" showscale=True,\n",
" opacity=0.3,\n",
" colorbar=dict(\n",
" title=\"feature value <br> (red is high)\", \n",
" showticklabels=False),\n",
" ),\n",
" name=col,\n",
" showlegend=False,\n",
" opacity=0.8,\n",
" hoverinfo=\"text\",\n",
" text=[f\"{index_name}={i}<br>{col}={value}<br>shap={shap:.{round}f}\" \n",
" for i, shap, value in zip(idxs, shap_values_df[col], X[col].replace({na_fill:np.nan}))],\n",
" ),\n",
" row=i+1, col=1);"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-53-456b72972909>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# make sure that columns are actually in X:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdisplay_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdisplay_columns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mmin_shap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshap_values_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmax_shap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshap_values_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mshap_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_shap\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmin_shap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-53-456b72972909>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# make sure that columns are actually in X:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdisplay_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdisplay_columns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mmin_shap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshap_values_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmax_shap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshap_values_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mshap_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_shap\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmin_shap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lf4eEUrG2Ebb"
},
"source": [
"pos_label = 1\n",
"cols = explainer.get_importances_df(kind='shap', topx=8, pos_label=pos_label)\\\n",
" ['Feature'].values.tolist()\n",
"\n",
"X_ = explainer.X_merged[cols]\n",
"shap_values_df = explainer.get_shap_values_df(pos_label)[cols]\n",
"shap_values_df"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jHYHQYt02KCK"
},
"source": [
"col = 'Fare'\n",
"cols = [col]\n",
"X_ = explainer.X_merged[cols]\n",
"shap_values_df = explainer.get_shap_values_df(pos_label)[cols]\n",
"shap_values_df"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tqbxmr391XSd"
},
"source": [
"## try to do a single trace\n",
"\n",
"fig = make_subplots(rows=1, cols=1, \n",
" column_titles = cols,\n",
" shared_xaxes=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "MchzQ9a93Bwe",
"outputId": "fae36402-de1a-4216-bc63-afccf13a2aab"
},
"source": [
"## single violin plot\n",
"shap_col = shap_values_df[col]\n",
"shaps = shap_col.values\n",
"shap_min, shap_max = np.min(shaps), np.max(shaps)\n",
"rng = shap_max - shap_min\n",
"xs = np.linspace(np.min(shaps) - rng * 0.2, np.max(shaps) + rng * 0.2, 100)\n",
"if np.std(shaps) < (max_shap - min_shap) / 100:\n",
" ds = gaussian_kde(shaps + np.random.randn(len(shaps)) * (max_shap - min_shap) / 100)(xs)\n",
"else:\n",
" ds = gaussian_kde(shaps)(xs)\n",
"ds /= np.max(ds) * 3\n",
"fig.add_trace(go.Scattergl(\n",
" x=xs,#shap_col,\n",
" y=ds, #np.random.rand(length),\n",
" mode='markers',\n",
" marker=dict(\n",
" size=5,\n",
" color=X_[col].replace({-999:np.nan}),\n",
" colorscale='Bluered',\n",
" showscale=True,\n",
" opacity=0.3,\n",
" colorbar=dict(\n",
" title=\"feature value <br> (red is high)\", \n",
" showticklabels=False),\n",
" ),\n",
" name=col,\n",
" showlegend=False,\n",
" opacity=0.8,\n",
" # hoverinfo=\"text\",\n",
" # text=[f\"{index_name}={i}<br>{col}={value}<br>shap={shap:.{round}f}\" \n",
" # for i, shap, value in zip(idxs, shap_values_df[col], X[col].replace({-999:np.nan}))],\n",
" ),\n",
" row=1, col=1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "QzfQXf4DDhZw",
"outputId": "7c69786c-13b9-4f5b-ab63-8667df9d888e"
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"source": [
"# pl.axhline(y=pos, color=\"#cccccc\", lw=0.5, dashes=(1, 5), zorder=-1) # add center line\n",
"\n",
"\n",
"\n",
"row_height = 0.4\n",
"\n",
