{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2022-04-27T09:07:43.037744Z", "iopub.status.busy": "2022-04-27T09:07:43.037276Z", "iopub.status.idle": "2022-04-27T09:07:43.040920Z", "shell.execute_reply": "2022-04-27T09:07:43.040475Z" }, "id": "tuOe1ymfHZPu" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "23R0Z9RojXYW" }, "source": [ "# Scikit-Learn Model Card Toolkit Demo\n" ] }, { "cell_type": "markdown", "metadata": { "id": "MfBg1C5NB3X0" }, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " View on TensorFlow.org\n", " \n", " Run in Google Colab\n", " \n", " View on GitHub\n", " \n", " Download notebook\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "A5epNF_aiYj0" }, "source": [ "## Background\n", "This notebook demonstrates how to generate a model card using the Model Card Toolkit with a scikit-learn model in a Jupyter/Colab environment. You can learn more about model cards at [https://modelcards.withgoogle.com/about](https://modelcards.withgoogle.com/about).\n", "\n", "## Setup\n", "We first need to install and import the necessary packages.\n", "\n", "### Upgrade to Pip 20.2 and Install Packages" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:43.044075Z", "iopub.status.busy": "2022-04-27T09:07:43.043707Z", "iopub.status.idle": "2022-04-27T09:07:49.048715Z", "shell.execute_reply": "2022-04-27T09:07:49.048111Z" }, "id": "1OiOQJDPiYj3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pip==21.3 in /tmpfs/src/tf_docs_env/lib/python3.7/site-packages (21.3)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: You are using pip version 21.3; however, version 22.0.4 is available.\r\n", "You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip 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"metadata": { "id": "JpcNkrmLiYj7" }, "source": [ "### Did you restart the runtime?\n", "\n", "If you are using Google Colab, the first time that you run the cell above, you must restart the runtime (Runtime > Restart runtime ...)." ] }, { "cell_type": "markdown", "metadata": { "id": "YKbr6rJDC9bk" }, "source": [ "### Import packages\n", "\n", "We import necessary packages, including scikit-learn." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:49.052898Z", "iopub.status.busy": "2022-04-27T09:07:49.052411Z", "iopub.status.idle": "2022-04-27T09:07:52.517896Z", "shell.execute_reply": "2022-04-27T09:07:52.517321Z" }, "id": "y25vFI3WiYj7" }, "outputs": [], "source": [ "from datetime import date\n", "from io import BytesIO\n", "from IPython import display\n", "import model_card_toolkit as mctlib\n", "from sklearn.datasets import load_breast_cancer\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import plot_roc_curve, plot_confusion_matrix\n", "\n", "import base64\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import seaborn as sns\n", "import uuid" ] }, { "cell_type": "markdown", "metadata": { "id": "XVdpINibiYj-" }, "source": [ "## Load data\n", "\n", "This example uses the Breast Cancer Wisconsin Diagnostic dataset that scikit-learn can load using the [load_breast_cancer()](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html) function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:52.521737Z", "iopub.status.busy": "2022-04-27T09:07:52.521317Z", "iopub.status.idle": "2022-04-27T09:07:52.534056Z", "shell.execute_reply": "2022-04-27T09:07:52.533567Z" }, "id": "aR6kzqPeiYj_" }, "outputs": [], "source": [ "cancer = load_breast_cancer()\n", "\n", "X = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n", "y = pd.Series(cancer.target)\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:52.536990Z", "iopub.status.busy": "2022-04-27T09:07:52.536539Z", "iopub.status.idle": "2022-04-27T09:07:52.557915Z", "shell.execute_reply": "2022-04-27T09:07:52.557475Z" }, "id": "DwjxhVtTiYkB" }, "outputs": [ { "data": { "text/html": [ "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst radiusworst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimension
1716.1320.68108.10798.80.117000.202200.172200.102800.21640.07356...20.9631.48136.801315.00.17890.42330.47840.207300.37060.11420
11714.8716.6798.64682.50.116200.164900.169000.089230.21570.06768...18.8127.37127.101095.00.18780.44800.47040.202700.35850.10650
19512.9116.3382.53516.40.079410.053660.038730.023770.18290.05667...13.8822.0090.81600.60.10970.15060.17640.082350.30240.06949
33718.7721.43122.901092.00.091160.140200.106000.060900.19530.06083...24.5434.37161.101873.00.14980.48270.46340.204800.36790.09870
50915.4623.95103.80731.30.118300.187000.203000.085200.18070.07083...17.1136.33117.70909.40.17320.49670.59110.216300.30130.10670
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5 rows × 30 columns

