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Scikit_Learn_Model_Card_Toolkit_Demo.ipynb
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/codesue/4ef3148e29f8d5c5c099ce4ee426a98d/scikit_learn_model_card_toolkit_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Tce3stUlHN0L" | |
| }, | |
| "source": [ | |
| "##### Copyright 2020 The TensorFlow Authors." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "cellView": "form", | |
| "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": [ | |
| "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", | |
| " <td>\n", | |
| " <a target=\"_blank\" href=\"https://www.tensorflow.org/responsible_ai/model_card_toolkit/examples/Scikit_Learn_Model_Card_Toolkit_Demo\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/model-card-toolkit/blob/main/model_card_toolkit/documentation/examples/Scikit_Learn_Model_Card_Toolkit_Demo.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <a target=\"_blank\" href=\"https://github.com/tensorflow/model-card-toolkit/blob/main/model_card_toolkit/documentation/examples/Scikit_Learn_Model_Card_Toolkit_Demo.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View on GitHub</a>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <a href=\"https://storage.googleapis.com/tensorflow_docs/model-card-toolkit/model_card_toolkit/documentation/examples/Scikit_Learn_Model_Card_Toolkit_Demo.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", | |
| " </td>\n", | |
| "</table>" | |
| ] | |
| }, | |
| { | |
| "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 and Install Packages" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "id": "1OiOQJDPiYj3", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "f0442720-b258-4e80-9ff8-3ec0f9436d49" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
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| "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.9/dist-packages (from oauth2client>=1.4.12->google-apitools<0.5.32,>=0.5.31->apache-beam[gcp]<3,>=2.40->tensorflow-data-validation<2.0.0,>=1.5.0->model-card-toolkit==2.0.0rc0) (0.4.8)\n", | |
| "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.9/dist-packages (from jupyter-core>=4.6.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (3.2.0)\n", | |
| "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.13,>=2.12->tensorflow<3,>=2.11->tensorflow-data-validation<2.0.0,>=1.5.0->model-card-toolkit==2.0.0rc0) (3.2.2)\n", | |
| "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.9/dist-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (21.2.0)\n", | |
| "Requirement already satisfied: bleach in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (6.0.0)\n", | |
| "Requirement already satisfied: defusedxml in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (0.7.1)\n", | |
| "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (0.2.2)\n", | |
| "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (1.2.1)\n", | |
| "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (0.8.4)\n", | |
| "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (1.5.0)\n", | |
| "Requirement already satisfied: lxml in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (4.9.2)\n", | |
| "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (0.4)\n", | |
| "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (4.11.2)\n", | |
| "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (0.7.2)\n", | |
| "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.9/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (2.16.3)\n", | |
| "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (1.15.1)\n", | |
| "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.9/dist-packages (from beautifulsoup4->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (2.4)\n", | |
| "Requirement already satisfied: webencodings in /usr/local/lib/python3.9/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (0.5.1)\n", | |
| "Requirement already satisfied: pycparser in /usr/local/lib/python3.9/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8,>=7->tensorflow-model-analysis<0.42.0,>=0.36.0->model-card-toolkit==2.0.0rc0) (2.21)\n", | |
| "Building wheels for collected packages: pyfarmhash, crcmod, dill, google-apitools\n", | |
| " Building wheel for pyfarmhash (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for pyfarmhash: filename=pyfarmhash-0.3.2-cp39-cp39-linux_x86_64.whl size=101495 sha256=a8d7d90d5a8a6b53ecb529a52be841656beaf3502273776515096d600351b6df\n", | |
| " Stored in directory: /root/.cache/pip/wheels/de/2b/b1/c541160670d70f4b08c4786f4e155337d4baeaa3e01d9d1400\n", | |
| " Building wheel for crcmod (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for crcmod: filename=crcmod-1.7-cp39-cp39-linux_x86_64.whl size=36920 sha256=1825b0c1c497ce6345094a921fbb22ad686a47cc87136a092c1b2be94a8dbe49\n", | |
| " Stored in directory: /root/.cache/pip/wheels/4a/6c/a6/ffdd136310039bf226f2707a9a8e6857be7d70a3fc061f6b36\n", | |
| " Building wheel for dill (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78545 sha256=667815554749c74386d5a8969b25536473ae837eed7858fd5fcc1b48dc3a0de4\n", | |
| " Stored in directory: /root/.cache/pip/wheels/4f/0b/ce/75d96dd714b15e51cb66db631183ea3844e0c4a6d19741a149\n", | |
| " Building wheel for google-apitools (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for google-apitools: filename=google_apitools-0.5.31-py3-none-any.whl size=131038 sha256=1b3727a241b3b67c8eadb5730ce9a7ce0baf58b15c53df498793c25478979f55\n", | |
| " Stored in directory: /root/.cache/pip/wheels/6c/f8/60/b9e91899dbaf25b6314047d3daee379bdd8d61b1dc3fd5ec7f\n", | |
| "Successfully built pyfarmhash crcmod dill google-apitools\n", | |