"shap_col = shap_values_df[col]\n",
"shaps = shap_col.values\n",
"features = X_\n",
"values = features.values\n",
"idx2cat = None\n",
"# convert from a DataFrame or other types\n",
"if str(type(features)) == \"<class 'pandas.core.frame.DataFrame'>\":\n",
" \n",
" feature_names = features.columns\n",
" # feature index to category flag\n",
" idx2cat = features.dtypes.astype(str).isin([\"object\", \"category\"]).tolist()\n",
" features = features.values\n",
"\n",
"inds = np.arange(len(shaps))\n",
"np.random.shuffle(inds)\n",
"if values is not None:\n",
" values = values[inds]\n",
"shaps = shaps[inds]\n",
"colored_feature = True\n",
"try:\n",
" if idx2cat is not None and idx2cat[i]: # check categorical feature\n",
" colored_feature = False\n",
" else:\n",
" values = np.array(values, dtype=np.float64) # make sure this can be numeric\n",
"except:\n",
" colored_feature = False\n",
"N = len(shaps)\n",
"# hspacing = (np.max(shaps) - np.min(shaps)) / 200\n",
"# curr_bin = []\n",
"nbins = 100\n",
"quant = np.round(nbins * (shaps - np.min(shaps)) / (np.max(shaps) - np.min(shaps) + 1e-8))\n",
"inds = np.argsort(quant + np.random.randn(N) * 1e-6)\n",
"layer = 0\n",
"last_bin = -1\n",
"ys = np.zeros(N)\n",
"for ind in inds:\n",
" if quant[ind] != last_bin:\n",
" layer = 0\n",
" ys[ind] = np.ceil(layer / 2) * ((layer % 2) * 2 - 1)\n",
" layer += 1\n",
" last_bin = quant[ind]\n",
"ys *= 0.9 * (row_height / np.max(ys + 1))\n",
"\n",
"if features is not None:\n",
" # trim the color range, but prevent the color range from collapsing\n",
" vmin = np.nanpercentile(values, 5)\n",
" vmax = np.nanpercentile(values, 95)\n",
" if vmin == vmax:\n",
" vmin = np.nanpercentile(values, 1)\n",
" vmax = np.nanpercentile(values, 99)\n",
" if vmin == vmax:\n",
" vmin = np.min(values)\n",
" vmax = np.max(values)\n",
" if vmin > vmax: # fixes rare numerical precision issues\n",
" vmin = vmax\n",
"\n",
" assert features.shape[0] == len(shaps), \"Feature and SHAP matrices must have the same number of rows!\"\n",
"\n",
" # plot the nan values in the interaction feature as grey\n",
" nan_mask = np.isnan(values)\n",
"\n",
" # pl.scatter(shaps[nan_mask], pos + ys[nan_mask], color=\"#777777\", vmin=vmin,\n",
" # vmax=vmax, s=16, alpha=alpha, linewidth=0,\n",
" # zorder=3, rasterized=len(shaps) > 500)\n",
"\n",
" # plot the non-nan values colored by the trimmed feature value\n",
" cvals = values[np.invert(nan_mask)].astype(np.float64)\n",
" cvals_imp = cvals.copy()\n",
" cvals_imp[np.isnan(cvals)] = (vmin + vmax) / 2.0\n",
" cvals[cvals_imp > vmax] = vmax\n",
" cvals[cvals_imp < vmin] = vmin\n",
"\n",
" # pl.scatter(shaps[np.invert(nan_mask)], pos + ys[np.invert(nan_mask)],\n",
" # # cmap=cmap, \n",
" # vmin=vmin,\n",
" # vmax=vmax, s=16,\n",
" # c=cvals, alpha=alpha, linewidth=0,\n",
" # zorder=3, rasterized=len(shaps) > 500)\n",
"\n",
"pos=1\n",
"fig.add_trace(go.Scattergl(\n",
" x=shaps,#[np.invert(nan_mask)],#shap_col,\n",
" y=pos + ys,#[np.invert(nan_mask)], #np.random.rand(length),\n",
" mode='markers',\n",
" marker=dict(\n",
" size=8,\n",
" color=cvals,#X_[col].replace({-999:np.nan}),\n",
" colorscale='Bluered',\n",
" showscale=True,\n",
" opacity=1,\n",
" colorbar=dict(\n",
" title=\"feature value <br> (red is high)\", \n",
" showticklabels=False),\n",
" ),\n",
" name=col,\n",
" showlegend=False,\n",
" opacity=0.8,\n",
" # hoverinfo=\"text\",\n",
" # text=[f\"{index_name}={i}<br>{col}={value}<br>shap={shap:.{round}f}\" \n",
" # for i, shap, value in zip(idxs, shap_values_df[col], X[col].replace({-999:np.nan}))],\n",
" ),\n",
" row=1, col=1)"
],
"execution_count": null,
"outputs": [
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Katriina<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.027\", \"Passenger=Pekoniemi, Mr. Edvard<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.006\", \"Passenger=Turpin, Mr. William John Robert<br>No_of_siblings_plus_spouses_on_board=1<br>shap=-0.017\", \"Passenger=Moore, Mr. Leonard Charles<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Osen, Mr. Olaf Elon<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.001\", \"Passenger=Ford, Miss. Robina Maggie \\\"Ruby\\\"<br>No_of_siblings_plus_spouses_on_board=2<br>shap=-0.031\", \"Passenger=Navratil, Mr. Michel (\\\"Louis M Hoffman\\\")<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.013\", \"Passenger=Bateman, Rev. Robert James<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.011\", \"Passenger=Meo, Mr. Alfonzo<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.005\", \"Passenger=Cribb, Mr. John Hatfield<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.004\", \"Passenger=Chibnall, Mrs. (Edith Martha Bowerman)<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.021\", \"Passenger=Van der hoef, Mr. Wyckoff<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.018\", \"Passenger=Sivola, Mr. Antti Wilhelm<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.006\", \"Passenger=Klasen, Mr. Klas Albin<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.061\", \"Passenger=Rood, Mr. Hugh Roscoe<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.008\", \"Passenger=Andersen-Jensen, Miss. Carla Christine Nielsine<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.021\", \"Passenger=Brown, Mrs. James Joseph (Margaret Tobin)<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.002\", \"Passenger=Vande Walle, Mr. Nestor Cyriel<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.010\", \"Passenger=Backstrom, Mr. Karl Alfred<br>No_of_siblings_plus_spouses_on_board=1<br>shap=-0.027\", \"Passenger=Blank, Mr. Henry<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.019\", \"Passenger=Ali, Mr. Ahmed<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.015\", \"Passenger=Green, Mr. George Henry<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.005\", \"Passenger=Nenkoff, Mr. Christo<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.005\", \"Passenger=Asplund, Miss. Lillian Gertrud<br>No_of_siblings_plus_spouses_on_board=4<br>shap=-0.184\", \"Passenger=Harknett, Miss. Alice Phoebe<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.018\", \"Passenger=Hunt, Mr. George Henry<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.018\", \"Passenger=Reed, Mr. James George<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.003\", \"Passenger=Stead, Mr. William Thomas<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.001\", \"Passenger=Thorne, Mrs. Gertrude Maybelle<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.012\", \"Passenger=Parrish, Mrs. (Lutie Davis)<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.054\", \"Passenger=Smith, Mr. Thomas<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Asplund, Master. Edvin Rojj Felix<br>No_of_siblings_plus_spouses_on_board=4<br>shap=-0.212\", \"Passenger=Healy, Miss. Hanora \\\"Nora\\\"<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.008\", \"Passenger=Andrews, Miss. Kornelia Theodosia<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.008\", \"Passenger=Abbott, Mrs. Stanton (Rosa Hunt)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.019\", \"Passenger=Smith, Mr. Richard William<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.001\", \"Passenger=Connolly, Miss. Kate<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.002\", \"Passenger=Bishop, Mrs. Dickinson H (Helen Walton)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.019\", \"Passenger=Levy, Mr. Rene Jacques<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.005\", \"Passenger=Lewy, Mr. Ervin G<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Williams, Mr. Howard Hugh \\\"Harry\\\"<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Sage, Mr. George John Jr<br>No_of_siblings_plus_spouses_on_board=8<br>shap=-0.185\", \"Passenger=Nysveen, Mr. Johan Hansen<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.005\", \"Passenger=Frauenthal, Mrs. Henry William (Clara Heinsheimer)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.019\", \"Passenger=Denkoff, Mr. Mitto<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.005\", \"Passenger=Burns, Miss. Elizabeth Margaret<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.002\", \"Passenger=Dimic, Mr. Jovan<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.005\", \"Passenger=del Carlo, Mr. Sebastiano<br>No_of_siblings_plus_spouses_on_board=1<br>shap=-0.030\", \"Passenger=Beavan, Mr. William Thomas<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.016\", \"Passenger=Meyer, Mrs. Edgar Joseph (Leila