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" ], "text/plain": [ " mean radius mean texture mean perimeter mean area mean smoothness \\\n", "17 16.13 20.68 108.10 798.8 0.11700 \n", "117 14.87 16.67 98.64 682.5 0.11620 \n", "195 12.91 16.33 82.53 516.4 0.07941 \n", "337 18.77 21.43 122.90 1092.0 0.09116 \n", "509 15.46 23.95 103.80 731.3 0.11830 \n", "\n", " mean compactness mean concavity mean concave points mean symmetry \\\n", "17 0.20220 0.17220 0.10280 0.2164 \n", "117 0.16490 0.16900 0.08923 0.2157 \n", "195 0.05366 0.03873 0.02377 0.1829 \n", "337 0.14020 0.10600 0.06090 0.1953 \n", "509 0.18700 0.20300 0.08520 0.1807 \n", "\n", " mean fractal dimension ... worst radius worst texture \\\n", "17 0.07356 ... 20.96 31.48 \n", "117 0.06768 ... 18.81 27.37 \n", "195 0.05667 ... 13.88 22.00 \n", "337 0.06083 ... 24.54 34.37 \n", "509 0.07083 ... 17.11 36.33 \n", "\n", " worst perimeter worst area worst smoothness worst compactness \\\n", "17 136.80 1315.0 0.1789 0.4233 \n", "117 127.10 1095.0 0.1878 0.4480 \n", "195 90.81 600.6 0.1097 0.1506 \n", "337 161.10 1873.0 0.1498 0.4827 \n", "509 117.70 909.4 0.1732 0.4967 \n", "\n", " worst concavity worst concave points worst symmetry \\\n", "17 0.4784 0.20730 0.3706 \n", "117 0.4704 0.20270 0.3585 \n", "195 0.1764 0.08235 0.3024 \n", "337 0.4634 0.20480 0.3679 \n", "509 0.5911 0.21630 0.3013 \n", "\n", " worst fractal dimension \n", "17 0.11420 \n", "117 0.10650 \n", "195 0.06949 \n", "337 0.09870 \n", "509 0.10670 \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:52.560702Z", "iopub.status.busy": "2022-04-27T09:07:52.560256Z", "iopub.status.idle": "2022-04-27T09:07:52.564379Z", "shell.execute_reply": "2022-04-27T09:07:52.563899Z" }, "id": "fCOK-1gyiYkE" }, "outputs": [ { "data": { "text/plain": [ "17 0\n", "117 0\n", "195 1\n", "337 0\n", "509 0\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "KmOnApwWiYkG" }, "source": [ "## Plot data\n", "\n", "We will create several plots from the data that we will include in the model card." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:52.567313Z", "iopub.status.busy": "2022-04-27T09:07:52.566873Z", "iopub.status.idle": "2022-04-27T09:07:52.569934Z", "shell.execute_reply": "2022-04-27T09:07:52.569529Z" }, "id": "O9n6rAV7iYkG" }, "outputs": [], "source": [ "# Utility function that will export a plot to a base-64 encoded string that the model card will accept.\n", "\n", "def plot_to_str():\n", " img = BytesIO()\n", " plt.savefig(img, format='png')\n", " return base64.encodebytes(img.getvalue()).decode('utf-8')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:52.572939Z", "iopub.status.busy": "2022-04-27T09:07:52.572459Z", "iopub.status.idle": "2022-04-27T09:07:53.352625Z", "shell.execute_reply": "2022-04-27T09:07:53.352067Z" }, "id": "lpZLJG3hiYkI" }, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the mean radius feature for both the train and test sets\n", "\n", "sns.displot(x=X_train['mean radius'], hue=y_train)\n", "mean_radius_train = plot_to_str()\n", "\n", "sns.displot(x=X_test['mean radius'], hue=y_test)\n", "mean_radius_test = plot_to_str()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:53.355734Z", "iopub.status.busy": "2022-04-27T09:07:53.355244Z", "iopub.status.idle": "2022-04-27T09:07:53.933975Z", "shell.execute_reply": "2022-04-27T09:07:53.933476Z" }, "id": "sFenUqQPiYkK" }, "outputs": [ { "data": { "image/png": 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\n", 