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Google AutoML Metrics Example
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google-cloud-automl-metrics-vision.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Copy of AutoML Mertrics Vision.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyO9U/pj14SDsy5BCcc4Sfnp",
"include_colab_link": true
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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/leigh-johnson/ca8e7748b46dc2420369ec5de702d9be/copy-of-automl-mertrics-vision.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "Jhi-Z84xa7MA",
"outputId": "021cd279-9368-4960-e165-4168f6278a26"
},
"source": [
"!pip install --upgrade google-cloud-automl typing-extensions pillow imageio imageio-ffmpeg tqdm pyyaml"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting google-cloud-automl\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/84/76/c168c2c26744f611198a3a880fd066f89ac642178a2b840aafbf54cf4080/google_cloud_automl-2.2.0-py2.py3-none-any.whl (379kB)\n",
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"\u001b[?25hRequirement already up-to-date: typing-extensions in /usr/local/lib/python3.7/dist-packages (3.7.4.3)\n",
"Collecting pillow\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/1f/6d/b719ae8e21660a6a962636896dc4b7d657ef451a3ab941516401846ac5cb/Pillow-8.1.2-cp37-cp37m-manylinux1_x86_64.whl (2.2MB)\n",
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"\u001b[?25hCollecting imageio\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/6e/57/5d899fae74c1752f52869b613a8210a2480e1a69688e65df6cb26117d45d/imageio-2.9.0-py3-none-any.whl (3.3MB)\n",
"\u001b[K |████████████████████████████████| 3.3MB 19.4MB/s \n",
"\u001b[?25hCollecting imageio-ffmpeg\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/89/0f/4b49476d185a273163fa648eaf1e7d4190661d1bbf37ec2975b84df9de02/imageio_ffmpeg-0.4.3-py3-none-manylinux2010_x86_64.whl (26.9MB)\n",
"\u001b[K |████████████████████████████████| 26.9MB 138kB/s \n",
"\u001b[?25hCollecting tqdm\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/f8/3e/2730d0effc282960dbff3cf91599ad0d8f3faedc8e75720fdf224b31ab24/tqdm-4.59.0-py2.py3-none-any.whl (74kB)\n",
"\u001b[K |████████████████████████████████| 81kB 7.0MB/s \n",
"\u001b[?25hCollecting pyyaml\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/7a/a5/393c087efdc78091afa2af9f1378762f9821c9c1d7a22c5753fb5ac5f97a/PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636kB)\n",
"\u001b[K |████████████████████████████████| 645kB 35.3MB/s \n",
"\u001b[?25hCollecting proto-plus>=1.10.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/b4/8a/61c5a9b9b6288f9b060b6e3d88374fc083953a29aeac7206616c2d3c9c8e/proto_plus-1.18.1-py3-none-any.whl (42kB)\n",
"\u001b[K |████████████████████████████████| 51kB 5.6MB/s \n",
"\u001b[?25hRequirement already satisfied, skipping upgrade: google-api-core[grpc]<2.0.0dev,>=1.22.0 in /usr/local/lib/python3.7/dist-packages (from google-cloud-automl) (1.26.2)\n",
"Collecting libcst>=0.2.5\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/c3/7f/4aa1419b0ecb8a31a79fef7a79b49e6a07b977baa6c94612aeeda0228d17/libcst-0.3.18-py3-none-any.whl (512kB)\n",
"\u001b[K |████████████████████████████████| 522kB 39.0MB/s \n",
"\u001b[?25hRequirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.7/dist-packages (from imageio) (1.19.5)\n",
"Requirement already satisfied, skipping upgrade: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from proto-plus>=1.10.0->google-cloud-automl) (3.12.4)\n",
"Requirement already satisfied, skipping upgrade: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (20.9)\n",
"Requirement already satisfied, skipping upgrade: pytz in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (2018.9)\n",
"Requirement already satisfied, skipping upgrade: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (1.15.0)\n",
"Requirement already satisfied, skipping upgrade: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (2.23.0)\n",
"Requirement already satisfied, skipping upgrade: google-auth<2.0dev,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (1.28.0)\n",
"Requirement already satisfied, skipping upgrade: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (1.53.0)\n",
"Requirement already satisfied, skipping upgrade: setuptools>=40.3.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (54.2.0)\n",
"Requirement already satisfied, skipping upgrade: grpcio<2.0dev,>=1.29.0; extra == \"grpc\" in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (1.32.0)\n",
"Collecting typing-inspect>=0.4.0\n",
" Downloading https://files.pythonhosted.org/packages/42/1c/66402db44184904a2f14722d317a4da0b5c8c78acfc3faf74362566635c5/typing_inspect-0.6.0-py3-none-any.whl\n",