| "Installing collected packages: pyfarmhash, joblib, docopt, crcmod, zstandard, uritemplate, pymongo, pyarrow, overrides, orjson, objsize, jedi, fasteners, fastavro, dill, cachetools, attrs, absl-py, tensorflow-metadata, ml-metadata, jsonschema, hdfs, grpc-google-iam-v1, google-apitools, apache-beam, google-api-python-client, google-cloud-vision, google-cloud-videointelligence, google-cloud-spanner, google-cloud-recommendations-ai, google-cloud-pubsub, google-cloud-language, google-cloud-dlp, google-cloud-datastore, google-cloud-bigtable, google-cloud-bigquery-storage, tensorflow-serving-api, google-cloud-pubsublite, tfx-bsl, tensorflow-model-analysis, tensorflow-data-validation, model-card-toolkit\n", | |
| " Attempting uninstall: joblib\n", | |
| " Found existing installation: joblib 1.1.1\n", | |
| " Uninstalling joblib-1.1.1:\n", | |
| " Successfully uninstalled joblib-1.1.1\n", | |
| " Attempting uninstall: uritemplate\n", | |
| " Found existing installation: uritemplate 4.1.1\n", | |
| " Uninstalling uritemplate-4.1.1:\n", | |
| " Successfully uninstalled uritemplate-4.1.1\n", | |
| " Attempting uninstall: pyarrow\n", | |
| " Found existing installation: pyarrow 9.0.0\n", | |
| " Uninstalling pyarrow-9.0.0:\n", | |
| " Successfully uninstalled pyarrow-9.0.0\n", | |
| " Attempting uninstall: cachetools\n", | |
| " Found existing installation: cachetools 5.3.0\n", | |
| " Uninstalling cachetools-5.3.0:\n", | |
| " Successfully uninstalled cachetools-5.3.0\n", | |
| " Attempting uninstall: attrs\n", | |
| " Found existing installation: attrs 22.2.0\n", | |
| " Uninstalling attrs-22.2.0:\n", | |
| " Successfully uninstalled attrs-22.2.0\n", | |
| " Attempting uninstall: absl-py\n", | |
| " Found existing installation: absl-py 1.4.0\n", | |
| " Uninstalling absl-py-1.4.0:\n", | |
| " Successfully uninstalled absl-py-1.4.0\n", | |
| " Attempting uninstall: tensorflow-metadata\n", | |
| " Found existing installation: tensorflow-metadata 1.12.0\n", | |
| " Uninstalling tensorflow-metadata-1.12.0:\n", | |
| " Successfully uninstalled tensorflow-metadata-1.12.0\n", | |
| " Attempting uninstall: jsonschema\n", | |
| " Found existing installation: jsonschema 4.3.3\n", | |
| " Uninstalling jsonschema-4.3.3:\n", | |
| " Successfully uninstalled jsonschema-4.3.3\n", | |
| " Attempting uninstall: google-api-python-client\n", | |
| " Found existing installation: google-api-python-client 2.70.0\n", | |
| " Uninstalling google-api-python-client-2.70.0:\n", | |
| " Successfully uninstalled google-api-python-client-2.70.0\n", | |
| " Attempting uninstall: google-cloud-language\n", | |
| " Found existing installation: google-cloud-language 2.6.1\n", | |
| " Uninstalling google-cloud-language-2.6.1:\n", | |
| " Successfully uninstalled google-cloud-language-2.6.1\n", | |
| " Attempting uninstall: google-cloud-datastore\n", | |
| " Found existing installation: google-cloud-datastore 2.11.1\n", | |
| " Uninstalling google-cloud-datastore-2.11.1:\n", | |
| " Successfully uninstalled google-cloud-datastore-2.11.1\n", | |
| " Attempting uninstall: google-cloud-bigquery-storage\n", | |
| " Found existing installation: google-cloud-bigquery-storage 2.19.1\n", | |
| " Uninstalling google-cloud-bigquery-storage-2.19.1:\n", | |
| " Successfully uninstalled google-cloud-bigquery-storage-2.19.1\n", | |
| "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", | |
| "scikit-learn 1.2.2 requires joblib>=1.1.1, but you have joblib 0.14.1 which is incompatible.\n", | |
| "pandas-profiling 3.2.0 requires joblib~=1.1.0, but you have joblib 0.14.1 which is incompatible.\n", | |
| "imbalanced-learn 0.10.1 requires joblib>=1.1.1, but you have joblib 0.14.1 which is incompatible.\u001b[0m\u001b[31m\n", | |
| "\u001b[0mSuccessfully installed absl-py-1.0.0 apache-beam-2.46.0 attrs-21.4.0 cachetools-4.2.4 crcmod-1.7 dill-0.3.1.1 docopt-0.6.2 fastavro-1.7.3 fasteners-0.18 google-api-python-client-1.12.11 google-apitools-0.5.31 google-cloud-bigquery-storage-2.16.2 google-cloud-bigtable-1.7.3 google-cloud-datastore-1.15.5 google-cloud-dlp-3.12.1 google-cloud-language-1.3.2 google-cloud-pubsub-2.15.2 google-cloud-pubsublite-1.8.1 google-cloud-recommendations-ai-0.7.1 google-cloud-spanner-3.29.0 google-cloud-videointelligence-1.16.3 google-cloud-vision-3.4.1 grpc-google-iam-v1-0.12.6 hdfs-2.7.0 jedi-0.18.2 joblib-0.14.1 jsonschema-3.2.0 ml-metadata-1.12.0 model-card-toolkit-2.0.0rc0 objsize-0.6.1 orjson-3.8.9 overrides-6.5.0 pyarrow-6.0.1 pyfarmhash-0.3.2 pymongo-3.13.0 tensorflow-data-validation-1.10.0 tensorflow-metadata-1.10.0 tensorflow-model-analysis-0.41.1 tensorflow-serving-api-2.10.1 tfx-bsl-1.10.1 uritemplate-3.0.1 zstandard-0.20.0\n", | |
| "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", | |
| "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
| "Requirement already satisfied: seaborn in /usr/local/lib/python3.9/dist-packages (0.12.2)\n", | |
| "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/dist-packages (1.2.2)\n", | |
| "Requirement already satisfied: model-card-toolkit in /usr/local/lib/python3.9/dist-packages (2.0.0rc0)\n", | |
| "Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.9/dist-packages (from seaborn) (1.4.4)\n", | |
| "Requirement already satisfied: numpy!=1.24.0,>=1.17 in /usr/local/lib/python3.9/dist-packages (from seaborn) (1.22.4)\n", | |
| "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /usr/local/lib/python3.9/dist-packages (from seaborn) (3.7.1)\n", | |
| "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.9/dist-packages (from scikit-learn) (1.10.1)\n", | |
| "Collecting joblib>=1.1.1\n", | |
