From 359264cc2df32b5012ece5c700b78047e8c90e92 Mon Sep 17 00:00:00 2001 From: Aleksandr Smetanin Date: Thu, 23 Feb 2023 11:04:57 +0100 Subject: [PATCH 1/2] add notebook for explainability monitoring with sagemaker and shap --- ...ainability-monitoring-promo-planning.ipynb | 595 ++++++++++++++++++ 1 file changed, 595 insertions(+) create mode 100644 notebooks/explainability-monitoring-promo-planning.ipynb diff --git a/notebooks/explainability-monitoring-promo-planning.ipynb b/notebooks/explainability-monitoring-promo-planning.ipynb new file mode 100644 index 0000000..319e175 --- /dev/null +++ b/notebooks/explainability-monitoring-promo-planning.ipynb @@ -0,0 +1,595 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade boto3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "import time\n", + "import pandas as pd\n", + "import threading\n", + "\n", + "from datetime import datetime\n", + "\n", + "from sagemaker import get_execution_role, image_uris, Session\n", + "from sagemaker.clarify import (\n", + " DataConfig,\n", + " ModelConfig,\n", + " SHAPConfig,\n", + ")\n", + "from sagemaker.model import Model\n", + "from sagemaker.model_monitor import (\n", + " CronExpressionGenerator,\n", + " DataCaptureConfig,\n", + " ExplainabilityAnalysisConfig,\n", + " ModelExplainabilityMonitor,\n", + ")\n", + "from sagemaker.predictor import Predictor\n", + "from sagemaker.s3 import S3Downloader, S3Uploader" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basic Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "role = get_execution_role()\n", + "print(f\"Execution Role: {role}\")\n", + "\n", + "sagemaker_session = Session()\n", + "sagemaker_client = sagemaker_session.sagemaker_client\n", + "sagemaker_runtime_client = sagemaker_session.sagemaker_runtime_client\n", + "\n", + "region = sagemaker_session.boto_region_name\n", + "print(f\"AWS region: {region}\")\n", + "\n", + "# A different bucket can be used, but make sure the role for this notebook has\n", + "# the s3:PutObject permissions. This is the bucket into which the data is captured\n", + "bucket = Session().default_bucket()\n", + "print(f\"Demo Bucket: {bucket}\")\n", + "prefix = \"sagemaker/shap-observability-promo-planning\"\n", + "s3_key = f\"s3://{bucket}/{prefix}\"\n", + "print(f\"S3 key: {s3_key}\")\n", + "\n", + "s3_capture_upload_path = f\"{s3_key}/datacapture\"\n", + "s3_report_path = f\"{s3_key}/reports\"\n", + "\n", + "print(f\"Capture path: {s3_capture_upload_path}\")\n", + "print(f\"Report path: {s3_report_path}\")\n", + "\n", + "baseline_results_uri = f\"{s3_key}/baselining\"\n", + "print(f\"Baseline results uri: {baseline_results_uri}\")\n", + "\n", + "endpoint_instance_count = 1\n", + "endpoint_instance_type = \"ml.m5.large\"\n", + "schedule_expression = CronExpressionGenerator.hourly()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test bucket connectivity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload a test file\n", + "test_file = 'upload-test-file.txt'\n", + "with open(test_file, 'w') as f:\n", + " f.write('Hello world!\\n')\n", + "\n", + "S3Uploader.upload(test_file, f\"s3://{bucket}/test_upload\")\n", + "print(\"Success! We are all set to proceed.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model and Data Preparation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s3_data_uri = 's3://adp-rnd-ml-datasets/promotion-planning/validation/data.csv'\n", + "s3_model_uri = 's3://adp-rnd-ml-models/promotion-planning/model/promotion-planning-train-job-2023-01-31-084806/output/model.tar.gz'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_type = 'text/csv'\n", + "\n", + "model_dir = 'model'\n", + "model_file = f'{model_dir}/model.tar.gz'\n", + "S3Downloader.download(s3_model_uri, model_dir)\n", + "\n", + "\n", + "dataset_dir = 'data'\n", + "S3Downloader.download(s3_data_uri, dataset_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(f'{dataset_dir}/data.csv')\n", + "print('SHAPE:', df.shape)\n", + "\n", + "all_headers = df.columns.tolist()\n", + "label_header = all_headers[0]\n", + "all_headers[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fraction = 0.01\n", + "test_idx = df.sample(frac=fraction).index\n", + "test_data = df[df.index.isin(test_idx)]\n", + "print('Test shape:', test_data.shape)\n", + "test_dataset_dir = 'test'\n", + "test_dataset = f'{test_dataset_dir}/test.csv'\n", + "test_data.drop(label_header, axis=1).to_csv(test_dataset, index=False, header=False)\n", + "\n", + "\n", + "val_data = df[~df.index.isin(test_idx)].sample(frac=fraction)\n", + "print('Validation shape:', val_data.shape)\n", + "validation_dataset_dir = 'validation'\n", + "validation_dataset = f'{validation_dataset_dir}/validation.csv'\n", + "val_data.to_csv(validation_dataset, index=False, header=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_url = S3Uploader.upload(model_file, s3_key)\n", + "print(f\"Model file has been uploaded to {model_url}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_name = f\"shap-observability-promo-planning-{datetime.utcnow():%Y-%m-%d-%H%M}\"\n", + "print(\"Model name: \", model_name)\n", + "endpoint_name = f\"shap-observability-promo-planning-{datetime.utcnow():%Y-%m-%d-%H%M}\"\n", + "print(\"Endpoint name: \", endpoint_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_uri = image_uris.retrieve(\"xgboost\", region, '0.90-1')\n", + "print(f\"XGBoost image uri: {image_uri}\")\n", + "model = Model(\n", + " role=role,\n", + " name=model_name,\n", + " image_uri=image_uri,\n", + " model_data=model_url,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "\n", + "data_capture_config = DataCaptureConfig(\n", + " enable_capture=True,\n", + " sampling_percentage=100,\n", + " destination_s3_uri=s3_capture_upload_path,\n", + ")\n", + "print(f\"Deploying model {model_name} to endpoint {endpoint_name}\")\n", + "model.deploy(\n", + " initial_instance_count=endpoint_instance_count,\n", + " instance_type=endpoint_instance_type,\n", + " endpoint_name=endpoint_name,\n", + " data_capture_config=data_capture_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up endpoint invokations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class WorkerThread(threading.Thread):\n", + " def __init__(self, do_run, *args, **kwargs):\n", + " super(WorkerThread, self).__init__(*args, **kwargs)\n", + " self.__do_run = do_run\n", + " self.__terminate_event = threading.Event()\n", + "\n", + " def terminate(self):\n", + " self.__terminate_event.set()\n", + "\n", + " def run(self):\n", + " while not self.__terminate_event.is_set():\n", + " self.__do_run(self.__terminate_event)\n", + "\n", + "\n", + "def invoke_endpoint(terminate_event):\n", + " with open(test_dataset, \"r\") as f:\n", + " i = 0\n", + " for row in f:\n", + " payload = row.rstrip(\"\\n\")\n", + " response = sagemaker_runtime_client.invoke_endpoint(\n", + " EndpointName=endpoint_name,\n", + " ContentType=\"text/csv\",\n", + " Body=payload,\n", + " InferenceId=str(i), # unique ID per row\n", + " )\n", + " i += 1\n", + " response[\"Body\"].read()\n", + " time.sleep(1)\n", + " if terminate_event.is_set():\n", + " break\n", + "\n", + "\n", + "# Keep invoking the endpoint with test data\n", + "invoke_endpoint_thread = WorkerThread(do_run=invoke_endpoint)\n", + "invoke_endpoint_thread.start()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up monitoring job" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_config = ModelConfig(\n", + " model_name=model_name,\n", + " instance_count=endpoint_instance_count,\n", + " instance_type=endpoint_instance_type,\n", + " content_type=dataset_type,\n", + " accept_type=dataset_type,\n", + ")\n", + "\n", + "\n", + "\n", + "model_explainability_monitor = ModelExplainabilityMonitor(\n", + " role=role,\n", + " sagemaker_session=sagemaker_session,\n", + " max_runtime_in_seconds=1800,\n", + ")\n", + "\n", + "\n", + "model_explainability_baselining_job_result_uri = f\"{baseline_results_uri}/model_explainability\"\n", + "print(f'Explainability baseline s3 uri: {model_explainability_baselining_job_result_uri}')\n", + "model_explainability_data_config = DataConfig(\n", + " s3_data_input_path=validation_dataset,\n", + " s3_output_path=model_explainability_baselining_job_result_uri,\n", + " label=label_header,\n", + " headers=all_headers,\n", + " dataset_type=dataset_type,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Shap baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "test_dataframe = pd.read_csv(test_dataset, header=None)\n", + "shap_baseline = test_dataframe.sample(frac=fraction).values.tolist()\n", + "\n", + "\n", + "shap_config = SHAPConfig(\n", + " baseline=shap_baseline,\n", + " num_samples=50,\n", + " agg_method=\"mean_abs\",\n", + " save_local_shap_values=False,\n", + ")\n", + "\n", + "model_explainability_monitor.suggest_baseline(\n", + " data_config=model_explainability_data_config,\n", + " model_config=model_config,\n", + " explainability_config=shap_config,\n", + ")\n", + "latest_baselining_job_name = model_explainability_monitor.latest_baselining_job_name\n", + "print(f\"ModelExplainabilityMonitor baselining job: {latest_baselining_job_name}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Run explainability baselining job" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "model_explainability_monitor.latest_baselining_job.wait(logs=False)\n", + "model_explainability_constraints = model_explainability_monitor.suggested_constraints()\n", + "print()\n", + "print(\n", + " f\"ModelExplainabilityMonitor suggested constraints: {model_explainability_constraints.file_s3_uri}\"\n", + ")\n", + "print(S3Downloader.read_file(model_explainability_constraints.file_s3_uri))\n", + "\n", + "model_explainability_analysis_config = None\n", + "if not model_explainability_monitor.latest_baselining_job:\n", + " # Remove label because only features are required for the analysis\n", + " headers_without_label_header = copy.deepcopy(all_headers)\n", + " headers_without_label_header.remove(label_header)\n", + " model_explainability_analysis_config = ExplainabilityAnalysisConfig(\n", + " explainability_config=shap_config,\n", + " model_config=model_config,\n", + " headers=headers_without_label_header,\n", + " ) \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Monitoring schedule" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def wait_for_execution_to_start(model_monitor):\n", + " print(\n", + " \"A hourly schedule was created above and it will kick off executions ON the hour (plus 0 - 20 min buffer).