"Requirement already satisfied, skipping upgrade: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=14.3->google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (2.4.7)\n",
"Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (1.24.3)\n",
"Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (3.0.4)\n",
"Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (2.10)\n",
"Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (2020.12.5)\n",
"Requirement already satisfied, skipping upgrade: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (4.2.1)\n",
"Requirement already satisfied, skipping upgrade: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (4.7.2)\n",
"Requirement already satisfied, skipping upgrade: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (0.2.8)\n",
"Collecting mypy-extensions>=0.3.0\n",
" Downloading https://files.pythonhosted.org/packages/5c/eb/975c7c080f3223a5cdaff09612f3a5221e4ba534f7039db34c35d95fa6a5/mypy_extensions-0.4.3-py2.py3-none-any.whl\n",
"Requirement already satisfied, skipping upgrade: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2.0dev,>=1.21.1->google-api-core[grpc]<2.0.0dev,>=1.22.0->google-cloud-automl) (0.4.8)\n",
"\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"Installing collected packages: proto-plus, mypy-extensions, typing-inspect, pyyaml, libcst, google-cloud-automl, pillow, imageio, imageio-ffmpeg, tqdm\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
" Found existing installation: Pillow 7.1.2\n",
" Uninstalling Pillow-7.1.2:\n",
" Successfully uninstalled Pillow-7.1.2\n",
" Found existing installation: imageio 2.4.1\n",
" Uninstalling imageio-2.4.1:\n",
" Successfully uninstalled imageio-2.4.1\n",
" Found existing installation: tqdm 4.41.1\n",
" Uninstalling tqdm-4.41.1:\n",
" Successfully uninstalled tqdm-4.41.1\n",
"Successfully installed google-cloud-automl-2.2.0 imageio-2.9.0 imageio-ffmpeg-0.4.3 libcst-0.3.18 mypy-extensions-0.4.3 pillow-8.1.2 proto-plus-1.18.1 pyyaml-5.4.1 tqdm-4.59.0 typing-inspect-0.6.0\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"PIL",
"google"
]
}
}
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "y6Fr4v6ZvNGp",
"outputId": "5f10adcb-b189-4d75-fa29-0c2c6c48a437"
},
"source": [
"!pip install --upgrade pyyaml"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already up-to-date: pyyaml in /usr/local/lib/python3.7/dist-packages (5.4.1)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "Isyai242z8r8",
"outputId": "24b35a64-a7ab-4d8d-fd0b-af690abb5e36"
},
"source": [
"!pip install --force-reinstall plotly pandas"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting plotly\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/1f/f6/bd3c17c8003b6641df1228e80e1acac97ed8402635e46c2571f8e1ef63af/plotly-4.14.3-py2.py3-none-any.whl (13.2MB)\n",
"\u001b[K |████████████████████████████████| 13.2MB 327kB/s \n",
"\u001b[?25hCollecting pandas\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/f3/d4/3fe3b5bf9886912b64ef040040aec356fa48825e5a829a84c2667afdf952/pandas-1.2.3-cp37-cp37m-manylinux1_x86_64.whl (9.9MB)\n",
"\u001b[K |████████████████████████████████| 9.9MB 18.9MB/s \n",
"\u001b[?25hCollecting retrying>=1.3.3\n",
" Downloading https://files.pythonhosted.org/packages/44/ef/beae4b4ef80902f22e3af073397f079c96969c69b2c7d52a57ea9ae61c9d/retrying-1.3.3.tar.gz\n",
"Collecting six\n",
" Downloading https://files.pythonhosted.org/packages/ee/ff/48bde5c0f013094d729fe4b0316ba2a24774b3ff1c52d924a8a4cb04078a/six-1.15.0-py2.py3-none-any.whl\n",
"Collecting python-dateutil>=2.7.3\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/d4/70/d60450c3dd48ef87586924207ae8907090de0b306af2bce5d134d78615cb/python_dateutil-2.8.1-py2.py3-none-any.whl (227kB)\n",
"\u001b[K |████████████████████████████████| 235kB 39.9MB/s \n",
"\u001b[?25hCollecting numpy>=1.16.5\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/73/ef/8967d406f3f85018ceb5efab50431e901683188f1741ceb053efcab26c87/numpy-1.20.2-cp37-cp37m-manylinux2010_x86_64.whl (15.3MB)\n",
"\u001b[K |████████████████████████████████| 15.3MB 327kB/s \n",
"\u001b[?25hCollecting pytz>=2017.3\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/70/94/784178ca5dd892a98f113cdd923372024dc04b8d40abe77ca76b5fb90ca6/pytz-2021.1-py2.py3-none-any.whl (510kB)\n",
"\u001b[K |████████████████████████████████| 512kB 28.5MB/s \n",
"\u001b[?25hBuilding wheels for collected packages: retrying\n",
" Building wheel for retrying (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for retrying: filename=retrying-1.3.3-cp37-none-any.whl size=11430 sha256=de37f26277f73964a2ea637ca298e93e58b2c94ae0cf7f27eca5c1ed3f1b4299\n",
" Stored in directory: /root/.cache/pip/wheels/d7/a9/33/acc7b709e2a35caa7d4cae442f6fe6fbf2c43f80823d46460c\n",
"Successfully built retrying\n",