| " Using cached joblib-1.2.0-py3-none-any.whl (297 kB)\n", | |
| "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn) (3.1.0)\n", | |
| "Requirement already satisfied: ml-metadata<2.0.0,>=1.5.0 in /usr/local/lib/python3.9/dist-packages (from model-card-toolkit) (1.12.0)\n", | |
| "Requirement already satisfied: tensorflow-data-validation<2.0.0,>=1.5.0 in /usr/local/lib/python3.9/dist-packages (from model-card-toolkit) (1.10.0)\n", | |
| "Requirement already satisfied: absl-py<1.1,>=0.9 in /usr/local/lib/python3.9/dist-packages (from model-card-toolkit) (1.0.0)\n", | |
| "Requirement already satisfied: jinja2<3.2,>=3.1 in /usr/local/lib/python3.9/dist-packages (from model-card-toolkit) (3.1.2)\n", | |
| "Requirement already satisfied: tensorflow-metadata<2.0.0,>=1.5.0 in /usr/local/lib/python3.9/dist-packages (from model-card-toolkit) (1.10.0)\n", | |
| "Requirement already satisfied: tensorflow-model-analysis<0.42.0,>=0.36.0 in /usr/local/lib/python3.9/dist-packages (from model-card-toolkit) (0.41.1)\n", | |
| "Requirement already satisfied: jsonschema<4,>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from model-card-toolkit) (3.2.0)\n", | |
| "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from absl-py<1.1,>=0.9->model-card-toolkit) (1.16.0)\n", | |
| "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2<3.2,>=3.1->model-card-toolkit) (2.1.2)\n", | |
| "Requirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from jsonschema<4,>=3.2.0->model-card-toolkit) (67.6.1)\n", | |
| "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.9/dist-packages (from jsonschema<4,>=3.2.0->model-card-toolkit) (21.4.0)\n", | |
| "Requirement already satisfied: pyrsistent>=0.14.0 in /usr/local/lib/python3.9/dist-packages (from jsonschema<4,>=3.2.0->model-card-toolkit) (0.19.3)\n", | |
| "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n", | |
| "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (8.4.0)\n", | |
| "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.7)\n", | |
| "Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (5.12.0)\n", | |
| "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.39.3)\n", | |
| "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n", | |
| "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n", | |
| "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n", | |
| "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\n", | |
| "Requirement already satisfied: grpcio<2,>=1.8.6 in /usr/local/lib/python3.9/dist-packages (from ml-metadata<2.0.0,>=1.5.0->model-card-toolkit) (1.53.0)\n", | |
| "Requirement already satisfied: protobuf<4,>=3.13 in /usr/local/lib/python3.9/dist-packages (from ml-metadata<2.0.0,>=1.5.0->model-card-toolkit) (3.20.3)\n", | |
| "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.25->seaborn) (2022.7.1)\n", | |
| "Requirement already satisfied: tfx-bsl<1.11,>=1.10.1 in /usr/local/lib/python3.9/dist-packages (from tensorflow-data-validation<2.0.0,>=1.5.0->model-card-toolkit) (1.10.1)\n", | |
| "Requirement already satisfied: pyarrow<7,>=6 in /usr/local/lib/python3.9/dist-packages (from tensorflow-data-validation<2.0.0,>=1.5.0->model-card-toolkit) (6.0.1)\n", | |
| "Requirement already satisfied: pyfarmhash<0.4,>=0.2 in /usr/local/lib/python3.9/dist-packages (from tensorflow-data-validation<2.0.0,>=1.5.0->model-card-toolkit) (0.3.2)\n", | |
| "Requirement already satisfied: tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,<3,>=1.15.5 in /usr/local/lib/python3.9/dist-packages (from tensorflow-data-validation<2.0.0,>=1.5.0->model-card-toolkit) (2.12.0)\n", | |
| "Collecting tensorflow-data-validation<2.0.0,>=1.5.0\n", | |
| " Using cached tensorflow_data_validation-1.12.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (18.2 MB)\n", | |
| "Collecting tfx-bsl<1.13,>=1.12.0\n", | |
| " Using cached tfx_bsl-1.12.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (21.6 MB)\n", | |
| "Collecting tensorflow-metadata<2.0.0,>=1.5.0\n", | |
| " Using cached tensorflow_metadata-1.12.0-py3-none-any.whl (52 kB)\n", | |
| "Requirement already satisfied: apache-beam[gcp]<3,>=2.40 in /usr/local/lib/python3.9/dist-packages (from tensorflow-data-validation<2.0.0,>=1.5.0->model-card-toolkit) (2.46.0)\n", | |
| "Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.9/dist-packages (from tensorflow-metadata<2.0.0,>=1.5.0->model-card-toolkit) (1.59.0)\n", | |
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| "Collecting tensorflow-model-analysis<0.42.0,>=0.36.0\n", | |
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| "Installing collected packages: scikit-learn\n", | |
| " Attempting uninstall: scikit-learn\n", | |
| " Found existing installation: scikit-learn 1.2.2\n", | |
| " Uninstalling scikit-learn-1.2.2:\n", | |
| " Successfully uninstalled scikit-learn-1.2.2\n", | |
| "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", | |
| "imbalanced-learn 0.10.1 requires joblib>=1.1.1, but you have joblib 0.14.1 which is incompatible.\u001b[0m\u001b[31m\n", | |
| "\u001b[0mSuccessfully installed scikit-learn-1.0.2\n", | |
| "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", | |
| "\u001b[0m" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!pip install --upgrade pip\n", | |
| "!pip install -i https://test.pypi.org/simple/ model-card-toolkit==2.0.0rc0 --extra-index-url https://pypi.org/simple\n", | |
| "!pip install --upgrade seaborn scikit-learn model-card-toolkit" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "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": 1, | |
| "metadata": { | |
| "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": 2, | |
| "metadata": { | |