\"\n", + " )\n", + "\n", + " print(\"Waiting for the first execution to happen\", end=\"\")\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " while \"LastMonitoringExecutionSummary\" not in schedule_desc:\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(60)\n", + " print()\n", + " print(\"Done! Execution has been created\")\n", + "\n", + " print(\"Now waiting for execution to start\", end=\"\")\n", + " while schedule_desc[\"LastMonitoringExecutionSummary\"][\"MonitoringExecutionStatus\"] in \"Pending\":\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(10)\n", + "\n", + " print()\n", + " print(\"Done! Execution has started\")\n", + "\n", + "\n", + "\n", + "# Waits for the schedule to have last execution in a terminal status.\n", + "def wait_for_execution_to_finish(model_monitor):\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " execution_summary = schedule_desc.get(\"LastMonitoringExecutionSummary\")\n", + " if execution_summary is not None:\n", + " print(\"Waiting for execution to finish\", end=\"\")\n", + " while execution_summary[\"MonitoringExecutionStatus\"] not in [\n", + " \"Completed\",\n", + " \"CompletedWithViolations\",\n", + " \"Failed\",\n", + " \"Stopped\",\n", + " ]:\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(60)\n", + " schedule_desc = model_monitor.describe_schedule()\n", + " execution_summary = schedule_desc[\"LastMonitoringExecutionSummary\"]\n", + " print()\n", + " print(\"Done! Execution has finished\")\n", + " else:\n", + " print(\"Last execution not found\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_explainability_monitor.create_monitoring_schedule(\n", + " output_s3_uri=s3_report_path,\n", + " endpoint_input=endpoint_name,\n", + " schedule_cron_expression=schedule_expression,\n", + " analysis_config=model_explainability_analysis_config\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wait_for_execution_to_start(model_explainability_monitor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wait_for_execution_to_finish(model_explainability_monitor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "schedule_desc = model_explainability_monitor.describe_schedule()\n", + "execution_summary = schedule_desc.get(\"LastMonitoringExecutionSummary\")\n", + "if execution_summary and execution_summary[\"MonitoringExecutionStatus\"] in [\n", + " \"Completed\",\n", + " \"CompletedWithViolations\",\n", + "]:\n", + " last_model_explainability_monitor_execution = model_explainability_monitor.list_executions()[-1]\n", + " last_model_explainability_monitor_execution_report_uri = (\n", + " last_model_explainability_monitor_execution.output.destination\n", + " )\n", + " print(f\"Report URI: {last_model_explainability_monitor_execution_report_uri}\")\n", + " last_model_explainability_monitor_execution_report_files = sorted(\n", + " S3Downloader.list(last_model_explainability_monitor_execution_report_uri)\n", + " )\n", + " print(\"Found Report Files:\")\n", + " print(\"\\n \".join(last_model_explainability_monitor_execution_report_files))\n", + "else:\n", + " last_model_explainability_monitor_execution = None\n", + " print(\n", + " \"====STOP==== \\n No completed executions to inspect further. Please wait till an execution completes or investigate previously reported failures.\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if last_model_explainability_monitor_execution:\n", + " model_explainability_violations = (\n", + " last_model_explainability_monitor_execution.constraint_violations()\n", + " )\n", + " if model_explainability_violations:\n", + " print(model_explainability_violations.body_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "model_explainability_monitor.stop_monitoring_schedule()\n", + "model_explainability_monitor.delete_monitoring_schedule()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "invoke_endpoint_thread.terminate()\n", + "\n", + "predictor = Predictor(endpoint_name, sagemaker_session=sagemaker_session)\n", + "predictor.delete_endpoint()\n", + "predictor.delete_model()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "conda_python3", + "language": "python", + "name": "conda_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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 814e9a4c0c1cc61941ab8ade27df0860fb8cb885 Mon Sep 17 00:00:00 2001 From: Aleksandr Smetanin Date: Mon, 6 Mar 2023 18:37:27 +0100 Subject: [PATCH 2/2] add comments and images --- ...ainability-monitoring-promo-planning.ipynb | 802 +++++++++++++++--- notebooks/images/sm-shap-chart.png | Bin 0 -> 115748 bytes .../images/sm-shap-explainability-report.png | Bin 0 -> 221957 bytes .../images/sm-shap-monitoring-job-history.png | Bin 0 -> 139427 bytes .../images/sm-shap-monitoring-schedule.png | Bin 0 -> 226458 bytes 5 files changed, 696 insertions(+), 106 deletions(-) create mode 100644 