"\u001b[31mERROR: tensorflow 2.4.1 has requirement numpy~=1.19.2, but you'll have numpy 1.20.2 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=1.1.0; python_version >= \"3.0\", but you'll have pandas 1.2.3 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"Installing collected packages: six, retrying, plotly, python-dateutil, numpy, pytz, pandas\n",
" Found existing installation: six 1.15.0\n",
" Uninstalling six-1.15.0:\n",
" Successfully uninstalled six-1.15.0\n",
" Found existing installation: retrying 1.3.3\n",
" Uninstalling retrying-1.3.3:\n",
" Successfully uninstalled retrying-1.3.3\n",
" Found existing installation: plotly 4.4.1\n",
" Uninstalling plotly-4.4.1:\n",
" Successfully uninstalled plotly-4.4.1\n",
" Found existing installation: python-dateutil 2.8.1\n",
" Uninstalling python-dateutil-2.8.1:\n",
" Successfully uninstalled python-dateutil-2.8.1\n",
" Found existing installation: numpy 1.19.5\n",
" Uninstalling numpy-1.19.5:\n",
" Successfully uninstalled numpy-1.19.5\n",
" Found existing installation: pytz 2018.9\n",
" Uninstalling pytz-2018.9:\n",
" Successfully uninstalled pytz-2018.9\n",
" Found existing installation: pandas 1.1.5\n",
" Uninstalling pandas-1.1.5:\n",
" Successfully uninstalled pandas-1.1.5\n",
"Successfully installed numpy-1.20.2 pandas-1.2.3 plotly-4.14.3 python-dateutil-2.8.1 pytz-2021.1 retrying-1.3.3 six-1.15.0\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"_plotly_utils",
"dateutil",
"numpy",
"pandas",
"plotly",
"pytz",
"retrying",
"six"
]
}
}
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "s4j9tLzUdqGv",
"outputId": "f0310e3e-8c99-4f1a-d965-05d733dd0f1a"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XWIlFvpXdCrV",
"outputId": "90f451c8-6a8d-4551-e807-4136ee33632b"
},
"source": [
"import os\n",
"os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/path/to/google-application-credentials.json' \n",
"!echo $GOOGLE_APPLICATION_CREDENTIALS"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/drive/My Drive/gcp/get-grant-1b43edd58bea.json\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 225
},
"id": "H-nSJbRIayyt",
"outputId": "c31e58e6-a295-4131-a902-759bd7d7576b"
},
"source": [
"from google.cloud import automl\n",
"from google.protobuf.json_format import MessageToDict\n",
"import pandas as pd\n",
"pd.set_option('plotting.backend','plotly')\n",
"\n",
"project_id = \"your-project-id\"\n",
"model_id = \"your-automl-model-id\"\n",
"\n",
"# Initialize AutoMl API Client\n",
"client = automl.AutoMlClient()\n",
"# Get the full path of the model\n",
"model_full_id = client.model_path(project_id, \"us-central1\", model_id)\n",
"# Get all evaluation metrics for model\n",
"eval_metrics = client.list_model_evaluations(parent=model_full_id, filter=\"\")\n",
"# Deserialize from protobuf to dict\n",
"eval_metrics = [MessageToDict(e._pb) for e in eval_metrics ]\n",
"# Initialize DataFrame\n",
"df = pd.DataFrame(eval_metrics)\n",
"df"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>annotationSpecId</th>\n",
" <th>createTime</th>\n",
" <th>evaluatedExampleCount</th>\n",
" <th>imageObjectDetectionEvaluationMetrics</th>\n",
" <th>displayName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>172092261444616192</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>115</td>\n",
" <td>{'evaluatedBoundingBoxCount': 122, 'boundingBo...</td>\n",
" <td>nozzle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>4783778279872004096</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>237</td>\n",
" <td>{'evaluatedBoundingBoxCount': 250, 'boundingBo...</td>\n",
" <td>print</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>NaN</td>\n",
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" <td>{'evaluatedBoundingBoxCount': 889, 'boundingBo...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>2477935270658310144</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>133</td>\n",
" <td>{'evaluatedBoundingBoxCount': 268, 'boundingBo...</td>\n",
" <td>spaghetti</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>1325013766051463168</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>94</td>\n",
" <td>{'evaluatedBoundingBoxCount': 109, 'boundingBo...</td>\n",
" <td>adhesion</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>7089621289085698048</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>131</td>\n",
" <td>{'evaluatedBoundingBoxCount': 140, 'boundingBo...</td>\n",
" <td>raft</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name ... displayName\n",
"0 projects/702148157522/locations/us-central1/mo... ... nozzle\n",
"1 projects/702148157522/locations/us-central1/mo... ... print\n",
"2 projects/702148157522/locations/us-central1/mo... ... NaN\n",
"3 projects/702148157522/locations/us-central1/mo... ... spaghetti\n",