| "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": 3, | |
| "metadata": { | |
| "id": "DwjxhVtTiYkB", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 331 | |
| }, | |
| "outputId": "5b5a38c1-cdfb-4d30-b8f3-4c55bf2fcd37" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " mean radius mean texture mean perimeter mean area mean smoothness \\\n", | |
| "211 11.84 18.94 75.51 428.0 0.08871 \n", | |
| "203 13.81 23.75 91.56 597.8 0.13230 \n", | |
| "255 13.96 17.05 91.43 602.4 0.10960 \n", | |
| "450 11.87 21.54 76.83 432.0 0.06613 \n", | |
| "215 13.86 16.93 90.96 578.9 0.10260 \n", | |
| "\n", | |
| " mean compactness mean concavity mean concave points mean symmetry \\\n", | |
| "211 0.0690 0.02669 0.01393 0.1533 \n", | |
| "203 0.1768 0.15580 0.09176 0.2251 \n", | |
| "255 0.1279 0.09789 0.05246 0.1908 \n", | |
| "450 0.1064 0.08777 0.02386 0.1349 \n", | |
| "215 0.1517 0.09901 0.05602 0.2106 \n", | |
| "\n", | |
| " mean fractal dimension ... worst radius worst texture \\\n", | |
| "211 0.06057 ... 13.30 24.99 \n", | |
| "203 0.07421 ... 19.20 41.85 \n", | |
| "255 0.06130 ... 16.39 22.07 \n", | |
| "450 0.06612 ... 12.79 28.18 \n", | |
| "215 0.06916 ... 15.75 26.93 \n", | |
| "\n", | |
| " worst perimeter worst area worst smoothness worst compactness \\\n", | |
| "211 85.22 546.3 0.12800 0.1880 \n", | |
| "203 128.50 1153.0 0.22260 0.5209 \n", | |
| "255 108.10 826.0 0.15120 0.3262 \n", | |
| "450 83.51 507.2 0.09457 0.3399 \n", | |
| "215 104.40 750.1 0.14600 0.4370 \n", | |
| "\n", | |
| " worst concavity worst concave points worst symmetry \\\n", | |
| "211 0.1471 0.06913 0.2535 \n", | |
| "203 0.4646 0.20130 0.4432 \n", | |
| "255 0.3209 0.13740 0.3068 \n", | |
| "450 0.3218 0.08750 0.2305 \n", | |
| "215 0.4636 0.16540 0.3630 \n", | |
| "\n", | |
| " worst fractal dimension \n", | |
| "211 0.07993 \n", | |
| "203 0.10860 \n", | |
| "255 0.07957 \n", | |
| "450 0.09952 \n", | |
| "215 0.10590 \n", | |
| "\n", | |
| "[5 rows x 30 columns]" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-8f3672f7-8db8-405b-976c-2fb68b03e640\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <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>mean radius</th>\n", | |
| " <th>mean texture</th>\n", | |
| " <th>mean perimeter</th>\n", | |
| " <th>mean area</th>\n", | |
| " <th>mean smoothness</th>\n", | |
| " <th>mean compactness</th>\n", | |
| " <th>mean concavity</th>\n", | |
| " <th>mean concave points</th>\n", | |
| " <th>mean symmetry</th>\n", | |
| " <th>mean fractal dimension</th>\n", | |
| " <th>...</th>\n", | |
| " <th>worst radius</th>\n", | |
| " <th>worst texture</th>\n", | |
| " <th>worst perimeter</th>\n", | |
| " <th>worst area</th>\n", | |
| " <th>worst smoothness</th>\n", | |
| " <th>worst compactness</th>\n", | |
| " <th>worst concavity</th>\n", | |
| " <th>worst concave points</th>\n", | |
| " <th>worst symmetry</th>\n", | |
| " <th>worst fractal dimension</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>211</th>\n", | |
| " <td>11.84</td>\n", | |
| " <td>18.94</td>\n", | |
| " <td>75.51</td>\n", | |
| " <td>428.0</td>\n", | |
| " <td>0.08871</td>\n", | |
| " <td>0.0690</td>\n", | |
| " <td>0.02669</td>\n", | |
| " <td>0.01393</td>\n", | |
| " <td>0.1533</td>\n", | |
| " <td>0.06057</td>\n", | |
| " <td>...</td>\n", | |
| " <td>13.30</td>\n", | |
| " <td>24.99</td>\n", | |
| " <td>85.22</td>\n", | |
| " <td>546.3</td>\n", | |
| " <td>0.12800</td>\n", | |
| " <td>0.1880</td>\n", | |
| " <td>0.1471</td>\n", | |
| " <td>0.06913</td>\n", | |
| " <td>0.2535</td>\n", | |
| " <td>0.07993</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>203</th>\n", | |
| " <td>13.81</td>\n", | |
| " <td>23.75</td>\n", | |
| " <td>91.56</td>\n", | |
| " <td>597.8</td>\n", | |
| " <td>0.13230</td>\n", | |
| " <td>0.1768</td>\n", | |
| " <td>0.15580</td>\n", | |
| " <td>0.09176</td>\n", | |
| " <td>0.2251</td>\n", | |
| " <td>0.07421</td>\n", | |
| " <td>...</td>\n", | |
| " <td>19.20</td>\n", | |
| " <td>41.85</td>\n", | |
| " <td>128.50</td>\n", | |
| " <td>1153.0</td>\n", | |
| " <td>0.22260</td>\n", | |
| " <td>0.5209</td>\n", | |
| " <td>0.4646</td>\n", | |
| " <td>0.20130</td>\n", | |
| " <td>0.4432</td>\n", | |
| " <td>0.10860</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>255</th>\n", | |
| " <td>13.96</td>\n", | |
| " <td>17.05</td>\n", | |
| " <td>91.43</td>\n", | |
| " <td>602.4</td>\n", | |
| " <td>0.10960</td>\n", | |
| " <td>0.1279</td>\n", | |
| " <td>0.09789</td>\n", | |
| " <td>0.05246</td>\n", | |
| " <td>0.1908</td>\n", | |
| " <td>0.06130</td>\n", | |
| " <td>...</td>\n", | |
| " <td>16.39</td>\n", | |
| " <td>22.07</td>\n", | |
| " <td>108.10</td>\n", | |
| " <td>826.0</td>\n", | |
| " <td>0.15120</td>\n", | |
| " <td>0.3262</td>\n", | |
| " <td>0.3209</td>\n", | |
| " <td>0.13740</td>\n", | |
| " <td>0.3068</td>\n", | |
| " <td>0.07957</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>450</th>\n", | |
| " <td>11.87</td>\n", | |
| " <td>21.54</td>\n", | |
| " <td>76.83</td>\n", | |
| " <td>432.0</td>\n", | |
| " <td>0.06613</td>\n", | |
| " <td>0.1064</td>\n", | |
| " <td>0.08777</td>\n", | |
| " <td>0.02386</td>\n", | |
| " <td>0.1349</td>\n", | |
| " <td>0.06612</td>\n", | |
| " <td>...</td>\n", | |
| " <td>12.79</td>\n", | |
| " <td>28.18</td>\n", | |
| " <td>83.51</td>\n", | |
| " <td>507.2</td>\n", | |
| " <td>0.09457</td>\n", | |
| " <td>0.3399</td>\n", | |
| " <td>0.3218</td>\n", | |
| " <td>0.08750</td>\n", | |
| " <td>0.2305</td>\n", | |
| " <td>0.09952</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>215</th>\n", | |
| " <td>13.86</td>\n", | |
| " <td>16.93</td>\n", | |
| " <td>90.96</td>\n", | |
| " <td>578.9</td>\n", | |