notebooks/images/sm-shap-chart.png create mode 100644 notebooks/images/sm-shap-explainability-report.png create mode 100644 notebooks/images/sm-shap-monitoring-job-history.png create mode 100644 notebooks/images/sm-shap-monitoring-schedule.png diff --git a/notebooks/explainability-monitoring-promo-planning.ipynb b/notebooks/explainability-monitoring-promo-planning.ipynb index 319e175..7b824e3 100644 --- a/notebooks/explainability-monitoring-promo-planning.ipynb +++ b/notebooks/explainability-monitoring-promo-planning.ipynb @@ -1,5 +1,47 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Attribution Drift Monitoring with AWS SageMaker Clarity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This Jupyter notebook shows how to perform model bias explainability monitoring with AWS SageMaker (based on [docs](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker_model_monitor/fairness_and_explainability/SageMaker-Model-Monitor-Fairness-and-Explainability.html))\n", + "\n", + "\n", + "Aamazon SageMaker Clarify explainability monitoring offers tools to provide global explanations of models and to explain the predictions of a deployed model producing inferences. Such model explanation tools can help ML modelers and developers and other internal stakeholders understand model characteristics as a whole prior to deployment and to debug predictions provided by the model once deployed. The current offering includes a scalable and efficient implementation of [**SHAP**](https://papers.nips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html), based on the concept of the [**Shapley value**](https://en.wikipedia.org/wiki/Shapley_value) from the field of cooperative game theory that assigns each feature an importance value for a particular prediction.\n", + "\n", + "\n", + "Notebook structure:\n", + "\n", + "## Table of Contents\n", + "1. **[Configuration](#Configuration)** \n", + "1. **[Model and Data Preparation](#Model-and-Data-Preparation)**\n", + "1. **[Deploying model for ML Observability](#Deploying-model-for-ML-Observability)**\n", + "1. **[Generate traffic](#Generate-traffic)** - Provides artifictial traffic for explainability metrics\n", + "1. **[Setting up monitoring job](#Setting-up-monitoring-job)** - Creates Monitoring tasks by creating baseline and scheduling regular monitoring\n", + "1. **[Cleaning up](#Cleaning-up)** - Removes all the created resources.\n", + "\n", + "Prerequisites:\n", + "\n", + "- Existing Roles with all needed permissions (S3, SageMaker, etc.)\n", + "- Configured SageMaker Domain\n", + "- SageMaker Studio user\n", + "- S3 bucket with pretrained model and data\n", + "\n", + "One can use SageMaker Studio or Sagemaker Notebook instances (Jupyter-like environment) to run this notebook. \n", + "To do that, follow the next steps:\n", + "\n", + "1. Run the SageMaker Studio or Create new SageMaker notebook instance\n", + "1. Clone this repository (https://github.com/griddynamics/gd-ml-observability.git)\n", + "\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -9,17 +51,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ - "%pip install --upgrade boto3" + "%pip install -q --upgrade boto3" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 7, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import copy\n", @@ -50,14 +104,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Basic Configuration" + "## Configuration" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution Role: arn:aws:iam::125667932402:role/service-role/AmazonSageMaker-ExecutionRole-20230117T121938\n", + "AWS region: us-east-1\n", + "Demo Bucket: sagemaker-us-east-1-125667932402\n", + "S3 key: s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning\n", + "Capture path: s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/datacapture\n", + "Report path: s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/reports\n", + "Baseline results uri: s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/baselining\n" + ] + } + ], "source": [ "role = get_execution_role()\n", "print(f\"Execution Role: {role}\")\n", @@ -100,9 +170,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! We are all set to proceed.\n" + ] + } + ], "source": [ "# Upload a test file\n", "test_file = 'upload-test-file.txt'\n", @@ -117,13 +197,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Model and Data Preparation" + "## Model and Data Preparation" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, + "source": [ + "For the explainability demo promotion planning dataset is used with pre-trained XGBoost model. \n", + "We need to specify S3 URI for pre-trained model and data which will be used to compute baseline data set for shap." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "s3_data_uri = 's3://adp-rnd-ml-datasets/promotion-planning/validation/data.csv'\n", @@ -132,8 +222,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 11, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "dataset_type = 'text/csv'\n", @@ -149,9 +241,38 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SHAPE: (57834, 111)\n" + ] + }, + { + "data": { + "text/plain": [ + "['OUTPUT_LABEL',\n", + " 'amt',\n", + " 'oft',\n", + " 'amount_365_days_lag',\n", + " 'off_365_days_lag',\n", + " 'black_friday',\n", + " 'business_day',\n", + " 'cyber_monday',\n", + " 'day_of_week',\n", + " 'day_of_month']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = pd.read_csv(f'{dataset_dir}/data.csv')\n", "print('SHAPE:', df.shape)\n", @@ -162,10 +283,28 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], + "source": [ + " For purposes of this demo we won't be using just a fraction of all available data." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test shape: (578, 111)\n", + "Validation shape: (573, 111)\n" + ] + } + ], "source": [ "fraction = 0.01\n", "test_idx = df.sample(frac=fraction).index\n", @@ -185,9 +324,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model file has been uploaded to s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/model.tar.gz\n" + ] + } + ], "source": [ "model_url = S3Uploader.upload(model_file, s3_key)\n", "print(f\"Model file has been uploaded to {model_url}\")" @@ -197,27 +346,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Create endpoint" + "## Deploying model for ML Observability" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "model_name = f\"shap-observability-promo-planning-{datetime.utcnow():%Y-%m-%d-%H%M}\"\n", - "print(\"Model name: \", model_name)\n", - "endpoint_name = f\"shap-observability-promo-planning-{datetime.utcnow():%Y-%m-%d-%H%M}\"\n", - "print(\"Endpoint name: \", endpoint_name)" + "Setting up the pre-trained model" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model name: shap-observability-promo-planning-2023-03-06-1005\n", + "Endpoint name: shap-observability-promo-planning-2023-03-06-1005\n", + "XGBoost image uri: 683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:0.90-1-cpu-py3\n" + ] + } + ], "source": [ + "model_name = f\"shap-observability-promo-planning-{datetime.utcnow():%Y-%m-%d-%H%M}\"\n", + "print(\"Model name: \", model_name)\n", + "endpoint_name = f\"shap-observability-promo-planning-{datetime.utcnow():%Y-%m-%d-%H%M}\"\n", + "print(\"Endpoint name: \", endpoint_name)\n", + "\n", "image_uri = image_uris.retrieve(\"xgboost\", region, '0.90-1')\n", "print(f\"XGBoost image uri: {image_uri}\")\n", "model = Model(\n", @@ -226,13 +387,55 @@ " image_uri=image_uri,\n", " model_data=model_url,\n", " sagemaker_session=sagemaker_session,\n", - ")\n", - "\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Setting up data capture config to be able to monitor model bias based on the stored data" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ "data_capture_config = DataCaptureConfig(\n", " enable_capture=True,\n", " sampling_percentage=100,\n", " destination_s3_uri=s3_capture_upload_path,\n", - ")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Deploying the model to the endpoint that will be monitored" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deploying model shap-observability-promo-planning-2023-03-06-1005 to endpoint shap-observability-promo-planning-2023-03-06-1005\n", + "------!" + ] + } + ], + "source": [ "print(f\"Deploying model {model_name} to endpoint {endpoint_name}\")\n", "model.deploy(\n", " initial_instance_count=endpoint_instance_count,\n", @@ -246,13 +449,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Set up endpoint invokations" + "## Generate traffic" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, + "source": [ + "If there is no traffic, the monitoring jobs are marked as Failed since there is no data to process. \n", + "So for this example we generate traffic artificial traffic based on test dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "\n", @@ -297,13 +510,56 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Set up monitoring job" + "## Setting up monitoring job" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating model explainability monitor" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explainability baseline s3 uri: s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/baselining/model_explainability\n" + ] + } + ], + "source": [ + "model_explainability_monitor = ModelExplainabilityMonitor(\n", + " role=role,\n", + " sagemaker_session=sagemaker_session,\n", + " max_runtime_in_seconds=1800,\n", + ")\n", + "\n", + "\n", + "model_explainability_baselining_job_result_uri = f\"{baseline_results_uri}/model_explainability\"\n", + "print(f'Explainability baseline s3 uri: {model_explainability_baselining_job_result_uri}')" + ] + }, + { + "cell_type": "markdown", "metadata": {}, + "source": [ + "Creating model and data config" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "model_config = ModelConfig(\n", @@ -314,17 +570,6 @@ " accept_type=dataset_type,\n", ")\n", "\n", - "\n", - "\n", - "model_explainability_monitor = ModelExplainabilityMonitor(\n", - " role=role,\n", - " sagemaker_session=sagemaker_session,\n", - " max_runtime_in_seconds=1800,\n", - ")\n", - "\n", - "\n", - "model_explainability_baselining_job_result_uri = f\"{baseline_results_uri}/model_explainability\"\n", - "print(f'Explainability baseline s3 uri: {model_explainability_baselining_job_result_uri}')\n", "model_explainability_data_config = DataConfig(\n", " s3_data_input_path=validation_dataset,\n", " s3_output_path=model_explainability_baselining_job_result_uri,\n", @@ -342,12 +587,21 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, + "source": [ + "In order to create explainability baseline, we need to provide a baseline dataset for Kernel Shap algorithm.