Saks)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.014\", \"Passenger=Widener, Mr. Harry Elkins<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.020\", \"Passenger=Gustafsson, Mr. Karl Gideon<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.007\", \"Passenger=Plotcharsky, Mr. Vasil<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.005\", \"Passenger=Goodwin, Master. Sidney Leonard<br>No_of_siblings_plus_spouses_on_board=5<br>shap=-0.222\", \"Passenger=Sadlier, Mr. Matthew<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.003\", \"Passenger=Gustafsson, Mr. Johan Birger<br>No_of_siblings_plus_spouses_on_board=2<br>shap=-0.053\", \"Passenger=Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.012\", \"Passenger=Niskanen, Mr. Juha<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.002\", \"Passenger=Minahan, Miss. Daisy E<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.003\", \"Passenger=Matthews, Mr. William John<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.012\", \"Passenger=Charters, Mr. David<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.002\", \"Passenger=Phillips, Miss. Kate Florence (\\\"Mrs Kate Louise Phillips Marshall\\\")<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.000\", \"Passenger=Hart, Mrs. Benjamin (Esther Ada Bloomfield)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=-0.051\", \"Passenger=Johannesen-Bratthammer, Mr. Bernt<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.005\", \"Passenger=Peuchen, Major. Arthur Godfrey<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Anderson, Mr. Harry<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.010\", \"Passenger=Milling, Mr. Jacob Christian<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.012\", \"Passenger=West, Mrs. Edwy Arthur (Ada Mary Worth)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.001\", \"Passenger=Karlsson, Mr. Nils August<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Frost, Mr. Anthony Wood \\\"Archie\\\"<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.003\", \"Passenger=Bishop, Mr. Dickinson H<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.078\", \"Passenger=Windelov, Mr. Einar<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.005\", \"Passenger=Shellard, Mr. Frederick William<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.002\", \"Passenger=Svensson, Mr. Olof<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.002\", \"Passenger=O'Sullivan, Miss. Bridget Mary<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.015\", \"Passenger=Laitinen, Miss. Kristina Sofia<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.030\", \"Passenger=Penasco y Castellana, Mr. Victor de Satode<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.003\", \"Passenger=Bradley, Mr. George (\\\"George Arthur Brayton\\\")<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.008\", \"Passenger=Angle, Mrs. William A (Florence \\\"Mary\\\" Agnes Hughes)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=-0.009\", \"Passenger=Kassem, Mr. Fared<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.000\", \"Passenger=Cacic, Miss. Marija<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.036\", \"Passenger=Hart, Miss. Eva Miriam<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.041\", \"Passenger=Butt, Major. Archibald Willingham<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.005\", \"Passenger=Beane, Mr. Edward<br>No_of_siblings_plus_spouses_on_board=1<br>shap=-0.026\", \"Passenger=Goldsmith, Mr. Frank John<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.011\", \"Passenger=Ohman, Miss. Velin<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.003\", \"Passenger=Taussig, Mrs. Emil (Tillie Mandelbaum)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.009\", \"Passenger=Morrow, Mr. Thomas Rowan<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Harris, Mr. George<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.003\", \"Passenger=Appleton, Mrs. Edward Dale (Charlotte Lamson)<br>No_of_siblings_plus_spouses_on_board=2<br>shap=0.007\", \"Passenger=Patchett, Mr. George<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.001\", \"Passenger=Ross, Mr. John