| " <td>0.10260</td>\n", | |
| " <td>0.1517</td>\n", | |
| " <td>0.09901</td>\n", | |
| " <td>0.05602</td>\n", | |
| " <td>0.2106</td>\n", | |
| " <td>0.06916</td>\n", | |
| " <td>...</td>\n", | |
| " <td>15.75</td>\n", | |
| " <td>26.93</td>\n", | |
| " <td>104.40</td>\n", | |
| " <td>750.1</td>\n", | |
| " <td>0.14600</td>\n", | |
| " <td>0.4370</td>\n", | |
| " <td>0.4636</td>\n", | |
| " <td>0.16540</td>\n", | |
| " <td>0.3630</td>\n", | |
| " <td>0.10590</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 30 columns</p>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f3672f7-8db8-405b-976c-2fb68b03e640')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| " \n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", | |
| " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-8f3672f7-8db8-405b-976c-2fb68b03e640 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-8f3672f7-8db8-405b-976c-2fb68b03e640');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 3 | |
| } | |
| ], | |
| "source": [ | |
| "X_train.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "id": "fCOK-1gyiYkE", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "f3eb1503-b779-4041-c2ce-05b1014a6322" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "211 1\n", | |
| "203 0\n", | |
| "255 0\n", | |
| "450 1\n", | |
| "215 0\n", | |
| "dtype: int64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 4 | |
| } | |
| ], | |
| "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": 5, | |
| "metadata": { | |
| "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": 6, | |
| "metadata": { | |
| "id": "lpZLJG3hiYkI", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 996 | |
| }, | |
| "outputId": "aaf4f1bf-41e9-414d-ca1d-ab8041055593" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 558.875x500 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 558.875x500 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "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": 7, | |
| "metadata": { | |
| "id": "sFenUqQPiYkK", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 995 | |
| }, | |
| "outputId": "db8c7e88-66b8-47bb-e75d-7ad9a9ede591" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 558.875x500 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 558.875x500 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "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": 8, | |
| "metadata": { | |
| "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": 9, | |
| "metadata": { | |
| "id": "_vEWAT2OiYkP", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 503 | |
| }, | |
| "outputId": "d2b57562-a3dd-494e-ba53-0342092aa2fd" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.9/dist-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" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "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": 10, | |
| "metadata": { | |
| "id": "QiNgUZKxiYkR", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 509 | |
| }, | |
| "outputId": "f30eb581-be07-4d96-828b-568c64514212" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.9/dist-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" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 640x480 with 2 Axes>" | |
| ], | |
| "image/png": 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+/PhgFNBEHhkZqdTUVOXm5mr48OGSjq3wy83N1b333nvC/lar1aMb79F41Ucjtbs8zq3vx6OtVF4bpd3lceoUU6lrU/boXyVJOlwTpfg21bqrz8eqORquvP2dAxQ14DuLxdDVvy3Te6sdcjYE5y9s+KYxbzD75fHBKOBD65MnT1ZmZqYuvPBCXXzxxVq4cKGqq6s1evToQIeGk6hrCNeF8QeVec6nskXW6vuaaG0vTdAt//itDtdEBzo8oNH6pf2gjom12vAWi9wQWgKeyG+66SZ9++23mjlzpkpLS9WvXz+tW7fuhAVwCJw/rBvm+vehn9rorg3X/creQGj6eEucru19RaDDQBMKxKr15hDwRC5J995770mH0gEA8JeWOrQenH9eAAAAjwRFRQ4AQFPz5Xnpx48PRiRyAIApMLQOAACCDhU5AMAUWmpFTiIHAJhCS03kDK0DABDCqMgBAKbQUityEjkAwBQM+XYL2cnfKh54JHIAgCm01IqcOXIAAEIYFTkAwBRaakVOIgcAmEJLTeQMrQMAEMKoyAEAptBSK3ISOQDAFAzDIsOHZOzLsU2JoXUAAEIYFTkAwBR4HzkAACGspc6RM7QOAEAIoyIHAJhCS13sRiIHAJhCSx1aJ5EDAEyhpVbkzJEDABDCSOQAAFMwfh5ab2xrTEV+4MAB3XrrrWrfvr2io6PVp08fbd++/b9iMjRz5kwlJCQoOjpa6enp2r17t1fXIJEDAEzBkGQYPjQvr/fDDz/osssuU0REhN599119/vnn+utf/6p27dq59pk/f74WLVqkJUuWaNu2bWrTpo0yMjJUU1Pj8XWYIwcAoAk89thjSkpKUk5OjqsvJSXF9W/DMLRw4UI9+OCDGjZsmCRp+fLlcjgcWr16tW6++WaPrkNFDgAwheNPdvOleePtt9/WhRdeqN/97nfq2LGjzj//fD333HOu7UVFRSotLVV6erqrz263q3///srPz/f4OiRyAIApHF+17kuTpMrKSrdWW1t70ut99dVXWrx4sbp3767169fr7rvv1n333acXX3xRklRaWipJcjgcbsc5HA7XNk+QyAEA8EJSUpLsdrurZWVlnXQ/p9OpCy64QI888ojOP/983XXXXbrzzju1ZMkSv8bDHDkAwBSchkUWPzwQpri4WDabzdVvtVpPun9CQoJ69+7t1terVy+9+eabkqT4+HhJUllZmRISElz7lJWVqV+/fh7HRUUOADAFn1as/9wkyWazubVTJfLLLrtMhYWFbn1ffvmlOnfuLOnYwrf4+Hjl5ua6tldWVmrbtm1KS0vz+HtRkQMA0AQmTZqkSy+9VI888ohuvPFGffjhh/rb3/6mv/3tb5Iki8WiiRMn6uGHH1b37t2VkpKiGTNmKDExUcOHD/f4OiRyAIApNPcjWi+66CKtWrVK06dP15w5c5SSkqKFCxdq1KhRrn2mTp2q6upq3XXXXSovL9eAAQO0bt06RUVFeXwdEjkAwBQC8az166+/Xtdff/0pt1ssFs2ZM0dz5sxpdFwkcgCAKfhrsVuwYbEbAAAhjIocAGAK/73yvLHHByMSOQDAFI4lcl/myP0YjB8xtA4AQAijIgcAmEIgVq03BxI5AMAUDHn/TvFfHh+MGFoHACCEUZEDAEyBoXUAAEJZCx1bJ5EDAMzBx4pcQVqRM0cOAEAIoyIHAJgCT3YDACCEtdTFbgytAwAQwqjIAQDmYFh8W7AWpBU5iRwAYAotdY6coXUAAEIYFTkAwBzM/ECYt99+2+MT3nDDDY0OBgCAptJSV617lMiHDx/u0cksFosaGhp8iQcAAHjBo0TudDqbOg4AAJpekA6P+8KnOfKamhpFRUX5KxYAAJpMSx1a93rVekNDg+bOnaszzzxTMTEx+uqrryRJM2bM0NKlS/0eIAAAfmH4oQUhrxP5vHnztGzZMs2fP1+RkZGu/nPPPVfPP/+8X4MDAAC/zutEvnz5cv3tb3/TqFGjFB4e7urv27evvvjiC78GBwCA/1j80IKP13PkBw4cULdu3U7odzqdqq+v90tQAAD4XQu9j9zrirx379764IMPTuh/4403dP755/slKAAA4BmvK/KZM2cqMzNTBw4ckNPp1FtvvaXCwkItX75ca9eubYoYAQDwHRX5McOGDdOaNWv03nvvqU2bNpo5c6Z27dqlNWvW6Oqrr26KGAEA8N3xt5/50oJQo+4jv/zyy7VhwwZ/xwIAALzU6AfCbN++Xbt27ZJ0bN48NTXVb0EBAOBvvMb0Z/v379fll1+uiy++WBMmTNCECRN00UUXacCAAdq/f39TxAgAgO+a+YEws2fPlsVicWs9e/Z0ba+pqdG4cePUvn17xcTEaOTIkSorK/P6a3mdyO+44w7V19dr165dOnz4sA4fPqxdu3bJ6XTqjjvu8DoAAABaqnPOOUcHDx50tc2bN7u2TZo0SWvWrNHrr7+uvLw8lZSUaMSIEV5fw+uh9by8PG3ZskU9evRw9fXo0UNPPfWULr/8cq8DAACgWfi6YK0Rx7Zq1Urx8fEn9FdUVGjp0qVasWKFrrrqKklSTk6OevXqpa1bt+qSSy7x+BpeV+RJSUknffBLQ0ODEhMTvT0dAADNwmL43iSpsrLSrdXW1p7ymrt371ZiYqK6du2qUaNGad++fZKkgoIC1dfXKz093bVvz549lZycrPz8fK++l9eJ/PHHH9f48eO1fft2V9/27ds1YcIE/eUvf/H2dAAANA8/zZEnJSXJbre7WlZW1kkv179/fy1btkzr1q3T4sWLVVRUpMsvv1xHjhxRaWmpIiMj1bZtW7djHA6HSktLvfpaHg2tt2vXThbLf4YUqqur1b9/f7Vqdezwo0ePqlWrVhozZoyGDx/uVQAAAISS4uJi2Ww212er1XrS/YYMGeL693nnnaf+/furc+fOWrlypaKjo/0Wj0eJfOHChX67IAAAAeGnOXKbzeaWyD3Vtm1bnX322dqzZ4+uvvpq1dXVqby83K0qLysrO+mc+q/xKJFnZmZ6dVIAAIJOgB/RWlVVpb179+q2225TamqqIiIilJubq5EjR0qSCgsLtW/fPqWlpXl13kY/EEY6dg9cXV2dW19j/koBAKClmTJlioYOHarOnTurpKREs2bNUnh4uG655RbZ7XaNHTtWkydPVlxcnGw2m8aPH6+0tDSvVqxLjUjk1dXVeuCBB7Ry5Up9//33J2xvaGjw9pQAADS9Zq7I9+/fr1tuuUXff/+9OnTooAEDBmjr1q3q0KGDJGnBggUKCwvTyJEjVVtbq4yMDD3zzDNeh+V1Ip86daref/99LV68WLfddpuys7N14MABPfvss3r00Ue9DgAAgGbRzIn81Vdf/dXtUVFRys7OVnZ2tg9BNSKRr1mzRsuXL9fAgQM1evRoXX755erWrZs6d+6sl19+WaNGjfIpIAAA4Dmv7yM/fPiwunbtKunYfPjhw4clSQMGDNCmTZv8Gx0AAP7SQl9j6nUi79q1q4qKiiQdewrNypUrJR2r1H95YzsAAMHCX092CzZeJ/LRo0frk08+kSRNmzZN2dnZioqK0qRJk3T//ff7PUAAAHBqXs+RT5o0yfXv9PR0ffHFFyooKFC3bt103nnn+TU4AAD8JsD3kTcVn+4jl6TOnTurc+fO/ogFAAB4yaNEvmjRIo9PeN999zU6GAAAmopFvs1zB+dSNw8T+YIFCzw6mcViIZEDANCMPErkx1epB6vkOR+qlSUi0GEATeKdkp2BDgFoMpVHnGp3djNdzE8vTQk2Ps+RAwAQElroYjevbz8DAADBg4ocAGAOLbQiJ5EDAEzB16eztZgnuwEAgODRqET+wQcf6NZbb1VaWpoOHDggSXrppZe0efNmvwYHAIDfGH5oQcjrRP7mm28qIyND0dHR+vjjj1VbWytJqqio0COPPOL3AAEA8AsS+TEPP/ywlixZoueee04REf+5d/uyyy7Tjh07/BocAAD4dV4vdissLNQVV1xxQr/dbld5ebk/YgIAwO9Y7Paz+Ph47dmz54T+zZs3q2vXrn4JCgAAvzv+ZDdfWhDyOpHfeeedmjBhgrZt2yaLxaKSkhK9/PLLmjJliu6+++6miBEAAN+10Dlyr4fWp02bJqfTqd/85jf68ccfdcUVV8hqtWrKlCkaP358U8QIAABOwetEbrFY9Oc//1n333+/9uzZo6qqKvXu3VsxMTFNER8AAH7RUufIG/1kt8jISPXu3dufsQAA0HR4ROsxgwYNksVy6gn/f/7znz4FBAAAPOd1Iu/Xr5/b5/r6eu3cuVP//ve/lZmZ6a+4AADwLx+H1ltMRb5gwYKT9s+ePVtVVVU+BwQAQJNooUPrfntpyq233qoXXnjBX6cDAAAe8NtrTPPz8xUVFeWv0wEA4F8ttCL3OpGPGDHC7bNhGDp48KC2b9+uGTNm+C0wAAD8idvPfma3290+h4WFqUePHpozZ44GDx7st8AAAMDpeZXIGxoaNHr0aPXp00ft2rVrqpgAAICHvFrsFh4ersGDB/OWMwBA6Angs9YfffRRWSwWTZw40dVXU1OjcePGqX379oqJidHIkSNVVlbm9bm9XrV+7rnn6quvvvL6QgAABNLxOXJfWmN89NFHevbZZ3Xeeee59U+aNElr1qzR66+/rry8PJWUlJywDs0TXifyhx9+WFOmTNHatWt18OBBVVZWujUAAHBMVVWVRo0apeeee85tSrqiokJLly7VE088oauuukqpqanKycnRli1btHXrVq+u4XEinzNnjqqrq3Xttdfqk08+0Q033KBOnTqpXbt2ateundq2bcu8OQAguDXzsPq4ceN03XXXKT093a2/oKBA9fX1bv09e/ZUcnKy8vPzvbqGx4vdHnroIf3xj3/U+++/79UFAAAICn66j/yXo89Wq1VWq/WE3V999VXt2LFDH3300QnbSktLFRkZqbZt27r1OxwOlZaWehWWx4ncMI59gyuvvNKrCwAA0JIkJSW5fZ41a5Zmz57t1ldcXKwJEyZow4YNTf6wNK9uP/u1t54BABDM/PVAmOLiYtlsNlf/yarxgoICHTp0SBdccIGrr6GhQZs2bdLTTz+t9evXq66uTuXl5W5VeVlZmeLj472Ky6tEfvbZZ582mR8+fNirAAAAaBZ+Glq32WxuifxkfvOb3+jTTz916xs9erR69uypBx54QElJSYqIiFBubq5GjhwpSSosLNS+ffuUlpbmVVheJfKHHnrohCe7AQAAd7GxsTr33HPd+tq0aaP27du7+seOHavJkycrLi5ONptN48ePV1pami655BKvruVVIr/55pvVsWNHry4AAEAwCLZnrS9YsEBhYWEaOXKkamtrlZGRoWeeecbr83icyJkfBwCEtAC//Wzjxo1un6OiopSdna3s7GyfzuvxfeTHV60DAIDg4XFF7nQ6mzIOAACaFu8jBwAgdAXbHLm/kMgBAObQQityr1+aAgAAggcVOQDAHFpoRU4iBwCYQkudI2doHQCAEEZFDgAwB4bWAQAIXQytAwCAoENFDgAwB4bWAQAIYS00kTO0DgBACKMiBwCYguXn5svxwYhEDgAwhxY6tE4iBwCYArefAQCAoENFDgAwB4bWAQAIcUGajH3B0DoAACGMihwAYAotdbEbiRwAYA4tdI6coXUAAEIYFTkAwBQYWgcAIJQxtA4AAIINFTkAwBQYWgcAIJS10KF1EjkAwBxaaCJnjhwAgBBGRQ4AMIWWOkdORQ4AMAfDD80Lixcv1nnnnSebzSabzaa0tDS9++67ru01NTUaN26c2rdvr5iYGI0cOVJlZWVefy0SOQAATaBTp0569NFHVVBQoO3bt+uqq67SsGHD9Nlnn0mSJk2apDVr1uj1119XXl6eSkpKNGLECK+vw9A6AMAULIYhi9H48XFvjx06dKjb53nz5mnx4sXaunWrOnXqpKVLl2rFihW66qqrJEk5OTnq1auXtm7dqksuucTj61CRAwDMwU9D65WVlW6ttrb2tJduaGjQq6++qurqaqWlpamgoED19fVKT0937dOzZ08lJycrPz/fq69FIgcAwAtJSUmy2+2ulpWVdcp9P/30U8XExMhqteqPf/yjVq1apd69e6u0tFSRkZFq27at2/4Oh0OlpaVexcPQOgDAFPy1ar24uFg2m83Vb7VaT3lMjx49tHPnTlVUVOiNN95QZmam8vLyGh/ESZDIAQDm4KcHwhxfhe6JyMhIdevWTZKUmpqqjz76SE8++aRuuukm1dXVqby83K0qLysrU3x8vFdhMbQOAEAzcTqdqq2tVWpqqiIiIpSbm+vaVlhYqH379iktLc2rc1KRAwBMobkfCDN9+nQNGTJEycnJOnLkiFasWKGNGzdq/fr1stvtGjt2rCZPnqy4uDjZbDaNHz9eaWlpXq1Yl0jkAACzaOZnrR86dEh/+MMfdPDgQdntdp133nlav369rr76aknSggULFBYWppEjR6q2tlYZGRl65plnvA6LRA4AMIXmrsiXLl36q9ujoqKUnZ2t7Ozsxgcl5sgBAAhpVOQAAHNooa8xJZEDAEwjWN9g5guG1gEACGFU5AAAczCMY82X44MQiRwAYArNvWq9uTC0DgBACKMiBwCYA6vWAQAIXRbnsebL8cGIoXUAAEIYFTlO6/o/fKfr/vC9HEl1kqRvCqP08gKHtr/v2Wv8gGDzY1WYXpyfoC3v2lX+fSuddc5PunvufvXo95OkY4uTlz8er3Ur2quqMly9L6zWfY8W68yudQGOHD5poUPrAa3IN23apKFDhyoxMVEWi0WrV68OZDg4hW8PRuiFRxJ07zVna/yQs/XJv2I0O+drdT67JtChAY2y4E9J2rEpRlOf+kZLcr9Q6pVHNO2mbvruYIQkaWV2R/39hQ4a/2ixnlz7paJaO/V/f3+W6mosAY4cvji+at2XFowCmsirq6vVt29fnx8Yj6a1bYNdH/3TppIiqw58ZdWyxxJUUx2mnqnVgQ4N8FrtTxZtfqet7njwoPpcUq0zU+p025RSJXap1drl7WUY0urnO+iWCaW69JpKde1do6mLvtH3ZRHass4e6PDhi+P3kfvSglBAh9aHDBmiIUOGBDIEeCkszNDlQ8tlbe3Uru1tAh0O4LWGBoucDRZFWt1XLlmjnPrswxiV7ovU4UMRuuDyKte2Njanep7/o3YVtNHA4eXNHDHw60Jqjry2tla1tbWuz5WVlQGMxly69PxJC9fsUaTVqZ+qwzRnbBft2x0V6LAAr7WOcapXarVWLIxXcvev1bbDUW1c3U67CtoosUutDh869muxbYd6t+Padqh3bUNo4oEwQSArK0t2u93VkpKSAh2Saezfa9U9V5+t+67rrrXLz9CUJ/cpuTtz5AhNU5/6RoYh/f6Cc3V9l75avfQMDRz+gywh9RsRXjP80IJQSP3YTp8+XRUVFa5WXFwc6JBM42h9mEq+tmrPp62Vk5Wgos+jNfyObwMdFtAoiV3q9Je39ujve/4//e/2z/TUO7t1tN6ihM61iut4VJJU/m2E2zHl30a4tgHBJKQSudVqlc1mc2sIDItFiogM0j9PAQ9FtXaqveOojpSHqyDPprSMSsUn1ymuY70+3hzj2q/6SJi++Li1erHAM6S11FXrTPjgtEZPP6iP/hmrbw9EKjqmQYN+W67zLq3Sn3/fNdChAY2yfWOsDENKOqtWB4oi9fzcM5XUrUaDb/peFos0/I5v9cqTDp2ZUqv45Dq9OD9B7R31uvSaikCHDl/w9jP/q6qq0p49e1yfi4qKtHPnTsXFxSk5OTmAkeG/tT3jqO5ftE9xHY/qxyPhKtoVpT//vqt2bIoNdGhAo1RXhisnK0HfHYxQbNsGXXZtuUZPO6hWP4+m3zjukGp+DNOTU5NUVRmucy6q1ryXv1JkVHD+Ioe5WQwjcH9ibNy4UYMGDTqhPzMzU8uWLTvt8ZWVlbLb7RqoYWpliTjt/kAoWl+yM9AhAE2m8ohT7c7+ShUVFU02XXo8V6QNmaNWEY2/2+ZofY3y353ZpLE2RkAr8oEDByqAf0cAAMyER7QCAIBgw2I3AIAptNQHwpDIAQDm4DSONV+OD0IkcgCAOTBHDgAAgg0VOQDAFCzycY7cb5H4F4kcAGAOLfTJbgytAwAQwqjIAQCm0FJvP6MiBwCYQzO/jzwrK0sXXXSRYmNj1bFjRw0fPlyFhYVu+9TU1GjcuHFq3769YmJiNHLkSJWVlXl1HRI5AABNIC8vT+PGjdPWrVu1YcMG1dfXa/Dgwaqu/s/rcCdNmqQ1a9bo9ddfV15enkpKSjRixAivrsPQOgDAFCyGIYsPC9a8PXbdunVun5ctW6aOHTuqoKBAV1xxhSoqKrR06VKtWLFCV111lSQpJydHvXr10tatW3XJJZd4dB0qcgCAOTj90HxQUXHsffZxcXGSpIKCAtXX1ys9Pd21T8+ePZWcnKz8/HyPz0tFDgCAFyorK90+W61WWa3WXz3G6XRq4sSJuuyyy3TuuedKkkpLSxUZGam2bdu67etwOFRaWupxPFTkAABTOD607kuTpKSkJNntdlfLyso67bXHjRunf//733r11Vf9/r2oyAEA5uCnZ60XFxfLZrO5uk9Xjd97771au3atNm3apE6dOrn64+PjVVdXp/LycreqvKysTPHx8R6HRUUOADCH409286VJstlsbu1UidwwDN17771atWqV/vnPfyolJcVte2pqqiIiIpSbm+vqKyws1L59+5SWlubx16IiBwCgCYwbN04rVqzQ3//+d8XGxrrmve12u6Kjo2W32zV27FhNnjxZcXFxstlsGj9+vNLS0jxesS6RyAEAJtHcT3ZbvHixJGngwIFu/Tk5Obr99tslSQsWLFBYWJhGjhyp2tpaZWRk6JlnnvHqOiRyAIA5NPNLUwwP9o+KilJ2drays7MbGxVz5AAAhDIqcgCAKVicx5ovxwcjEjkAwBx4HzkAAAg2VOQAAHPw0wNhgg2JHABgCs399rPmwtA6AAAhjIocAGAOLXSxG4kcAGAOhnx7p3hw5nESOQDAHJgjBwAAQYeKHABgDoZ8nCP3WyR+RSIHAJhDC13sxtA6AAAhjIocAGAOTkkWH48PQiRyAIApsGodAAAEHSpyAIA5tNDFbiRyAIA5tNBEztA6AAAhjIocAGAOLbQiJ5EDAMyB288AAAhd3H4GAACCDhU5AMAcmCMHACCEOQ3J4kMydgZnImdoHQCAEEZFDgAwB4bWAQAIZT4mcgVnImdoHQCAEEZFDgAwB4bWAQAIYU5DPg2Ps2odAADz2LRpk4YOHarExERZLBatXr3abbthGJo5c6YSEhIUHR2t9PR07d692+vrkMgBAOZgOH1vXqiurlbfvn2VnZ190u3z58/XokWLtGTJEm3btk1t2rRRRkaGampqvLoOQ+sAAHNo5jnyIUOGaMiQIac4laGFCxfqwQcf1LBhwyRJy5cvl8Ph0OrVq3XzzTd7fB0qcgCAOTgN35ukyspKt1ZbW+t1KEVFRSotLVV6erqrz263q3///srPz/fqXCRyAAC8kJSUJLvd7mpZWVlen6O0tFSS5HA43PodDodrm6cYWgcAmIOfhtaLi4tls9lc3Var1dfIfEJFDgAwB0P/SeaNasdOY7PZ3FpjEnl8fLwkqayszK2/rKzMtc1TJHIAAJpZSkqK4uPjlZub6+qrrKzUtm3blJaW5tW5GFoHAJhDM69ar6qq0p49e1yfi4qKtHPnTsXFxSk5OVkTJ07Uww8/rO7duyslJUUzZsxQYmKihg8f7tV1SOQAAHNwOiV5dy/4icd7bvv27Ro0aJDr8+TJkyVJmZmZWrZsmaZOnarq6mrdddddKi8v14ABA7Ru3TpFRUV5dR0SOQAATWDgwIEyfqWKt1gsmjNnjubMmePTdUjkAABz4KUpAACEsBaayFm1DgBACKMiBwCYQwt9jSmJHABgCobhlOHlG8x+eXwwIpEDAMzBMHyrqpkjBwAA/kZFDgAwB8PHOfIgrchJ5AAAc3A6JYsP89xBOkfO0DoAACGMihwAYA4MrQMAELoMp1OGD0PrwXr7GUPrAACEMCpyAIA5MLQOAEAIcxqSpeUlcobWAQAIYVTkAABzMAxJvtxHHpwVOYkcAGAKhtOQ4cPQukEiBwAggAynfKvIuf0MAAD4GRU5AMAUGFoHACCUtdCh9ZBO5Mf/Ojqqep/u8QeCWeWR4PzlAfhDZdWxn+/mqHZ9zRVHVe+/YPwopBP5kSNHJEmb9U6AIwGaTruzAx0B0PSOHDkiu93eJOeOjIxUfHy8Npf6nivi4+MVGRnph6j8x2IE66C/B5xOp0pKShQbGyuLxRLocEyhsrJSSUlJKi4uls1mC3Q4gF/x8938DMPQkSNHlJiYqLCwplt/XVNTo7q6Op/PExkZqaioKD9E5D8hXZGHhYWpU6dOgQ7DlGw2G7/o0GLx8928mqoS/29RUVFBl4D9hdvPAAAIYSRyAABCGIkcXrFarZo1a5asVmugQwH8jp9vhKKQXuwGAIDZUZEDABDCSOQAAIQwEjkAACGMRA4AQAgjkcNj2dnZ6tKli6KiotS/f399+OGHgQ4J8ItNmzZp6NChSkxMlMVi0erVqwMdEuAxEjk88tprr2ny5MmaNWuWduzYob59+yojI0OHDh0KdGiAz6qrq9W3b19lZ2cHOhTAa9x+Bo/0799fF110kZ5++mlJx55zn5SUpPHjx2vatGkBjg7wH4vFolWrVmn48OGBDgXwCBU5Tquurk4FBQVKT0939YWFhSk9PV35+fkBjAwAQCLHaX333XdqaGiQw+Fw63c4HCotLQ1QVAAAiUQOAEBII5HjtM444wyFh4errKzMrb+srEzx8fEBigoAIJHI4YHIyEilpqYqNzfX1ed0OpWbm6u0tLQARgYAaBXoABAaJk+erMzMTF144YW6+OKLtXDhQlVXV2v06NGBDg3wWVVVlfbs2eP6XFRUpJ07dyouLk7JyckBjAw4PW4/g8eefvppPf744yotLVW/fv20aNEi9e/fP9BhAT7buHGjBg0adEJ/Zmamli1b1vwBAV4gkQMAEMKYIwcAIISRyAEACGEkcgAAQhiJHACAEEYiBwAghJHIAQAIYSRyAABCGIkc8NHtt9/u9u7qgQMHauLEic0ex8aNG2WxWFReXn7KfSwWi1avXu3xOWfPnq1+/fr5FNfXX38ti8WinTt3+nQeACdHIkeLdPvtt8tischisSgyMlLdunXTnDlzdPTo0Sa/9ltvvaW5c+d6tK8nyRcAfg3PWkeLdc011ygnJ0e1tbV65513NG7cOEVERGj69Okn7FtXV6fIyEi/XDcuLs4v5wEAT1CRo8WyWq2Kj49X586ddffddys9PV1vv/22pP8Mh8+bN0+JiYnq0aOHJKm4uFg33nij2rZtq7i4OA0bNkxff/2165wNDQ2aPHmy2rZtq/bt22vq1Kn65VOOfzm0XltbqwceeEBJSUmyWq3q1q2bli5dqq+//tr1fO927drJYrHo9ttvl3Ts7XJZWVlKSUlRdHS0+vbtqzfeeMPtOu+8847OPvtsRUdHa9CgQW5xeuqBBx7Q2WefrdatW6tr166aMWOG6uvrT9jv2WefVVJSklq3bq0bb7xRFRUVbtuff/559erVS1FRUerZs6eeeeYZr2MB0DgkcphGdHS06urqXJ9zc3NVWFioDRs2aO3ataqvr1dGRoZiY2P1wQcf6F//+pdiYmJ0zTXXuI7761//qmXLlumFF17Q5s2bdfjwYa1atepXr/uHP/xBr7zyihYtWqRdu3bp2WefVUxMjJKSkvTmm29KkgoLC3Xw4EE9+eSTkqSsrCwtX75cS5Ys0WeffaZJkybp1ltvVV5enqRjf3CMGDFCQ4cO1c6dO3XHHXdo2rRpXv83iY2N1bJly/T555/rySef1HPPPacFCxa47bNnzx6tXLlSa9as0bp16/Txxx/rnnvucW1/+eWXNXPmTM2bN0+7du3SI488ohkzZujFF1/0Oh4AjWAALVBmZqYxbNgwwzAMw+l0Ghs2bDCsVqsxZcoU13aHw2HU1ta6jnnppZeMHj16GE6n09VXW1trREdHG+vXrzcMwzASEhKM+fPnu7bX19cbnTp1cl3LMAzjyiuvNCZMmGAYhmEUFhYakowNGzacNM7333/fkGT88MMPrr6amhqjdevWxpYtW9z2HTt2rHHLLbcYhmEY06dPN3r37u22/YEHHjjhXL8kyVi1atUptz/++ONGamqq6/OsWbOM8PBwY//+/a6+d9991wgLCzMOHjxoGIZhnHXWWcaKFSvczjN37lwjLS3NMAzDKCoqMiQZH3/88SmvC6DxmCNHi7V27VrFxMSovr5eTqdTv//97zV79mzX9j59+rjNi3/yySfas2ePYmNj3c5TU1OjvXv3qqKiQgcPHnR7dWurVq104YUXnjC8ftzOnTsVHh6uK6+80uO49+zZox9//FFXX321W39dXZ3OP/98SdKuXbtOeIVsWlqax9c47rXXXtOiRYu0d+9eVVVV6ejRo7LZbG77JCcn68wzz3S7jtPpVGFhoWJjY7V3716NHTtWd955p2ufo0ePym63ex0PAO+RyNFiDRo0SIsXL1ZkZKQSExPVqpX7j3ubNm3cPldVVSk1NVUvv/zyCefq0KFDo2KIjo72+piqqipJ0j/+8Q+3BCodm/f3l/z8fI0aNUoPPfSQMjIyZLfb9eqrr+qvf/2r17E+99xzJ/xhER4e7rdYAZwaiRwtVps2bdStWzeP97/gggv02muvqWPHjidUpcclJCRo27ZtuuKKKyQdqzwLCgp0wQUXnHT/Pn36yOl0Ki8vT+np6SdsPz4i0NDQ4Orr3bu3rFar9u3bd8pKvlevXq6Fe8dt3br19F/yv2zZskWdO3fWn//8Z1ffN998c8J++/btU0lJiRITE13XCQsLU48ePeRwOJSYmKivvvpKo0aN8ur6APyDxW7Az0aNGqUzzjhDw4YN0wcffKCioiJt3LhR9913n/bv3y9JmjBhgh599FGtXr1aX3zxhe65555fvQe8S5cuyszM1JgxY7R69WrXOVeuXClJ6ty5sywWi9auXatvv/1WVVVVio2N1ZQpUzRp0iS9+OKL2rt3r3bs2KGnnnrKtYDsj3/8o3bv3q37779fhYWFWrFihZYtW+bV9+3evbv27dunV199VXv37tWiRYtOunAvKipKmZmZ+uSTT/TBBx/ovvvu04033qj4+HhJ0kMPPaSsrCwtWrRIX375pT799FPl5OToiSee8CoeAI1DIgd+1rp1a23atEnJyckaMWKEevXqpbFjx6qmpsZVof/pT3/SbbfdpszMTKWlpSk2Nla//e1vf/W8ixcv1v/8z//onnvuUc+ePXXnnXequrpaknTmmWfqoYce0rRp0+RwOHTvvfdKkubOnasZM2YoKytLvXr10jXXXKN//OMfSklJkXRs3vrNN9/U6tWr1bdvXy1ZskSPPPKIV9/3hhtu0KRJk3TvvfeqX79+2rJli2bMmHHCft26ddOIESN07bXXavDgwTrvvPPcbi+744479PzzzysnJ0d9+vTRlVdeqWXLlrliBdC0LMapVukAAICgR0UOAEAII5EDABDCSOQAAIQwEjkAACGMRA4AQAgjkQMAEMJI5AAAhDASOQAAIYxEDgBACCORAwAQwkjkAACEMBI5AAAh7P8Hyd60A8/IauQAAAAASUVORK5CYII=\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "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": 11, | |
| "metadata": { | |
| "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": 12, | |
| "metadata": { | |
| "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": 13, | |
| "metadata": { | |
| "id": "XUEG7n7ciYkY", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "outputId": "98ce99e9-c245-4fd6-ef59-6fcb710558d2" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "<title>\n", | |
| " Model Card for Breast Cancer Wisconsin (Diagnostic) Dataset\n", | |
| "</title>\n", | |
| "</head>\n", | |
| "<body>\n", | |
| " <h1>\n", | |
| " Model Card for Breast Cancer Wisconsin (Diagnostic) Dataset\n", | |
| " </h1>\n", | |
| " <div class=\"row\">\n", | |
| " \n", | |
| " <div class=\"col card\">\n", | |
| " <h2>Model Details</h2>\n", | |
| " <h3>Overview</h3>\n", | |
| " This model predicts whether breast cancer is benign or malignant based on image measurements.\n", | |
| " <h3>Version</h3>\n", | |
| " \n", | |
| " \n", | |
| " <div>name: 71d3ef7a-b983-4e4e-a19d-a45dfe70ed24</div>\n", | |
| "\n", | |
| " \n", | |
| " \n", | |
| " <div>date: 2023-04-02</div>\n", | |
| "\n", | |
| " \n", | |
| " \n", | |
| "\n", | |
| " \n", | |
| " \n", | |
| " <h3>Owners</h3>\n", | |
| " \n", | |
| " Model Cards Team, model-cards@google.com\n", | |
| " \n", | |
| " \n", | |
| " \n", | |
| " \n", | |
| " <h3>References</h3>\n", | |
| " <ul>\n", | |
| " \n", | |
| " <li><a href=\"https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)\">https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)</a></li>\n", | |
| " \n", | |
| " <li><a href=\"https://minds.wisconsin.edu/bitstream/handle/1793/59692/TR1131.pdf\">https://minds.wisconsin.edu/bitstream/handle/1793/59692/TR1131.pdf</a></li>\n", | |
| " \n", | |
| " </ul>\n", | |
| " \n", | |
| " </div>\n", | |
| " \n", | |
| " \n", | |
| " \n", | |
| " <div class=\"col card\">\n", | |
| " <h2>Considerations</h2>\n", | |
| " \n", | |
| " <h3>Intended Users</h3>\n", | |
| " \n", | |
| " \n", | |
| " <ul>\n", | |
| " \n", | |
| " <li>Medical professionals</li>\n", | |
| " \n", | |
| " <li>ML researchers</li>\n", | |
| " \n", | |
| " </ul>\n", | |
| "\n", | |
| " \n", | |
| " \n", | |
| " <h3>Use Cases</h3>\n", | |
| " \n", | |
| " \n", | |
| " <ul>\n", | |
| " \n", | |
| " <li>Breast cancer diagnosis</li>\n", | |
| " \n", | |
| " </ul>\n", | |
| "\n", | |
| " \n", | |
| " \n", | |
| " <h3>Limitations</h3>\n", | |
| " \n", | |
| " \n", | |
| " <ul>\n", | |
| " \n", | |
| " <li>Breast cancer diagnosis</li>\n", | |
| " \n", | |
| " </ul>\n", | |
| "\n", | |
| " \n", | |
| " \n", | |
| " \n", | |
| " <h3>Ethical Considerations</h3>\n", | |
| " <ul>\n", | |
| " <li>\n", | |
| " <div>Risk: Manual selection of image sections to digitize could create selection bias</div>\n", | |
| " <div>Mitigation Strategy: Automate the selection process</div>\n", | |
| " </li> </ul>\n", | |
| " </div>\n", | |
| " \n", | |
| " </div>\n", | |
| " \n", | |
| " \n", | |
| " <div class=\"col card\">\n", | |
| " <h2>Datasets</h2>\n", | |
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| " \n", | |
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| "/VtuucUaN26cz2Mu5fwAMENP+5IJwDfGjBnj/blHjx6Kjo7W6NGjvdvi4uIkSUePHpUkffzxx9q+\n", | |
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| "SktLzwi9053v/AAwFxfnAgbKzMzUl19+qbvvvlsffvihysvL9e6772rhwoVqa2tTv379FB0drd/+\n", | |
| "9rc6dOiQtm3bpuzsbL+sPWjQIBUUFKiqqkpfffWVJGnlypV65ZVXlJubq08++UQul0vr16/Xo48+\n", | |
| "Kkn6/PPPtXjxYj399NOaNGmS1qxZo6eeekq7d+8+53GnTZum4uJivfLKKzp48KAee+yxM0LmUs4P\n", | |
| "AHMRLoCBEhMT9cEHH6itrU3p6ekaPXq0li5dqr59+yooKEhBQUFav369SkpKNGrUKC1btkzPPvus\n", | |
| "X9b+xS9+oS1btmjgwIEaN26cJGnGjBl6++239ac//UkTJkzQjTfeqOeff15JSUmyLEsLFizQDTfc\n", | |
| "oKysLO/jFy9erHnz5qm+vv6cx12xYoWWL1+uCRMm6MSJE/r+97/f6fMDwFwO6/Q3kAEAAAIU/+oB\n", | |
| "AACMQbgAAABjEC4AAMAYhAsAADAG4QIAAIxBuAAAAGMQLgAAwBiECwAAMAbhAgAAjEG4AAAAYxAu\n", | |
| "AADAGIQLAAAwBuECAACMQbgAAABjEC4AAMAYhAsAADAG4QIAAIzx/wFvkr+5Gsi2YgAAAABJRU5E\n", | |
| "rkJggg==\n", | |
| "' alt='None' />\n", | |
| " </div>\n", | |
| " \n", | |
| " </div>\n", | |
| "\n", | |
| " \n", | |
| " </div>\n", | |
| " </div>\n", | |
| " \n", | |
| " </div>\n", | |
| "\n", | |
| " \n", | |
| " \n", | |
| " \n", | |
| "<div class=\"col card\">\n", | |
| " <h2>Quantitative Analysis</h2>\n", | |
| " \n", | |
| " \n", | |
| " \n", | |
| " <div class=\"row\">\n", | |
| " <div class=\"col\">\n", | |
| " <p>ROC curve and confusion matrix</p>\n", | |
| " \n", | |
| " <div class=\"img-container\">\n", | |
| " \n", | |
| " \n", | |
| " <div class=\"img-item\">\n", | |
| " <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n", | |
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| "' alt='None' />\n", | |
| " </div>\n", | |
| " \n", | |
| " </div>\n", | |
| "\n", | |
| " </div>\n", | |
| " </div>\n", | |
| "\n", | |
| " \n", | |
| "</div>\n", | |
| "\n", | |
| " \n", | |
| "</body>\n", | |
| "</html>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "# Return the model card document as an HTML page\n", | |
| "\n", | |
| "html = mct.export_format()\n", | |
| "\n", | |
| "display.display(display.HTML(html))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "r2lHReWTSeEN" | |
| }, | |
| "execution_count": 13, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "toc_visible": true, | |
| "include_colab_link": 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": "3.7.8" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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