\n", + "Number of samples determines the size of generated synthetic dataset (if not provided clarify will choose a value based on number of features). There are three ways to aggregate shap values: `mean_abs` (mean of absolute SHAP values), `median` (median of SHAP values for all instances) and `mean_sq` (mean of squared SHAP values)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "\n", "test_dataframe = pd.read_csv(test_dataset, header=None)\n", "shap_baseline = test_dataframe.sample(frac=fraction).values.tolist()\n", "\n", @@ -357,8 +611,39 @@ " num_samples=50,\n", " agg_method=\"mean_abs\",\n", " save_local_shap_values=False,\n", - ")\n", - "\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Run explainability baselining job" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker:Creating processing-job with name baseline-suggestion-job-2023-03-06-10-17-13-690\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ModelExplainabilityMonitor baselining job: baseline-suggestion-job-2023-03-06-10-17-13-690\n" + ] + } + ], + "source": [ "model_explainability_monitor.suggest_baseline(\n", " data_config=model_explainability_data_config,\n", " model_config=model_config,\n", @@ -372,23 +657,169 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Run explainability baselining job" + "Wait for baselining job and review the constaints" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................................................................................................!" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:botocore.credentials:Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ModelExplainabilityMonitor suggested constraints: s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/baselining/model_explainability/analysis.json\n", + "{\n", + " \"version\": \"1.0\",\n", + " \"explanations\": {\n", + " \"kernel_shap\": {\n", + " \"label0\": {\n", + " \"global_shap_values\": {\n", + " \"amt\": 0.10948012214978607,\n", + " \"oft\": 0.10853867704455387,\n", + " \"amount_365_days_lag\": 0.10292516679605697,\n", + " \"off_365_days_lag\": 0.11545188903392774,\n", + " \"black_friday\": 0.09804559208200446,\n", + " \"business_day\": 0.11297093042708263,\n", + " \"cyber_monday\": 0.11255411381564072,\n", + " \"day_of_week\": 0.1071939282195742,\n", + " \"day_of_month\": 0.11620003500504866,\n", + " \"month\": 0.10880362477807314,\n", + " \"is_holiday\": 0.10026392279858574,\n", + " \"list_price\": 0.506711290138479,\n", + " \"list_price_7_days_lag\": 0.11800608684558543,\n", + " \"list_price_30_days_lag\": 0.21218059951008095,\n", + " \"list_price_365_days_lag\": 0.09798197500285986,\n", + " \"post_holiday\": 0.1098804984022009,\n", + " \"pre_holiday\": 0.10016967365175429,\n", + " \"profit_365_days_lag\": 0.13291067261012388,\n", + " \"quantity_365_days_lag\": 0.14637940625479695,\n", + " \"promo_price\": 0.21770283722827494,\n", + " \"promo_price_7_days_lag\": 0.11190892386092048,\n", + " \"promo_price_30_days_lag\": 0.09842779868694215,\n", + " \"promo_price_365_days_lag\": 0.09502923450685478,\n", + " \"purchase_price\": 0.15528941556558437,\n", + " \"ratio_list_price_to_purchase_price\": 0.12373419983583203,\n", + " \"ratio_list_price_to_purchase_price_7_days_lag\": 0.10299942812377823,\n", + " \"ratio_list_price_to_purchase_price_30_days_lag\": 0.09802656115694341,\n", + " \"ratio_list_price_to_purchase_price_365_days_lag\": 0.10960291324109075,\n", + " \"ratio_promo_price_to_list_price\": 0.23390822838115832,\n", + " \"ratio_promo_price_to_list_price_7_days_lag\": 0.11964902540624262,\n", + " \"ratio_promo_price_to_list_price_30_days_lag\": 0.208234085642571,\n", + " \"ratio_promo_price_to_list_price_365_days_lag\": 0.10114835002583304,\n", + " \"ratio_promo_price_to_purchase_price\": 0.11991915780945882,\n", + " \"ratio_promo_price_to_purchase_price_7_days_lag\": 0.09458811055155714,\n", + " \"ratio_promo_price_to_purchase_price_30_days_lag\": 0.09958696575882661,\n", + " \"ratio_promo_price_to_purchase_price_365_days_lag\": 0.09744384475847295,\n", + " \"super_bowl\": 0.10334844558545071,\n", + " \"season_of_year_one_hot_0\": 0.10322065371209216,\n", + " \"season_of_year_one_hot_1\": 0.0978278709937913,\n", + " \"season_of_year_one_hot_2\": 0.10885422587158512,\n", + " \"season_of_year_one_hot_3\": 0.09546615791048331,\n", + " \"occasion_one_hot_0\": 0.1086362024160909,\n", + " \"occasion_one_hot_1\": 0.10174865677631961,\n", + " \"occasion_one_hot_2\": 0.11505962294717757,\n", + " \"occasion_one_hot_3\": 0.091129324919371,\n", + " \"occasion_one_hot_4\": 0.1086840188270564,\n", + " \"occasion_one_hot_5\": 0.11460496065820619,\n", + " \"occasion_one_hot_6\": 