Hugo<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.010\", \"Passenger=Murdlin, Mr. Joseph<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Bourke, Miss. Mary<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.016\", \"Passenger=Boulos, Mr. Hanna<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.005\", \"Passenger=Duff Gordon, Sir. Cosmo Edmund (\\\"Mr Morgan\\\")<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.042\", \"Passenger=Homer, Mr. Harry (\\\"Mr E Haven\\\")<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.008\", \"Passenger=Lindell, Mr. Edvard Bengtsson<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.011\", \"Passenger=Daniel, Mr. Robert Williams<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.018\", \"Passenger=Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.011\", \"Passenger=Shutes, Miss. Elizabeth W<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.010\", \"Passenger=Jardin, Mr. Jose Neto<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.004\", \"Passenger=Horgan, Mr. John<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Lobb, Mrs. William Arthur (Cordelia K Stanlick)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=-0.023\", \"Passenger=Yasbeck, Mr. Antoni<br>No_of_siblings_plus_spouses_on_board=1<br>shap=-0.036\", \"Passenger=Bostandyeff, Mr. Guentcho<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Lundahl, Mr. Johan Svensson<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.005\", \"Passenger=Stahelin-Maeglin, Dr. Max<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.017\", \"Passenger=Willey, Mr. Edward<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.005\", \"Passenger=Stanley, Miss. Amy Zillah Elsie<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.018\", \"Passenger=Hegarty, Miss. Hanora \\\"Nora\\\"<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.010\", \"Passenger=Eitemiller, Mr. George Floyd<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.003\", \"Passenger=Colley, Mr. Edward Pomeroy<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.008\", \"Passenger=Coleff, Mr. Peju<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.003\", \"Passenger=Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=-0.001\", \"Passenger=Davidson, Mr. Thornton<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.040\", \"Passenger=Turja, Miss. Anna Sofia<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.007\", \"Passenger=Hassab, Mr. Hammad<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.013\", \"Passenger=Goodwin, Mr. Charles Edward<br>No_of_siblings_plus_spouses_on_board=5<br>shap=-0.169\", \"Passenger=Panula, Mr. Jaako Arnold<br>No_of_siblings_plus_spouses_on_board=4<br>shap=-0.130\", \"Passenger=Fischer, Mr. Eberhard Thelander<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.010\", \"Passenger=Humblen, Mr. Adolf Mathias Nicolai Olsen<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.003\", \"Passenger=Astor, Mrs. John Jacob (Madeleine Talmadge Force)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=-0.004\", \"Passenger=Hansen, Mr. Henrik Juul<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.095\", \"Passenger=Calderhead, Mr. Edward Pennington<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.011\", \"Passenger=Klaber, Mr. Herman<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.024\", \"Passenger=Taylor, Mr. Elmer Zebley<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.015\", \"Passenger=Larsson, Mr. August Viktor<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.030\", \"Passenger=Greenberg, Mr. Samuel<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.012\", \"Passenger=Troutt, Miss. Edwina Celia \\\"Winnie\\\"<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.018\", \"Passenger=McEvoy, Mr. Michael<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.010\", \"Passenger=Johnson, Mr. Malkolm Joackim<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.001\", \"Passenger=Gillespie, Mr. William Henry<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.014\", \"Passenger=Allen, Miss. Elisabeth Walton<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.010\", \"Passenger=Berriman, Mr. William John<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.003\", \"Passenger=Troupiansky, Mr. Moses Aaron<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.003\", \"Passenger=Williams, Mr. Leslie<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.009\", \"Passenger=Ivanoff, Mr. Kanio<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.005\", \"Passenger=McNamee, Mr. Neal<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.026\", \"Passenger=Connaghton, Mr. Michael<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.003\", \"Passenger=Carlsson, Mr. August Sigfrid<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.014\", \"Passenger=Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.005\", \"Passenger=Eklund, Mr. Hans Linus<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.000\", \"Passenger=Hogeboom, Mrs. John C (Anna Andrews)<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.004\", \"Passenger=Moran, Mr. Daniel J<br>No_of_siblings_plus_spouses_on_board=1<br>shap=0.026\", \"Passenger=Lievens, Mr. Rene Aime<br>No_of_siblings_plus_spouses_on_board=0<br>shap=-0.004\", \"Passenger=Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)<br>No_of_siblings_plus_spouses_on_board=0<br>shap=0.013\", \"Passenger=Ayoub, Miss. 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Carla Christine Nielsine<br>Embarked_Cherbourg=0<br>shap=-0.018\", \"Passenger=Brown, Mrs. James Joseph (Margaret Tobin)<br>Embarked_Cherbourg=1<br>shap=-0.007\", \"Passenger=Vande Walle, Mr. Nestor Cyriel<br>Embarked_Cherbourg=0<br>shap=-0.012\", \"Passenger=Backstrom, Mr. Karl Alfred<br>Embarked_Cherbourg=0<br>shap=-0.010\", \"Passenger=Blank, Mr. Henry<br>Embarked_Cherbourg=1<br>shap=-0.007\", \"Passenger=Ali, Mr. Ahmed<br>Embarked_Cherbourg=0<br>shap=0.000\", \"Passenger=Green, Mr. George Henry<br>Embarked_Cherbourg=0<br>shap=-0.003\", \"Passenger=Nenkoff, Mr. Christo<br>Embarked_Cherbourg=0<br>shap=-0.011\", \"Passenger=Asplund, Miss. Lillian Gertrud<br>Embarked_Cherbourg=0<br>shap=-0.018\", \"Passenger=Harknett, Miss. 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Kristina Sofia<br>Embarked_Cherbourg=0<br>shap=-0.009\", \"Passenger=Penasco y Castellana, Mr. Victor de Satode<br>Embarked_Cherbourg=1<br>shap=0.023\", \"Passenger=Bradley, Mr. George (\\\"George Arthur Brayton\\\")<br>Embarked_Cherbourg=0<br>shap=0.002\", \"Passenger=Angle, Mrs. William A (Florence \\\"Mary\\\" Agnes Hughes)<br>Embarked_Cherbourg=0<br>shap=-0.006\", \"Passenger=Kassem, Mr. Fared<br>Embarked_Cherbourg=1<br>shap=0.051\", \"Passenger=Cacic, Miss. Marija<br>Embarked_Cherbourg=0<br>shap=-0.010\", \"Passenger=Hart, Miss. Eva Miriam<br>Embarked_Cherbourg=0<br>shap=-0.006\", \"Passenger=Butt, Major. Archibald Willingham<br>Embarked_Cherbourg=0<br>shap=0.007\", \"Passenger=Beane, Mr. Edward<br>Embarked_Cherbourg=0<br>shap=-0.004\", \"Passenger=Goldsmith, Mr. Frank John<br>Embarked_Cherbourg=0<br>shap=-0.013\", \"Passenger=Ohman, Miss. Velin<br>Embarked_Cherbourg=0<br>shap=-0.014\", \"Passenger=Taussig, Mrs. Emil (Tillie Mandelbaum)<br>Embarked_Cherbourg=0<br>shap=-0.009\", \"Passenger=Morrow, Mr. Thomas Rowan<br>Embarked_Cherbourg=0<br>shap=-0.012\", \"Passenger=Harris, Mr. George<br>Embarked_Cherbourg=0<br>shap=-0.007\", \"Passenger=Appleton, Mrs. Edward Dale (Charlotte Lamson)<br>Embarked_Cherbourg=0<br>shap=-0.003\", \"Passenger=Patchett, Mr. George<br>Embarked_Cherbourg=0<br>shap=-0.013\", \"Passenger=Ross, Mr. John Hugo<br>Embarked_Cherbourg=1<br>shap=0.036\", \"Passenger=Murdlin, Mr. Joseph<br>Embarked_Cherbourg=0<br>shap=-0.008\", \"Passenger=Bourke, Miss. Mary<br>Embarked_Cherbourg=0<br>shap=-0.009\", \"Passenger=Boulos, Mr. Hanna<br>Embarked_Cherbourg=1<br>shap=0.022\", \"Passenger=Duff Gordon, Sir. 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" {\"responsive\": true}\n",
" ).then(function(){\n",
" \n",
"var gd = document.getElementById('92de0838-0ca5-419e-a1f3-81a8d3dad214');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" })\n",
" };\n",
" \n",
" </script>\n",
" </div>\n",
"</body>\n",
"</html>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "sUU_XjI3AIUe"
},
"source": [
"\n",
"\n",
"ExplainerDashboard(explainer).run()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ALPrOGTPEQpw"
},
"source": [
"# see whether this actually generates the html for \n",
"card.layout.index"
],
"execution_count": null,
"outputs": []
}
]
}
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