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"grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "UZWrB1VnAGmy", "colab_type": "text" }, "source": [ "# SHAP\n", "\n", "`SHAP` is a library for interpreting neural networks, and we can use it to help us with tabular data too! I wrote a library called `FastSHAP` which ports over the usabilities of it. Let's do a walkthrough of what each does and how it works.\n", "\n", "* Note: I only have it ported for tabular data\n", "\n", "First let's install our libraries:" ] }, { "cell_type": "code", "metadata": { "id": "-2JzM5awAGmz", "colab_type": "code", "colab": {} }, "source": [ "!pip install fastinference fastai" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "bj2OvIDKAGm3", "colab_type": "text" }, "source": [ "## How to use it\n", "\n", "FIrst we need to train a model. We'll quickly train our `ADULTS` model now:" ] }, { "cell_type": "code", "metadata": { "id": "D5Ark17LAGm3", "colab_type": "code", "colab": {} }, "source": [ "from fastai.tabular.all import *" ], "execution_count": 2, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "63ATq7poAGm6", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, "outputId": "d6be891b-3c6f-4e2a-e23b-3488112014d0" }, "source": [ "path = untar_data(URLs.ADULT_SAMPLE)\n", "df = pd.read_csv(path/'adult.csv')" ], "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "aC2yQOlrAGm9", "colab_type": "code", "colab": {} }, "source": [ "dep_var = 'salary'\n", "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']\n", "cont_names = ['age', 'fnlwgt', 'education-num']\n", "procs = [Categorify, FillMissing, Normalize]" ], "execution_count": 4, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "mzJxKKXNAGnB", "colab_type": "code", "colab": {} }, "source": [ "splits = IndexSplitter(list(range(800,1000)))(range_of(df))\n", "to = TabularPandas(df, procs, cat_names, cont_names, y_names=\"salary\", splits=splits)\n", "dls = to.dataloaders()" ], "execution_count": 5, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "JM_oZH9BAGnE", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 77 }, "outputId": "9ab2689e-b3e7-4534-f21c-1645e9570ca8" }, "source": [ "learn = tabular_learner(dls, layers=[200,100], metrics=accuracy)\n", "learn.fit(1, 1e-2)" ], "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
epochtrain_lossvalid_lossaccuracytime
00.3695590.4826480.83500000:07
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "NJKtABLqAGnG", "colab_type": "text" }, "source": [ "Now let's go through some example usage!" ] }, { "cell_type": "markdown", "metadata": { "id": "tS1Kc1doAGnH", "colab_type": "text" }, "source": [ "# `fastinference`\n", "\n", "First let's import the interpretability module:" ] }, { "cell_type": "code", "metadata": { "id": "_gHP6ux4AGnH", "colab_type": "code", "colab": {} }, "source": [ "from fastinference.tabular import *" ], "execution_count": 7, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "tu9AKdLiAGnK", "colab_type": "text" }, "source": [ "And now we'll make a `ShapInterpretation` object. It expects your `Learner` along with some test data to look at and any keywords that `SHAP` can use. If you don't pass anything in it will use a subset of your validation data:" ] }, { "cell_type": "code", "metadata": { "id": "ahkBsXf_AGnK", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "2bb24d85876d4f2f91c3947b1cfc7e90", "a282185de0ba46c6af41ea0389aa7bd2", "67d28e16948a4ad49a255a3d554bebc3", "d110d8b928e649b7b792f5952eda10f8", "00ba7a9f780941c98833a1297a9e564c", "bc98c362a18c426aba2d223937a75e40", "1021c3e7fa644c0794db812e35a00c1e", "c444ba52585a47f8b6ec6d714d1ffc2a" ] }, "outputId": "0a5bc4d6-bd46-45f9-b6e9-ecc8da97f5d6" }, "source": [ "exp = ShapInterpretation(learn, df.iloc[:100])" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2bb24d85876d4f2f91c3947b1cfc7e90", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0), HTML(value='')))" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "hBTMDC6rAGnM", "colab_type": "text" }, "source": [ "Let's look at the various methods available to us:" ] }, { "cell_type": "markdown", "metadata": { "id": "9ADxJBxaAGnN", "colab_type": "text" }, "source": [ "## Decision Plot\n", "\n", "The decisio plot will visualize a model's decision by looking at the \"SHAP\" values for a particular row. If you plot too many samples at once it can make your plot illegible.\n", "\n", "Let's look at the tenth row of our dataframe:" ] }, { "cell_type": "code", "metadata": { "id": "5uPnwYzpAGnN", "colab_type": "code", "colab": {}, "outputId": "cd5ab5a3-b64f-4a6f-817d-4d9d513bbf38" }, "source": [ "df.iloc[10]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "age 23\n", "workclass Private\n", "fnlwgt 529223\n", "education Bachelors\n", "education-num 13\n", "marital-status Never-married\n", "occupation NaN\n", "relationship Own-child\n", "race Black\n", "sex Male\n", "capital-gain 0\n", "capital-loss 0\n", "hours-per-week 10\n", "native-country United-States\n", "salary <50k\n", "Name: 10, dtype: object" ] }, "metadata": { "tags": [] }, "execution_count": 0 } ] }, { "cell_type": "markdown", "metadata": { "id": "qmY7b27qAGnP", "colab_type": "text" }, "source": [ "As we can see, our `y` value is '<50k'. Let's look at how the model performed and what could have been influencing our result into an *opposite* direction" ] }, { "cell_type": "code", "metadata": { "id": "oUL-c-gqAGnQ", "colab_type": "code", "colab": {}, "outputId": "793225ba-77a3-4d35-a634-64f110eb5d96" }, "source": [ "exp.decision_plot(class_id=0, row_idx=10)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Classification model detected, displaying score for the class <50k.