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hbXXWWIrtBbbvsv0924/Zvria39fHfpL97vvjbvtI2w/Y/k617+uq+SfZvr/q+ujL1Zdv0AW6PkAkbZJ06AM5l0m6IyIWSbqjmu5Hm/Tr+y5Jn4yIJdVwS4dr6pT9kj4UESdLepOkD1Rd3vT7sW+131L/H/efS1oWEa+TtETScttvkvRxNfb9VZL+W9IF9ZWIZl0fIBFxr6SfHDJ7haRrqvFrJL2jkzV1Sot9HwgRsTsiHq7Gn5X0uKQT1OfHfpL97nvR8LNqcnY1hKRlkr5Sze+7Y97Luj5AWpgbEbur8R9LmltnMTW4yPZ3q0tcfXUJZyK2F0p6vaT7NUDH/pD9lgbguNsetv2IpL2Stkj6d0k/jYj91Sq7NCCB2gt6NUB+IRrfQx6k7yJ/VtIr1TjF3y3pE7VWU5jtl0j6qqRLIuKZ5mX9fOwn2O+BOO4RcSAilqjRQ8Upkn6n3oowmV4NkD2250lS9XNvzfV0TETsqf6RjUv6vBr/yPqS7dlq/Ce6OSJuqGb3/bGfaL8H6bhLUkT8VNJdkk6VdIztgw890/VRF+nVALlZ0spqfKWkm2qspaMO/udZOVvStlbr9jLblnS1pMcjYn3Tor4+9q32exCOu+05to+pxo+SdKYa94DuknROtVrfHfNe1vVPotu+TtJSNbp03iNpraSvSbpe0oikHZLOjYi+u9ncYt+XqnEZIyT9UNL7m+4J9A3bp0v6hqRHJY1Xs9eocT+gb4/9JPt9vvr8uNv+PTVukg+r8cvt9RHxUduvUOO9QcdJ+rakP4uIn9dXKQ7q+gABAHSnXr2EBQCoGQECAEghQAAAKQQIACCFAAEApBAgGEi232v7t6bx+TUzWQ/QiwgQDKr3SkoHiBrPZhyWpqepgb5AgKAWthfa/r7tTbZ/YHuz7bfavq9618cp1XovrjoPfMD2t22vaPr8N2w/XA2nVfOX2r7b9leq7W+unu5ubvscSaOSNlfv1jjK9u/bvsf2Q7Zvsz3P9stsb7e9uPrcdbbfZ/tKSUdVn91c1dL8zpZLbV9ejd9t+1O2t0q6eKJ2OvDHDZQREQwMHR8kLVTj3RevVeMXmYckbZRkNbps/1q13hVqPHksScdI+oGkF0v6DUlHVvMXSdpajS+V9D9q9Jk0JOlbkk6foP27JY1W47Ml/ZukOdX0uyRtrMbPrLZxnqRbmz7/s0P2ZVvT9KWSLm9q5zNTtcPA0IsDp9So039ExKOSZPsxNV4UFbYfVeM/ZUk6S9If2b60mj5SjW5M/lPSp20vkXRA0m83bfeBiNhVbfeRalvfnKSOxZJeI2lLdbIyrEaPt4qILbbfKekqSa9L7ueXp2oH6EUECOrU3J/ReNP0uH75d9OS/iQitjd/sLpEtEeN/9SHJP1fi+0e0NR/zy3psYg49dcW2EOSXi3pfyUdq8b7KA61X796OfjIQ5Y/N1U7QC/iHgi63W2SPnjwPobt11fzXyZpdzS6N3+PGr/NH45nJR1djW+XNMf2qVUbs23/brXsL9XoEfbdkv6x6mpdkl5oGt8j6eW2f9P2iyS9vUWbk7UD9BwCBN3uY2rcO/hudZnrY9X8z0haafs7arx06LkWn29lk6TPVZe4htXoLvzj1fYekXRadfP8QjXeUf4NSfdK+pvq8xuqmjZHxAuSPirpATXeovf9iRqMiOcnaucw6wa6Br3xAgBSOAMBAKQQIACAFAIEAJBCgAAAUggQAEAKAQIASCFAAAAp/w9pF5gxnX4VvQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the mean texture feature for both the train and test sets\n", "\n", "sns.displot(x=X_train['mean texture'], hue=y_train)\n", "mean_texture_train = plot_to_str()\n", "\n", "sns.displot(x=X_test['mean texture'], hue=y_test)\n", "mean_texture_test = plot_to_str()" ] }, { "cell_type": "markdown", "metadata": { "id": "hA7QthuhiYkM" }, "source": [ "## Train model" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:53.937161Z", "iopub.status.busy": "2022-04-27T09:07:53.936763Z", "iopub.status.idle": "2022-04-27T09:07:54.254570Z", "shell.execute_reply": "2022-04-27T09:07:54.254070Z" }, "id": "8VkTo7BxiYkN" }, "outputs": [], "source": [ "# Create a classifier and fit the training data\n", "\n", "clf = GradientBoostingClassifier().fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": { "id": "7fo-7XlIiYkP" }, "source": [ "## Evaluate model" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:54.257917Z", "iopub.status.busy": "2022-04-27T09:07:54.257505Z", "iopub.status.idle": "2022-04-27T09:07:54.403022Z", "shell.execute_reply": "2022-04-27T09:07:54.402455Z" }, "id": "_vEWAT2OiYkP" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n", " warnings.warn(msg, category=FutureWarning)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot a ROC curve\n", "\n", "plot_roc_curve(clf, X_test, y_test)\n", "roc_curve = plot_to_str()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:54.405661Z", "iopub.status.busy": "2022-04-27T09:07:54.405332Z", "iopub.status.idle": "2022-04-27T09:07:54.561717Z", "shell.execute_reply": "2022-04-27T09:07:54.561123Z" }, "id": "QiNgUZKxiYkR" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n", " warnings.warn(msg, category=FutureWarning)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot a confusion matrix\n", "\n", "plot_confusion_matrix(clf, X_test, y_test)\n", "confusion_matrix = plot_to_str()" ] }, { "cell_type": "markdown", "metadata": { "id": "gN48E4y-iYkT" }, "source": [ "## Create a model card" ] }, { "cell_type": "markdown", "metadata": { "id": "CBdRuxURiYkT" }, "source": [ "### Initialize toolkit and model card" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:54.564994Z", "iopub.status.busy": "2022-04-27T09:07:54.564574Z", "iopub.status.idle": "2022-04-27T09:07:54.568245Z", "shell.execute_reply": "2022-04-27T09:07:54.567778Z" }, "id": "CI9ganKQiYkT" }, "outputs": [], "source": [ "mct = mctlib.ModelCardToolkit()\n", "\n", "model_card = mct.scaffold_assets()" ] }, { "cell_type": "markdown", "metadata": { "id": "CERQtrHWiYkV" }, "source": [ "### Annotate information into model card" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:54.571582Z", "iopub.status.busy": "2022-04-27T09:07:54.571178Z", "iopub.status.idle": "2022-04-27T09:07:54.580065Z", "shell.execute_reply": "2022-04-27T09:07:54.579612Z" }, "id": "TLzNJ_kriYkV" }, "outputs": [], "source": [ "model_card.model_details.name = 'Breast Cancer Wisconsin (Diagnostic) Dataset'\n", "model_card.model_details.overview = (\n", " 'This model predicts whether breast cancer is benign or malignant based on '\n", " 'image measurements.')\n", "model_card.model_details.owners = [\n", " mctlib.Owner(name= 'Model Cards Team', contact='model-cards@google.com')\n", "]\n", "model_card.model_details.references = [\n", " mctlib.Reference(reference='https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)'),\n", " mctlib.Reference(reference='https://minds.wisconsin.edu/bitstream/handle/1793/59692/TR1131.pdf')\n", "]\n", "model_card.model_details.version.name = str(uuid.uuid4())\n", "model_card.model_details.version.date = str(date.today())\n", "\n", "model_card.considerations.ethical_considerations = [mctlib.Risk(\n", " name=('Manual selection of image sections to digitize could create '\n", " 'selection bias'),\n", " mitigation_strategy='Automate the selection process'\n", ")]\n", "model_card.considerations.limitations = [mctlib.Limitation(description='Breast cancer diagnosis')]\n", "model_card.considerations.use_cases = [mctlib.UseCase(description='Breast cancer diagnosis')]\n", "model_card.considerations.users = [mctlib.User(description='Medical professionals'), mctlib.User(description='ML researchers')]\n", "\n", "model_card.model_parameters.data.append(mctlib.Dataset())\n", "model_card.model_parameters.data[0].graphics.description = (\n", " f'{len(X_train)} rows with {len(X_train.columns)} features')\n", "model_card.model_parameters.data[0].graphics.collection = [\n", " mctlib.Graphic(image=mean_radius_train),\n", " mctlib.Graphic(image=mean_texture_train)\n", "]\n", "model_card.model_parameters.data.append(mctlib.Dataset())\n", "model_card.model_parameters.data[1].graphics.description = (\n", " f'{len(X_test)} rows with {len(X_test.columns)} features')\n", "model_card.model_parameters.data[1].graphics.collection = [\n", " mctlib.Graphic(image=mean_radius_test),\n", " mctlib.Graphic(image=mean_texture_test)\n", "]\n", "model_card.quantitative_analysis.graphics.description = (\n", " 'ROC curve and confusion matrix')\n", "model_card.quantitative_analysis.graphics.collection = [\n", " mctlib.Graphic(image=roc_curve),\n", " mctlib.Graphic(image=confusion_matrix)\n", "]\n", "\n", "mct.update_model_card(model_card)" ] }, { "cell_type": "markdown", "metadata": { "id": "TBqFqMHEiYkX" }, "source": [ "## Generate model card" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2022-04-27T09:07:54.582997Z", "iopub.status.busy": "2022-04-27T09:07:54.582515Z", "iopub.status.idle": "2022-04-27T09:07:54.623587Z", "shell.execute_reply": "2022-04-27T09:07:54.623083Z" }, "id": "XUEG7n7ciYkY" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " Model Card for Breast Cancer Wisconsin (Diagnostic) Dataset\n", "\n", "\n", "\n", "