"4 projects/702148157522/locations/us-central1/mo... ... adhesion\n",
"5 projects/702148157522/locations/us-central1/mo... ... raft\n",
"\n",
"[6 rows x 6 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 225
},
"id": "oUsUdkMDxozP",
"outputId": "e2281d77-eaa1-4c8f-dfa4-0d90b6076b34"
},
"source": [
"df1 = pd.concat(\n",
" [df.drop(['imageObjectDetectionEvaluationMetrics'], axis=1),\n",
" df['imageObjectDetectionEvaluationMetrics'].apply(pd.Series)],\n",
" axis=1\n",
")\n",
"df1"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <th>name</th>\n",
" <th>annotationSpecId</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
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" <td>115</td>\n",
" <td>nozzle</td>\n",
" <td>122</td>\n",
" <td>[{'iouThreshold': 0.1, 'meanAveragePrecision':...</td>\n",
" <td>0.634350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>4783778279872004096</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>237</td>\n",
" <td>print</td>\n",
" <td>250</td>\n",
" <td>[{'iouThreshold': 0.1, 'meanAveragePrecision':...</td>\n",
" <td>0.790857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
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" <td>308</td>\n",
" <td>NaN</td>\n",
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" <td>[{'iouThreshold': 0.95, 'meanAveragePrecision'...</td>\n",
" <td>0.731560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>2477935270658310144</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>133</td>\n",
" <td>spaghetti</td>\n",
" <td>268</td>\n",
" <td>[{'iouThreshold': 0.1, 'meanAveragePrecision':...</td>\n",
" <td>0.741908</td>\n",
" </tr>\n",
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" <th>4</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>1325013766051463168</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>94</td>\n",
" <td>adhesion</td>\n",
" <td>109</td>\n",
" <td>[{'iouThreshold': 0.45, 'meanAveragePrecision'...</td>\n",
" <td>0.677704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>7089621289085698048</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>131</td>\n",
" <td>raft</td>\n",
" <td>140</td>\n",
" <td>[{'iouThreshold': 0.45, 'meanAveragePrecision'...</td>\n",
" <td>0.730516</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name ... boundingBoxMeanAveragePrecision\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.634350\n",
"1 projects/702148157522/locations/us-central1/mo... ... 0.790857\n",
"2 projects/702148157522/locations/us-central1/mo... ... 0.731560\n",
"3 projects/702148157522/locations/us-central1/mo... ... 0.741908\n",
"4 projects/702148157522/locations/us-central1/mo... ... 0.677704\n",
"5 projects/702148157522/locations/us-central1/mo... ... 0.730516\n",
"\n",
"[6 rows x 8 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 402
},
"id": "79FapEHbygrG",
"outputId": "19b6c09d-9638-4beb-a847-80064aa2d16a"
},
"source": [
"df2 = df1.explode('boundingBoxMetricsEntries')\n",
"df2"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <td>nozzle</td>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <td>{'iouThreshold': 0.35, 'meanAveragePrecision':...</td>\n",
" <td>0.634350</td>\n",
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" <td>7089621289085698048</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
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" <td>raft</td>\n",
" <td>140</td>\n",
" <td>{'iouThreshold': 0.25, 'meanAveragePrecision':...</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>7089621289085698048</td>\n",
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" <td>raft</td>\n",
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" <td>{'iouThreshold': 0.5, 'meanAveragePrecision': ...</td>\n",
" <td>0.730516</td>\n",
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" <th>5</th>\n",
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" <td>7089621289085698048</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>131</td>\n",
" <td>raft</td>\n",
" <td>140</td>\n",
" <td>{'iouThreshold': 0.05, 'meanAveragePrecision':...</td>\n",
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" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>7089621289085698048</td>\n",
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" <td>raft</td>\n",
" <td>140</td>\n",
" <td>{'iouThreshold': 0.9, 'meanAveragePrecision': ...</td>\n",
" <td>0.730516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>7089621289085698048</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>131</td>\n",
" <td>raft</td>\n",
" <td>140</td>\n",
" <td>{'iouThreshold': 0.85, 'meanAveragePrecision':...</td>\n",
" <td>0.730516</td>\n",
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"text/plain": [