0.10497980358354382,\n", + " \"occasion_one_hot_7\": 0.09815689919396274,\n", + " \"dress_length_one_hot_0\": 0.1002487162101105,\n", + " \"dress_length_one_hot_1\": 0.10383538205895343,\n", + " \"dress_length_one_hot_2\": 0.10312540511246755,\n", + " \"dress_length_one_hot_3\": 0.1043003394041985,\n", + " \"dress_types_one_hot_0\": 0.10400076944028507,\n", + " \"dress_types_one_hot_1\": 0.10138649853273446,\n", + " \"dress_types_one_hot_2\": 0.10930426805410717,\n", + " \"dress_types_one_hot_3\": 0.12152324177662033,\n", + " \"dress_types_one_hot_4\": 0.10714419452367374,\n", + " \"dress_types_one_hot_5\": 0.10404574293893364,\n", + " \"dress_types_one_hot_6\": 0.11036050004825468,\n", + " \"dress_types_one_hot_7\": 0.14217532011890302,\n", + " \"dress_types_one_hot_8\": 0.10985604939356246,\n", + " \"dress_types_one_hot_9\": 0.10875167657463633,\n", + " \"dress_types_one_hot_10\": 0.09929935068210678,\n", + " \"dress_types_one_hot_11\": 0.10816445625172055,\n", + " \"material_one_hot_0\": 0.1169215728312945,\n", + " \"material_one_hot_1\": 0.0966299357681652,\n", + " \"material_one_hot_2\": 0.11408043362923098,\n", + " \"material_one_hot_3\": 0.09676312781703811,\n", + " \"material_one_hot_4\": 0.10583165954531816,\n", + " \"material_one_hot_5\": 0.09682183355584198,\n", + " \"material_one_hot_6\": 0.11162891116148937,\n", + " \"material_one_hot_7\": 0.0999889880261619,\n", + " \"material_one_hot_8\": 0.1008431062402366,\n", + " \"material_one_hot_9\": 0.09252033469402106,\n", + " \"material_one_hot_10\": 0.10140153803449072,\n", + " \"neckline_style_one_hot_0\": 0.10034076070054586,\n", + " \"neckline_style_one_hot_1\": 0.10170723693646244,\n", + " \"neckline_style_one_hot_2\": 0.10712473879055205,\n", + " \"neckline_style_one_hot_3\": 0.10804204168326723,\n", + " \"neckline_style_one_hot_4\": 0.09997906266711504,\n", + " \"neckline_style_one_hot_5\": 0.09219191529749457,\n", + " \"neckline_style_one_hot_6\": 0.09906187353225106,\n", + " \"neckline_style_one_hot_7\": 0.10349327712023981,\n", + " \"color_category_one_hot_0\": 0.17467884345743942,\n", + " \"color_category_one_hot_1\": 0.13175136646218338,\n", + " \"color_category_one_hot_2\": 0.10429479313053264,\n", + " \"color_category_one_hot_3\": 0.10172490686121644,\n", + " \"color_category_one_hot_4\": 0.10297211578117726,\n", + " \"color_category_one_hot_5\": 0.10149597782975311,\n", + " \"color_category_one_hot_6\": 0.10139388201224835,\n", + " \"color_category_one_hot_7\": 0.10880969566521541,\n", + " \"color_category_one_hot_8\": 0.1136614344496582,\n", + " \"color_category_one_hot_9\": 0.09186830208395018,\n", + " \"color_category_one_hot_10\": 0.09771373919144166,\n", + " \"color_category_one_hot_11\": 0.11334586331334309,\n", + " \"color_category_one_hot_12\": 0.10017991258039771,\n", + " \"color_category_one_hot_13\": 0.11312960236694848,\n", + " \"color_category_one_hot_14\": 0.11541271978929242,\n", + " \"color_category_one_hot_15\": 0.10327216093405615,\n", + " \"size_one_hot_0\": 0.10785212146689158,\n", + " \"size_one_hot_1\": 0.11141512747936261,\n", + " \"size_one_hot_2\": 0.10979325922577386,\n", + " \"size_one_hot_3\": 0.10691508607399734,\n", + " \"size_one_hot_4\": 0.1067305058871434,\n", + " \"size_one_hot_5\": 0.0971575940917513,\n", + " \"size_one_hot_6\": 0.11754653617217176,\n", + " \"size_one_hot_7\": 0.3089827303796554,\n", + " \"size_one_hot_8\": 0.11105645757546824,\n", + " \"size_one_hot_9\": 0.7021859316571338\n", + " },\n", + " \"expected_value\": 0.42680683235327405\n", + " }\n", + " }\n", + " }\n", + "}\n" + ] + } + ], "source": [ - "\n", "model_explainability_monitor.latest_baselining_job.wait(logs=False)\n", "model_explainability_constraints = model_explainability_monitor.suggested_constraints()\n", - "print()\n", - "print(\n", - " f\"ModelExplainabilityMonitor suggested constraints: {model_explainability_constraints.file_s3_uri}\"\n", - ")\n", - "print(S3Downloader.read_file(model_explainability_constraints.file_s3_uri))\n", + "if model_explainability_constraints is not None:\n", + " print(\n", + " \"ModelExplainabilityMonitor suggested constraints: \"\n", + " f\"{model_explainability_constraints.file_s3_uri}\"\n", + " )\n", + " print(S3Downloader.read_file(model_explainability_constraints.file_s3_uri))\n", "\n", "model_explainability_analysis_config = None\n", "if not model_explainability_monitor.latest_baselining_job:\n", @@ -412,8 +843,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Uploading analysis config to {s3_uri}.\n", + "INFO:sagemaker.model_monitor.model_monitoring:Creating Monitoring Schedule with name: monitoring-schedule-2023-03-06-10-26-10-817\n" + ] + } + ], + "source": [ + "model_explainability_monitor.create_monitoring_schedule(\n", + " output_s3_uri=s3_report_path,\n", + " endpoint_input=endpoint_name,\n", + " schedule_cron_expression=schedule_expression,\n", + " analysis_config=model_explainability_analysis_config\n", + ")" + ] + }, + { + "cell_type": "markdown", "metadata": {}, + "source": [ + "You can find created monitoring schedule in SageMaker Studio by navigating to Deployments -> Endpoints section in sidebar, choosing you endpoint and opening `Model explainability` tab.