\n", "(use `class_id` to specify another class)\n", "Displaying row 10 of 100 (use `row_idx` to specify another row)\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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L3OCcSyln+5K8AvwbLxnsDlzqnKtMEiZSuxQs5j53greY++t1MPZibzH3zNEh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\nIfx3COGYDvog45jTQwhVwMPAUUmfm9ubjgwh/DKEcH+bsit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"text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "N0J7cuQBAGnT", "colab_type": "text" }, "source": [ "And now if we turn this the other way around:" ] }, { "cell_type": "code", "metadata": { "id": "gz9iQfO5AGnT", "colab_type": "code", "colab": {}, "outputId": "45b52856-20e9-4bda-b727-e97d5bdb8d99" }, "source": [ "exp.decision_plot(class_id=1, row_idx=10)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Classification model detected, displaying score for the class >=50k.\n", "(use `class_id` to specify another class)\n", "Displaying row 10 of 100 (use `row_idx` to specify another row)\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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Xh9N6uYzo5tC8sQKUVJ1Ck0hMVfqGXV8mgovEXOdmDv9zeID/ORw2FHpkLfd47tsIl73j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\nJh5t2Q+IiKfChrSh/1EtxLSceERsu3p3X5+yvwXon8QwC5i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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "GvoKxM6PAGnV", "colab_type": "text" }, "source": [ "We can visually see what variables were having the largest impact on the model. (Note, it shows the pre-processed datapoints)" ] }, { "cell_type": "markdown", "metadata": { "id": "cJDwFPhZAGnW", "colab_type": "text" }, "source": [ "## Dependency Plots\n", "\n", "Dependency plots use the same variable on the x and y axis, with the y axis being the \"SHAP\" values of it. We can pass in a variable name and a particular class ID and it will show the dependency plot for all of the test data we passed in:" ] }, { "cell_type": "code", "metadata": { "id": "fGF6qoi-AGnW", "colab_type": "code", "colab": {}, "outputId": "b8b70540-242f-4f13-a619-a2bd8c2b2e79" }, "source": [ "exp.dependence_plot('age', class_id=0)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Classification model detected, displaying score for the class <50k.\n", "(use `class_id` to specify another class)\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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IvdSwO/el3yljSd97IKMfO4TUkVmxDkkppRKWiDwUve+cW+ec+0ZE/uy3jlZb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\nOCQlJnE0Z/Kembz+TAE11Y6UVGGSzq7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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "UkswidckAGnY", "colab_type": "code", "colab": {}, "outputId": "87bdc1b6-3fbe-45df-f28b-fb47a8e757eb" }, "source": [ "exp.dependence_plot('age', class_id=1)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Classification model detected, displaying score for the class >=50k.\n", "(use `class_id` to specify another class)\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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mFP6Mwb+ZDICrDrFq9ptUfFvEjoVbWHP2u10er1JKdbbe0iU+Alhvra0BsNbW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\n75y7zmcdbaYJWymlVK8iIjcC04BjnXM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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "jbruWzF0AGna", "colab_type": "text" }, "source": [ "## Force Plot\n", "\n", "Fore plots will visualize the \"SHAP\" values with an added force layout. We can see how each variable at a certain value affects whether it falls into class A or class B:" ] }, { "cell_type": "code", "metadata": { "id": "Ew7W2feXAGnb", "colab_type": "code", "colab": {}, "outputId": "62e718d6-803e-4029-bd47-c058147ca1a4" }, "source": [ "exp.force_plot(class_id=1)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Classification model detected, displaying score for the class >=50k.\n", "(use `class_id` to specify another class)\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "execute_result", "data": { "text/html": [ "\n", "
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\n", " Visualization omitted, Javascript library not loaded!
\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security. If you are using\n", " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 0 } ] }, { "cell_type": "markdown", "metadata": { "id": "TBw2I2g9AGnd", "colab_type": "text" }, "source": [ "## Summary Plot\n", "\n", "Similar to feature importance, it shows the average impoact of a particular value on model performance:" ] }, { "cell_type": "code", "metadata": { "id": "MryVQRRVAGnd", "colab_type": "code", "colab": {}, "outputId": "90b77258-08c3-408a-dae7-e6e374c1f48c" }, "source": [ "exp.summary_plot()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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IiEhTtISJ4KcCrwLTzGwp8CJx4nMGnEO8/PVKWl9FLjlw92nAL4iTsRcSJ08/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\nW/NzdbIQwuuk16SBc6AnceRmViPiK9W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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "lpiJK-RAAGng", "colab_type": "text" }, "source": [ "## Waterfall Plot\n", "\n", "And finally the waterfall plot. It'll explain a single prediction. It can accept a `row_index` and a `class_id` which defualts to the first one. It can be an integer or string representation of the class we want to look at. Let's look at that row 10 again:" ] }, { "cell_type": "code", "metadata": { "id": "gnd1DL52AGnh", "colab_type": "code", "colab": {}, "outputId": "49857e3a-ece0-415b-a31e-c71ef846134c" }, "source": [ "exp.waterfall_plot(row_idx=10, class_id='<50k')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Classification model detected, displaying score for the class <50k.\n", "(use `class_id` to specify another class)\n", "Displaying row 10 of 100 (use `row_idx` to specify another row)\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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UZ5VKpWuwhJCIKJgEbdPqcDx+m3fet+/fCty12Ft+/Dbvc5PGA91vev8FgCduA8bpgVX/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