" name ... boundingBoxMeanAveragePrecision\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.634350\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.634350\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.634350\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.634350\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.634350\n",
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"5 projects/702148157522/locations/us-central1/mo... ... 0.730516\n",
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"[102 rows x 8 columns]"
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},
"metadata": {
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},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BVi1oREPy9H9",
"outputId": "919faa4f-3e5f-4c2b-a75e-276fefdc6808"
},
"source": [
"df2.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['name', 'annotationSpecId', 'createTime', 'evaluatedExampleCount',\n",
" 'displayName', 'evaluatedBoundingBoxCount', 'boundingBoxMetricsEntries',\n",
" 'boundingBoxMeanAveragePrecision'],\n",
" dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 402
},
"id": "UtdmF8q-yxaj",
"outputId": "d20c4516-2f50-4833-82fa-073f0ce83ecf"
},
"source": [
"df3 = pd.concat(\n",
" [ df2.drop(['boundingBoxMetricsEntries'], axis=1),\n",
" df2['boundingBoxMetricsEntries'].apply(pd.Series)],\n",
" axis=1\n",
")\n",
"df3"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"text/plain": [
" name ... confidenceMetricsEntries\n",
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"\n",
"[102 rows x 10 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 402
},
"id": "R1is6ohxzRSG",
"outputId": "e4277501-cbbf-43b0-a89c-1d28e2a53ccb"
},
"source": [
"df4 = df3.explode('confidenceMetricsEntries')\n",
"df4"
],
"execution_count": null,
"outputs": [
{
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},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 570
},
"id": "X_TTrjm8ziVG",
"outputId": "f72cedd2-f99b-4c99-9151-672489b6c2ea"
},
"source": [
"df5 = pd.concat(\n",
" [ df4.drop(['confidenceMetricsEntries'], axis=1),\n",
" df4['confidenceMetricsEntries'].apply(pd.Series)],\n",
" axis=1\n",
")\n",
"df5"
],
"execution_count": null,
"outputs": [
{
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" <td>7089621289085698048</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
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" <td>140</td>\n",
" <td>0.730516</td>\n",
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" <td>0.968856</td>\n",
" <td>0.028571</td>\n",
" <td>1.000000</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>7089621289085698048</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>131</td>\n",
" <td>raft</td>\n",
" <td>140</td>\n",
" <td>0.730516</td>\n",
" <td>0.85</td>\n",
" <td>0.322413</td>\n",
" <td>0.972023</td>\n",
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" <td>1.000000</td>\n",
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" <td>projects/702148157522/locations/us-central1/mo...</td>\n",
" <td>7089621289085698048</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
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" <td>7089621289085698048</td>\n",
" <td>2020-11-02T06:27:54.249904Z</td>\n",
" <td>131</td>\n",
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" <td>140</td>\n",
" <td>0.730516</td>\n",
" <td>0.85</td>\n",
" <td>0.322413</td>\n",
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" <td>1.000000</td>\n",
" <td>0.014184</td>\n",
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],
"text/plain": [
" name ... f1Score\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.097521\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.220575\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.284337\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.735849\n",
"0 projects/702148157522/locations/us-central1/mo... ... 0.743590\n",
".. ... ... ...\n",
"5 projects/702148157522/locations/us-central1/mo... ... 0.068966\n",
"5 projects/702148157522/locations/us-central1/mo... ... 0.055556\n",
"5 projects/702148157522/locations/us-central1/mo... ... 0.041958\n",
"5 projects/702148157522/locations/us-central1/mo... ... 0.028169\n",
"5 projects/702148157522/locations/us-central1/mo... ... 0.014184\n",
"\n",
"[11076 rows x 13 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "UYN1Z3cJz1OA",
"outputId": "fbb19715-2dd8-4df2-ea96-f20e787c4acf"
},
"source": [
"df5[(df5['iouThreshold'] >= 0.5) & (df5['confidenceThreshold'] >= 0.5)]['f1Score'].hist()"
],
"execution_count": null,
"outputs": [
{
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"metadata": {
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},
{
"cell_type": "code",
"metadata": {
"id": "dunYFd9Ez67I"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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