\n", + "![Schedule](images/sm-shap-monitoring-schedule.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Wait for monitoring job to start first execution" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "def wait_for_execution_to_start(model_monitor):\n", @@ -465,41 +938,74 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_explainability_monitor.create_monitoring_schedule(\n", - " output_s3_uri=s3_report_path,\n", - " endpoint_input=endpoint_name,\n", - " schedule_cron_expression=schedule_expression,\n", - " analysis_config=model_explainability_analysis_config\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 36, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A hourly schedule was created above and it will kick off executions ON the hour (plus 0 - 20 min buffer).\n", + "Waiting for the first execution to happen\n", + "Done! Execution has been created\n", + "Now waiting for execution to start\n", + "Done! Execution has started\n" + ] + } + ], "source": [ "wait_for_execution_to_start(model_explainability_monitor)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 43, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waiting for execution to finish..\n", + "Done! Execution has finished\n" + ] + } + ], "source": [ "wait_for_execution_to_finish(model_explainability_monitor)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 44, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:botocore.credentials:Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Report URI: s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/reports/shap-observability-promo-planning-2023-03-06-1005/monitoring-schedule-2023-03-06-10-26-10-817/2023/03/06/17\n", + "Found Report Files:\n", + "s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/reports/shap-observability-promo-planning-2023-03-06-1005/monitoring-schedule-2023-03-06-10-26-10-817/2023/03/06/17/analysis.json\n", + " s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/reports/shap-observability-promo-planning-2023-03-06-1005/monitoring-schedule-2023-03-06-10-26-10-817/2023/03/06/17/report.html\n", + " s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/reports/shap-observability-promo-planning-2023-03-06-1005/monitoring-schedule-2023-03-06-10-26-10-817/2023/03/06/17/report.ipynb\n", + " s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/reports/shap-observability-promo-planning-2023-03-06-1005/monitoring-schedule-2023-03-06-10-26-10-817/2023/03/06/17/report.pdf\n" + ] + } + ], "source": [ "\n", "schedule_desc = model_explainability_monitor.describe_schedule()\n", @@ -527,9 +1033,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 46, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Could not retrieve constraints file at location 's3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/reports/shap-observability-promo-planning-2023-03-06-1005/monitoring-schedule-2023-03-06-10-26-10-817/2023/03/06/17/constraint_violations.json'. To manually retrieve ConstraintViolations object from a given uri, use 'my_model_monitor.constraints(my_s3_uri)' or 'ConstraintViolations.from_s3_uri(my_s3_uri)'\n" + ] + } + ], "source": [ "if last_model_explainability_monitor_execution:\n", " model_explainability_violations = (\n", @@ -543,14 +1060,82 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Cleanup" + "Now the feature importance chart is diplayed in SageMaker Studio\n", + "![Chart](images/sm-shap-chart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also view previous Explainability monitoring jobs in `Monitoring job history` tab.\n", + "![Monitoring Jobs History](images/sm-shap-monitoring-job-history.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sagemaker also provides reports, which are saved to S3 to specified `s3_report_path` in multiple formats including HTML and PDF.\n", + "![Report](images/sm-shap-explainability-report.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Report path: s3://sagemaker-us-east-1-125667932402/sagemaker/shap-observability-promo-planning/reports\n" + ] + } + ], + "source": [ + "print(f\"Report path: {s3_report_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleaning up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If there is no plan to use the endpoint further, it should be deleted to avoid incurring additional charges. Note that deleting endpoint does not delete the data that was captured during the model invocations." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Stopping Monitoring Schedule with name: monitoring-schedule-2023-03-06-10-26-10-817\n", + "\n", + "Deleting Monitoring Schedule with name: monitoring-schedule-2023-03-06-10-26-10-817\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Deleting Model Explainability Job Definition with name: model-explainability-job-definition-2023-03-06-10-26-10-817\n" + ] + } + ], "source": [ "\n", "model_explainability_monitor.stop_monitoring_schedule()\n", @@ -559,7 +1144,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -573,9 +1158,9 @@ ], 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