\n", " Model Card for Breast Cancer Wisconsin (Diagnostic) Dataset\n", "

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Model Details

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Overview

\n", " This model predicts whether breast cancer is benign or malignant based on image measurements.\n", "

Version

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name: b5fe505a-3f3c-4839-a668-c68f0ab32b21
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date: 2022-04-27
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Owners

\n", " \n", " Model Cards Team, model-cards@google.com\n", " \n", " \n", " \n", " \n", "

References

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Considerations

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Intended Users

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  • Medical professionals
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  • ML researchers
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Use Cases

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  • Breast cancer diagnosis
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Limitations

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  • Breast cancer diagnosis
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Ethical Considerations

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  • \n", "
    Risk: Manual selection of image sections to digitize could create selection bias
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    Mitigation Strategy: Automate the selection process
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Datasets

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Quantitative Analysis

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\n", " ROC curve and confusion matrix\n", " \n", "
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\n", "\n", " \n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Return the model card document as an HTML page\n", "\n", "html = mct.export_format()\n", "\n", "display.display(display.HTML(html))" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "Scikit_Learn_Model_Card_Toolkit_Demo.ipynb", "provenance": [], "toc_visible": true }, "environment": { "name": "common-cpu.m56", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/base-cpu:m56" }, "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": "3.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }