Model Drift Detection with Assays

Wallaroo provides the ability to monitor the input and outputs of models to detect model drift by using model insights. Changes in the inputs or model predictions that fall outside of expected norms, known as data drift, can occur due to errors in the data processing pipeline or due to changes in the environment such as user preference or behavior.

Monitoring tasks called assays provides model insights by comparing a model’s predictions or the input data coming into the model against an established baseline. Changes in the distribution of this data can be an indication of model drift, or of a change in the environment that the model trained for. This can provide tips on whether a model needs to be retrained or the environment data analyzed for accuracy or other needs.

Model Insights provides interactive assays to explore data from a pipeline and learn how the data is behaving. With this information and the knowledge of your particular business use case you can then choose appropriate settings to launch an assay to run on a given frequency as more data is collected.

This tutorial and the assets are available as part of the Wallaroo Tutorials repository.

Wallaroo MLOps API User Management Tutorial

This tutorial focuses on using the Wallaroo MLOps API for assay management and other minor commands. For this tutorial, we will be using the Wallaroo SDK to provide authentication credentials for ease of use examples. See the Wallaroo API Guide for full details on using the Wallaroo MLOps API.

References

The following references are available for more information about Wallaroo and the Wallaroo MLOps API:

• Wallaroo Documentation Site: The Wallaroo Documentation Site.
• Wallaroo MLOps API Documentation from a Wallaroo instance: A Swagger UI based documentation is available from your Wallaroo instance at https://{Wallaroo Domain}/v1/api/docs. For example, if the Wallaroo Domain is example.wallaroo.ai, the Wallaroo MLOps API Documentation is at https://example.wallaroo.ai/v1/api/docs. Note the . is part of the prefix.

IMPORTANT NOTE: The Wallaroo MLOps API is provided as an early access features. Future iterations may adjust the methods and returns to provide a better user experience. Please refer to this guide for updates.

Prerequisites

• An installed Wallaroo instance.
• The following Python libraries installed:
  • requests
  • json
  • wallaroo: The Wallaroo SDK. Included with the Wallaroo JupyterHub service by default.

Connection Steps

Import Libraries

For these examples, we will rely on the wallaroo SDK and requests library for making connections to our sample Wallaroo Ops instance.

pyarrow is the Apache Arrow library used for data schemas in Wallaroo, while base64 is used to convert data schemas to base64 format for model uploads.

For the examples, the Python requests library will be used to make the REST HTTP(S) connections.

import wallaroo

import requests

import json

import pandas as pd

import datetime

import time

Connect to the Wallaroo Instance

The next step is to connect to Wallaroo through the Wallaroo client. The Python library is included in the Wallaroo install and available through the Jupyter Hub interface provided with your Wallaroo environment.

This is accomplished using the wallaroo.Client() command, which provides a URL to grant the SDK permission to your specific Wallaroo environment. When displayed, enter the URL into a browser and confirm permissions. Store the connection into a variable that can be referenced later.

If logging into the Wallaroo instance through the internal JupyterHub service, use wl = wallaroo.Client(). For more information on Wallaroo Client settings, see the Client Connection guide.

# Login through local Wallaroo instance

wl = wallaroo.Client()

Retrieve API Service URL

The Wallaroo SDK provides the API endpoint through the wallaroo.client.api_endpoint variable. This is derived from the Wallaroo OPs DNS settings.

The method wallaroo.client.auth.auth_header() retrieves the HTTP authorization headers for the API connection.

Both of these are used to authenticate for the Wallaroo MLOps API calls used in the future examples.

• References
  • Wallaroo API Connection Guide

display(wl.api_endpoint)
display(wl.auth.auth_header())

'https://doc-test.wallarooexample.ai'
{‘Authorization’: ‘Bearer eyJhbGciOiJSUzI1NiIsInR5cCIgOiAiSldUIiwia2lkIiA6ICI5Y2JRMDNmMHo1NHJCb2k5T0ZjWGdhMXZqeGxpX3VHS21kLVlTVTNSVzVVIn0.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.1HfASbbHssERSeITEeHlTV2YWSfkxjYeBvx5UjXmXStTTLVKBWexJyuHIYQOmuF9w0ru1dC5p51O4-VI1VUyaPxZ0j_H0hb3aOe-SNrGQqTa0pU1do4xJ4BLEvvn7eDVVtsMC11R1SDXdHY3ZucNxTzJ5jXzphEY3-6xIq_sH2VVMIgbkRT-IOpoz8A3S6tL-9rMpaSMgqDzlf0xY_1oyl1Z66MXo0rnKKBSMFrH62SX5qFBpFRkVsQt26VQgB0v2O1e0OPDoBOdmZ1QicUJkMuJSdh4J912qZqTGyNv3vmQuByMv-Bf58r230RRZqrWC6yHPq4w2SXVtpqu3ZYNCA’}

Enablement Management

Enablement Management allows users to see what Wallaroo features have been activated.

List Enablement Features

Lists the enablement features for the Wallaroo instance.

List Enablement Features Parameters

An empty set {}

List Enablement Features Returns

# List enablement features
# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/features/list"

data = {
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
response

{'features': {'plateau': 'true'},
 'name': 'Wallaroo Dev',
 'is_auth_enabled': True}

!curl {wl.api_endpoint}/v1/api/features/list \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{{}}'

{"features":{"plateau":"true"},"name":"Wallaroo Dev","is_auth_enabled":true}

Assays

Create Baseline Summary Statistics

• Endpoint: /v1/api/assays/summarize

Generate static baseline summary stats for a vector or windowed data. For windows data, this requires that inferences have been performed in the target pipeline. For vector data, a numpy array is used.

Create Baseline Summary Statistics Parameters

Create Baseline Summary Statistics Returns

Create Baseline Summary Statistics Examples

The following example code will:

• Create a workspace for our assay values.
• Upload a model and create a pipeline with the model as a pipeline step.
• Perform sample inference and use the inference results create the baseline summary data.

# create a new workspace using the Wallaroo SDK

workspace = wl.get_workspace("sample-api-workspace-assays", create_if_not_exist=True)
display(workspace)
wl.set_current_workspace(workspace)
workspace_id = workspace.id()
workspace_id

{'name': 'sample-api-workspace-assays', 'id': 6, 'archived': False, 'created_by': 'fb2916bc-551e-4a76-88e8-0f7d7720a0f9', 'created_at': '2024-07-29T18:49:03.544471+00:00', 'models': [{'name': 'api-sample-model', 'versions': 2, 'owner_id': '""', 'last_update_time': datetime.datetime(2024, 7, 29, 18, 52, 41, 834943, tzinfo=tzutc()), 'created_at': datetime.datetime(2024, 7, 29, 18, 49, 4, 879661, tzinfo=tzutc())}], 'pipelines': [{'name': 'sample-api-pipeline-with-models', 'create_time': datetime.datetime(2024, 7, 29, 18, 49, 5, 277194, tzinfo=tzutc()), 'definition': '[]'}]}
6

# upload the model via the Wallaroo SDK
example_model_name = f"api-sample-model"
pipeline_name = "sample-api-pipeline-with-models"
model = wl.upload_model(example_model_name, 
                         './models/ccfraud.onnx', 
                          framework=wallaroo.framework.Framework.ONNX)

# create the pipeline
         
pipeline = wl.build_pipeline(pipeline_name)
pipeline.add_model_step(model)

# deploy the pipeline
assay_start = datetime.datetime.now(datetime.timezone.utc)
pipeline.deploy()

# sample inferences
results = pipeline.infer_from_file('./data/cc_data_api_1k.df.json')

results

1001 rows × 4 columns

# set the results to a non-array value
results_baseline_df = results.copy()
results_baseline_df['dense_1']=results_baseline_df['out.dense_1'].map(lambda x: x[0])
# results_baseline_df

# set the numpy array
results_baseline = results_baseline_df['dense_1'].to_numpy()
# the values as a numpy
results_baseline

array([9.9300325e-01, 9.9300325e-01, 9.9300325e-01, ..., 1.1070371e-03,
       8.5330010e-04, 1.2498498e-03])

Create the summary via Requests.

## create the summary via requests

# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/assays/summarize"

data = {'summarizer': {'type': 'UnivariateContinuous',
  'bin_mode': 'Quantile',
  'aggregation': 'Density',
  'metric': 'PSI',
  'num_bins': 5,
  'bin_weights': None,
  'bin_width': None,
  'provided_edges': None},
 'baseline': {'vector': results_baseline.tolist()}}

# display(data)

response = requests.post(endpoint, json=data, headers=headers, verify=True)
baseline_summary = response.json()
display(baseline_summary)

{'count': 1001,
 'min': 1.5199184e-06,
 'max': 1.0,
 'mean': 0.006598251655689411,
 'median': 0.00054195523,
 'std': 0.07661715949339488,
 'edges': [1.5199184e-06,
  0.00026360154,
  0.00045093894,
  0.0006800592,
  0.00096952915,
  1.0,
  None],
 'edge_names': ['left_outlier',
  'q_20',
  'q_40',
  'q_60',
  'q_80',
  'q_100',
  'right_outlier'],
 'aggregated_values': [0.0,
  0.1998001998001998,
  0.1998001998001998,
  0.2007992007992008,
  0.1998001998001998,
  0.1998001998001998,
  0.0],
 'aggregation': 'Density',
 'start': None,
 'end': None}

Create Assay

• Endpoint: /v1/api/assays/create

Create a new array in a specified pipeline. Assays required at least one inference request is completed.

Create Assay Parameters

Create Assay Returns

Create Assay Examples

The following example code will:

• Create an assay from the endpoint /v1/api/assays/create with baseline generated from Create Baseline Summary Statistics.
  • For our example, we will be using the output of the field dense_2 at the index 0 for the iopath.
• The assay id is stored for later examples.

Create assay via Requests.

import datetime

assay_window_end = datetime.datetime.now(datetime.timezone.utc)

## Create assay

# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/assays/create"

# generate random values so the assay is always unique
import string
import random

# # make a random 4 character suffix to prevent overwriting other user's workspaces
suffix= ''.join(random.choice(string.ascii_lowercase) for i in range(4))

exampleAssayName = f"api assay example {suffix}"
# display(exampleAssayName)

data = {
    "name": exampleAssayName,
    "pipeline_id": pipeline.id(),
    "pipeline_name": pipeline_name,
    "active": True,
    "status": "created",
    "baseline": {
        "static": baseline_summary
    },
    "window": {
        "pipeline_name": pipeline_name,
        "model_name": example_model_name,
        "width": "1 minute",
        "start": assay_start.isoformat(),
        "interval": "1 minute",
        "path": "output dense_1 0",
        "workspace_id": workspace_id,
        "locations": []
    },
    "summarizer": {
        "type": "UnivariateContinuous",
        "bin_mode": "Quantile",
        "aggregation": "Density",
        "metric": "PSI",
        "num_bins": 5,
        "bin_weights": None,
        "bin_width": None,
        "provided_edges": None,
        "add_outlier_edges": None
    },
    "warning_threshold": None,
    "alert_threshold": 0.25,
    "run_until": assay_window_end.isoformat(),
    "workspace_id": workspace_id
}

print(data)

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
print(response)
assay_id = response['assay_id']

{'name': 'api assay example yrje', 'pipeline_id': 1, 'pipeline_name': 'sample-api-pipeline-with-models', 'active': True, 'status': 'created', 'baseline': {'static': {'count': 1001, 'min': 1.5199184e-06, 'max': 1.0, 'mean': 0.006598251655689411, 'median': 0.00054195523, 'std': 0.07661715949339488, 'edges': [1.5199184e-06, 0.00026360154, 0.00045093894, 0.0006800592, 0.00096952915, 1.0, None], 'edge_names': ['left_outlier', 'q_20', 'q_40', 'q_60', 'q_80', 'q_100', 'right_outlier'], 'aggregated_values': [0.0, 0.1998001998001998, 0.1998001998001998, 0.2007992007992008, 0.1998001998001998, 0.1998001998001998, 0.0], 'aggregation': 'Density', 'start': None, 'end': None}}, 'window': {'pipeline_name': 'sample-api-pipeline-with-models', 'model_name': 'api-sample-model', 'width': '1 minute', 'start': '2024-07-29T19:48:46.944191+00:00', 'interval': '1 minute', 'path': 'output dense_1 0', 'workspace_id': 6, 'locations': []}, 'summarizer': {'type': 'UnivariateContinuous', 'bin_mode': 'Quantile', 'aggregation': 'Density', 'metric': 'PSI', 'num_bins': 5, 'bin_weights': None, 'bin_width': None, 'provided_edges': None, 'add_outlier_edges': None}, 'warning_threshold': None, 'alert_threshold': 0.25, 'run_until': '2024-07-29T19:49:10.195390+00:00', 'workspace_id': 6}
{'assay_id': 4}

Create assay via curl.

exampleAssayName = f"api assay curl examples {suffix}"

data = {
    "name": exampleAssayName,
    "pipeline_id": pipeline.id(),
    "pipeline_name": pipeline_name,
    "active": True,
    "status": "created",
    "baseline": {
        "static": baseline_summary
    },
    "window": {
        "pipeline_name": pipeline_name,
        "model_name": example_model_name,
        "width": "1 minute",
        "start": assay_start.isoformat(),
        "interval": "1 minute",
        "path": "output dense_1 0",
        "workspace_id": workspace_id,
        "locations": []
    },
    "summarizer": {
        "type": "UnivariateContinuous",
        "bin_mode": "Quantile",
        "aggregation": "Density",
        "metric": "PSI",
        "num_bins": 5,
        "bin_weights": None,
        "bin_width": None,
        "provided_edges": None,
        "add_outlier_edges": None
    },
    "warning_threshold": None,
    "alert_threshold": 0.25,
    "run_until": assay_window_end.isoformat(),
    "workspace_id": workspace_id
}

!curl {wl.api_endpoint}/v1/api/assays/create \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{json.dumps(data)}' > curl_assay_create.json

f = open('curl_assay_create.json')

# returns JSON object as 
# a dictionary
curl_assay_create = json.load(f)
curl_assay_id = curl_assay_create['assay_id']

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  1285  100    14  100  1271     62   5700 --:--:-- --:--:-- --:--:--  5762

List Assays

• Endpoint: /v1/api/assays/list

Lists all assays in the specified pipeline.

List Assays Parameters

List Assays Returns

A list of assays with the following fields.

List Assays Examples

Display a list of all assays in a related to a pipeline.

List all pipeline assays via Requests.

# Get assays
# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/assays/list"

data = {
    "pipeline_id": pipeline.id()
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
response[0]

{'id': 5,
 'name': 'api assay curl examples yrje',
 'active': True,
 'status': 'created',
 'warning_threshold': None,
 'alert_threshold': 0.25,
 'pipeline_id': 1,
 'pipeline_name': 'sample-api-pipeline-with-models',
 'last_run': None,
 'next_run': '2024-07-29T19:49:13.286785+00:00',
 'run_until': '2024-07-29T19:49:10.19539+00:00',
 'updated_at': '2024-07-29T19:49:13.288544+00:00',
 'baseline': {'static': {'count': 1001,
   'min': 1.5199184e-06,
   'max': 1.0,
   'mean': 0.006598251655689411,
   'median': 0.00054195523,
   'std': 0.07661715949339488,
   'edges': [1.5199184e-06,
    0.00026360154,
    0.00045093894,
    0.0006800592,
    0.00096952915,
    1.0,
    None],
   'edge_names': ['left_outlier',
    'q_20',
    'q_40',
    'q_60',
    'q_80',
    'q_100',
    'right_outlier'],
   'aggregated_values': [0.0,
    0.1998001998001998,
    0.1998001998001998,
    0.2007992007992008,
    0.1998001998001998,
    0.1998001998001998,
    0.0],
   'aggregation': 'Density',
   'start': None,
   'end': None}},
 'window': {'interval': '1 minute',
  'model_name': 'api-sample-model',
  'path': 'output dense_1 0',
  'pipeline_name': 'sample-api-pipeline-with-models',
  'start': '2024-07-29T19:48:46.944191Z',
  'width': '1 minute',
  'workspace_id': 6,
  'locations': []},
 'summarizer': {'type': 'UnivariateContinuous',
  'bin_mode': 'Quantile',
  'aggregation': 'Density',
  'metric': 'PSI',
  'num_bins': 5,
  'bin_weights': None,
  'provided_edges': None},
 'workspace_id': 6,
 'workspace_name': 'sample-api-workspace-assays'}

List all pipeline assays via Curl.

data = {
    "pipeline_id": pipeline.id()
}

!curl {wl.api_endpoint}/v1/api/assays/list \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{json.dumps(data)}'

[{"id":5,"name":"api assay curl examples yrje","active":true,"status":"created","warning_threshold":null,"alert_threshold":0.25,"pipeline_id":1,"pipeline_name":"sample-api-pipeline-with-models","last_run":null,"next_run":"2024-07-29T19:49:13.286785+00:00","run_until":"2024-07-29T19:49:10.19539+00:00","updated_at":"2024-07-29T19:49:13.288544+00:00","baseline":{"static":{"count":1001,"min":1.5199184e-6,"max":1.0,"mean":0.006598251655689411,"median":0.00054195523,"std":0.07661715949339488,"edges":[1.5199184e-6,0.00026360154,0.00045093894,0.0006800592,0.00096952915,1.0,null],"edge_names":["left_outlier","q_20","q_40","q_60","q_80","q_100","right_outlier"],"aggregated_values":[0.0,0.1998001998001998,0.1998001998001998,0.2007992007992008,0.1998001998001998,0.1998001998001998,0.0],"aggregation":"Density","start":null,"end":null}},"window":{"interval":"1 minute","model_name":"api-sample-model","path":"output dense_1 0","pipeline_name":"sample-api-pipeline-with-models","start":"2024-07-29T19:48:46.944191Z","width":"1 minute","workspace_id":6,"locations":[]},"summarizer":{"type":"UnivariateContinuous","bin_mode":"Quantile","aggregation":"Density","metric":"PSI","num_bins":5,"bin_weights":null,"provided_edges":null},"workspace_id":6,"workspace_name":"sample-api-workspace-assays"},{"id":4,"name":"api assay example yrje","active":true,"status":"created","warning_threshold":null,"alert_threshold":0.25,"pipeline_id":1,"pipeline_name":"sample-api-pipeline-with-models","last_run":null,"next_run":"2024-07-29T19:49:10.35859+00:00","run_until":"2024-07-29T19:49:10.19539+00:00","updated_at":"2024-07-29T19:49:10.360418+00:00","baseline":{"static":{"count":1001,"min":1.5199184e-6,"max":1.0,"mean":0.006598251655689411,"median":0.00054195523,"std":0.07661715949339488,"edges":[1.5199184e-6,0.00026360154,0.00045093894,0.0006800592,0.00096952915,1.0,null],"edge_names":["left_outlier","q_20","q_40","q_60","q_80","q_100","right_outlier"],"aggregated_values":[0.0,0.1998001998001998,0.1998001998001998,0.2007992007992008,0.1998001998001998,0.1998001998001998,0.0],"aggregation":"Density","start":null,"end":null}},"window":{"interval":"1 minute","model_name":"api-sample-model","path":"output dense_1 0","pipeline_name":"sample-api-pipeline-with-models","start":"2024-07-29T19:48:46.944191Z","width":"1 minute","workspace_id":6,"locations":[]},"summarizer":{"type":"UnivariateContinuous","bin_mode":"Quantile","aggregation":"Density","metric":"PSI","num_bins":5,"bin_weights":null,"provided_edges":null},"workspace_id":6,"workspace_name":"sample-api-workspace-assays"},{"id":3,"name":"api assay curl examples aqsf","active":false,"status":"{\"run_at\": \"2024-07-29T18:55:14.728247311+00:00\",  \"num_ok\": 0, \"num_warnings\": 0, \"num_alerts\": 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Activate or Deactivate Assay

• Endpoint: /v1/api/assays/set_active

Activates or deactivates an existing assay.

Activate or Deactivate Assay Parameters

Activate or Deactivate Assay Returns

Activate or Deactivate Assay Examples

The recently created assay will be deactivated then activated.

Deactivate and activate an assay via Requests

# Deactivate assay

# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/assays/set_active"

data = {
    'id': assay_id,
    'active': False
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
response

{'id': 4, 'active': False}

# Activate assay

# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/assays/set_active"

data = {
    'id': assay_id,
    'active': True
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
response

{'id': 4, 'active': True}

Deactivate and activate an assay via curl

data = {
    'id': curl_assay_id,
    'active': False
}

!curl {wl.api_endpoint}/v1/api/assays/set_active \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{json.dumps(data)}'

{"id":5,"active":false}

data = {
    'id': curl_assay_id,
    'active': True
}

!curl {wl.api_endpoint}/v1/api/assays/set_active \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{json.dumps(data)}'

{"id":5,"active":true}

# perform sample inferences then wait 2 minutes to give time for the inference results to be logged

pipeline.infer_from_file('./data/cc_data_api_1k.df.json')
time.sleep(120)

Get Assay Baseline

• Endpoint: /v1/api/assays/get_baseline

Get Assay Baseline Parameters

Get Assay Baseline Returns

Returns the assay baseline as defined in Create Assay.

Get Assay Baseline Examples

The following examples show retrieving the first three baselines from the recently created baseline.

Retrieve assay baseline via Requests.

## Get Assay Baseline

# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/assays/get_baseline"

data = {
    'workspace_id': workspace_id,
    'pipeline_name': pipeline_name,
    'limit': 1
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
response

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   -0.0982688053,
   0.1475098349,
   -0.3097481612,
   -1.0898892231,
   2.804466934,
   -0.4211447753,
   -0.7315488305,
   -0.5311840374,
   -0.9053830525,
   0.5382443229,
   -0.68327623,
   -1.1848642272,
   0.9872236995,
   -0.0260721428,
   -0.1405966468,
   0.0759031399],
  'metadata.dropped': [],
  'metadata.elapsed': [4976659, 5674079],
  'metadata.last_model': '{"model_name":"api-sample-model","model_sha":"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507"}',
  'metadata.partition': 'engine-5d965d96f4-wxk8l',
  'metadata.pipeline_version': '800fa3cd-607f-4550-90ec-227d262f8a45',
  'out.dense_1': [0.00011596084],
  'time': 1722279178343},
 {'anomaly.count': 0,
  'in.dense_input': [-0.8464537996,
   -0.7608807925,
   2.186072883,
   -0.1614362994,
   -0.4069378894,
   0.734079177,
   -0.4611705734,
   0.4751492626,
   1.4952832213,
   -0.9349105827,
   -0.7654272171,
   0.4362793613,
   -0.6623354486,
   -1.5326388376,
   -1.4311992842,
   -1.0573215483,
   0.9304904478,
   -1.2836000946,
   -1.079419331,
   0.7138847264,
   0.2710369668,
   1.1943291742,
   0.2527110226,
   0.3107779567,
   0.4219366694,
   2.4854295825,
   0.1754876037,
   -0.2362979978,
   0.9979986569],
  'metadata.dropped': [],
  'metadata.elapsed': [4976659, 5674079],
  'metadata.last_model': '{"model_name":"api-sample-model","model_sha":"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507"}',
  'metadata.partition': 'engine-5d965d96f4-wxk8l',
  'metadata.pipeline_version': '800fa3cd-607f-4550-90ec-227d262f8a45',
  'out.dense_1': [0.0002785325],
  'time': 1722279178343},
 {'anomaly.count': 0,
  'in.dense_input': [1.0046377125,
   0.0343666504,
   -1.3512533246,
   0.4160460291,
   0.5910548281,
   -0.8187740907,
   0.5840864966,
   -0.447623496,
   1.1193896296,
   -0.1156579903,
   0.1298919303,
   -2.6410683948,
   1.1658091033,
   2.3607999565,
   -0.4265055896,
   -0.4862102299,
   0.5102253659,
   -0.3384745171,
   -0.4081285365,
   -0.199414607,
   0.0151691668,
   0.2644673476,
   -0.0483547565,
   0.9869714364,
   0.629627219,
   0.8990505678,
   -0.3731273846,
   -0.2166148809,
   0.6374669208],
  'metadata.dropped': [],
  'metadata.elapsed': [4976659, 5674079],
  'metadata.last_model': '{"model_name":"api-sample-model","model_sha":"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507"}',
  'metadata.partition': 'engine-5d965d96f4-wxk8l',
  'metadata.pipeline_version': '800fa3cd-607f-4550-90ec-227d262f8a45',
  'out.dense_1': [0.0011070371],
  'time': 1722279178343},
 {'anomaly.count': 0,
  'in.dense_input': [0.4951101913,
   -0.2499369449,
   0.4553345161,
   0.9242750451,
   -0.3643510229,
   0.602688482,
   -0.3785553207,
   0.3170957153,
   0.7368986387,
   -0.1195106678,
   0.4017042912,
   0.7371143425,
   -1.2229791154,
   0.0061993212,
   -1.3541149574,
   -0.5839052891,
   0.1648461272,
   -0.1527212037,
   0.2456232399,
   -0.1432012313,
   -0.0383696111,
   0.0865420131,
   -0.284099885,
   -0.5027591867,
   1.1117147574,
   -0.5666540195,
   0.121220185,
   0.0667640208,
   0.6583281816],
  'metadata.dropped': [],
  'metadata.elapsed': [4976659, 5674079],
  'metadata.last_model': '{"model_name":"api-sample-model","model_sha":"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507"}',
  'metadata.partition': 'engine-5d965d96f4-wxk8l',
  'metadata.pipeline_version': '800fa3cd-607f-4550-90ec-227d262f8a45',
  'out.dense_1': [0.0008533001],
  'time': 1722279178343}]

Retrieve assay baseline via curl.

data = {
    'workspace_id': workspace_id,
    'pipeline_name': pipeline_name,
    'limit': 3
}

!curl {wl.api_endpoint}/v1/api/assays/get_baseline \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{json.dumps(data)}' > output.txt

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  777k  100  777k  100    83   844k     90 --:--:-- --:--:-- --:--:--  844k

Run Assay Interactively

• Endpoint: /v1/api/assays/run_interactive

A list of assays with the following fields.

Run Assay Interactively Parameters

Run Assay Interactively Returns

Run Assay Interactively Returns

An interactive assay is requested based on the previous assays created.

Retrieve interactive assay via Requests.

## Run interactive assay

import datetime

assay_window_end = datetime.datetime.now(datetime.timezone.utc)

# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/assays/run_interactive"

data = {
    "name": exampleAssayName,
    "id": assay_id,
    "pipeline_id": pipeline.id(),
    "pipeline_name": pipeline_name,
    "active": True,
    "status": "created",
    "baseline": {
        "static": baseline_summary
    },
    "window": {
        "pipeline_name": pipeline_name,
        "model_name": example_model_name,
        "width": "1 minutes",
        "start": assay_start.isoformat(),
        "interval": "1 minutes",
        "path": "output dense_1 0",
        "workspace_id": workspace_id,
        "locations": []
    },
    "summarizer": {
        "type": "UnivariateContinuous",
        "bin_mode": "Quantile",
        "aggregation": "Density",
        "metric": "PSI",
        "num_bins": 5,
        "bin_weights": None,
        "bin_width": None,
        "provided_edges": None,
        "add_outlier_edges": True
    },
    "warning_threshold": None,
    "alert_threshold": 0.25,
    "run_until": assay_window_end.isoformat(),
    "workspace_id": workspace_id
}

# print(data)

response = requests.post(endpoint, json=data, headers=headers, verify=True)
# print(len(response.json))
print(response.json())

[{'id': None, 'assay_id': 4, 'window_start': '2024-07-29T19:48:46.944191Z', 'analyzed_at': '2024-07-29T19:54:43.286044715Z', 'elapsed_millis': 18, 'pipeline_id': None, 'workspace_id': None, 'workspace_name': None, 'baseline_summary': {'count': 1001, 'min': 1.5199184e-06, 'max': 1.0, 'mean': 0.006598251655689411, 'median': 0.00054195523, 'std': 0.07661715949339488, 'edges': [1.5199184e-06, 0.00026360154, 0.00045093894, 0.0006800592, 0.00096952915, 1.0, None], 'edge_names': ['left_outlier', 'q_20', 'q_40', 'q_60', 'q_80', 'q_100', 'right_outlier'], 'aggregated_values': [0.0, 0.1998001998001998, 0.1998001998001998, 0.2007992007992008, 0.1998001998001998, 0.1998001998001998, 0.0], 'aggregation': 'Density', 'start': None, 'end': None}, 'window_summary': {'count': 1001, 'min': 1.519918441772461e-06, 'max': 1.0, 'mean': 0.0065982516233499475, 'median': 0.0005419552326202393, 'std': 0.07661715908141875, 'edges': [1.5199184e-06, 0.00026360154, 0.00045093894, 0.0006800592, 0.00096952915, 1.0, None], 'edge_names': ['left_outlier', 'e_2.64e-4', 'e_4.51e-4', 'e_6.80e-4', 'e_9.70e-4', 'e_1.00e0', 'right_outlier'], 'aggregated_values': [0.0, 0.1988011988011988, 0.1998001998001998, 0.2017982017982018, 0.1988011988011988, 0.2007992007992008, 0.0], 'aggregation': 'Density', 'start': '2024-07-29T19:48:46.944191Z', 'end': '2024-07-29T19:49:46.944191Z'}, 'warning_threshold': None, 'alert_threshold': 0.25, 'score': 1.995545904121508e-05, 'scores': [0.0, 5.007534289255001e-06, 0.0, 4.957831510618228e-06, 5.007534289255001e-06, 4.982558952086851e-06, 0.0], 'bin_index': None, 'summarizer': {'type': 'UnivariateContinuous', 'bin_mode': 'Quantile', 'aggregation': 'Density', 'metric': 'PSI', 'num_bins': 5, 'bin_weights': None, 'provided_edges': None}, 'status': 'Ok', 'created_at': None}, {'id': None, 'assay_id': 4, 'window_start': '2024-07-29T19:49:46.944191Z', 'analyzed_at': '2024-07-29T19:54:43.286185134Z', 'elapsed_millis': 27, 'pipeline_id': None, 'workspace_id': None, 'workspace_name': None, 'baseline_summary': {'count': 1001, 'min': 1.5199184e-06, 'max': 1.0, 'mean': 0.006598251655689411, 'median': 0.00054195523, 'std': 0.07661715949339488, 'edges': [1.5199184e-06, 0.00026360154, 0.00045093894, 0.0006800592, 0.00096952915, 1.0, None], 'edge_names': ['left_outlier', 'q_20', 'q_40', 'q_60', 'q_80', 'q_100', 'right_outlier'], 'aggregated_values': [0.0, 0.1998001998001998, 0.1998001998001998, 0.2007992007992008, 0.1998001998001998, 0.1998001998001998, 0.0], 'aggregation': 'Density', 'start': None, 'end': None}, 'window_summary': {'count': 1001, 'min': 1.519918441772461e-06, 'max': 1.0, 'mean': 0.0065982516233499475, 'median': 0.0005419552326202393, 'std': 0.07661715908141875, 'edges': [1.5199184e-06, 0.00026360154, 0.00045093894, 0.0006800592, 0.00096952915, 1.0, None], 'edge_names': ['left_outlier', 'e_2.64e-4', 'e_4.51e-4', 'e_6.80e-4', 'e_9.70e-4', 'e_1.00e0', 'right_outlier'], 'aggregated_values': [0.0, 0.1988011988011988, 0.1998001998001998, 0.2017982017982018, 0.1988011988011988, 0.2007992007992008, 0.0], 'aggregation': 'Density', 'start': '2024-07-29T19:49:46.944191Z', 'end': '2024-07-29T19:50:46.944191Z'}, 'warning_threshold': None, 'alert_threshold': 0.25, 'score': 1.995545904121508e-05, 'scores': [0.0, 5.007534289255001e-06, 0.0, 4.957831510618228e-06, 5.007534289255001e-06, 4.982558952086851e-06, 0.0], 'bin_index': None, 'summarizer': {'type': 'UnivariateContinuous', 'bin_mode': 'Quantile', 'aggregation': 'Density', 'metric': 'PSI', 'num_bins': 5, 'bin_weights': None, 'provided_edges': None}, 'status': 'Ok', 'created_at': None}]

Retrieve interactive assay via curl.

data = {
    "name": exampleAssayName,
    "id": assay_id,
    "pipeline_id": pipeline.id(),
    "pipeline_name": pipeline_name,
    "active": True,
    "status": "created",
    "baseline": {
        "static": baseline_summary
    },
    "window": {
        "pipeline_name": pipeline_name,
        "model_name": example_model_name,
        "width": "1 minutes",
        "start": assay_start.isoformat(),
        "interval": "1 minutes",
        "path": "output dense_1 0",
        "workspace_id": workspace_id,
        "locations": []
    },
    "summarizer": {
        "type": "UnivariateContinuous",
        "bin_mode": "Quantile",
        "aggregation": "Density",
        "metric": "PSI",
        "num_bins": 5,
        "bin_weights": None,
        "bin_width": None,
        "provided_edges": None,
        "add_outlier_edges": True
    },
    "warning_threshold": None,
    "alert_threshold": 0.25,
    "run_until": assay_window_end.isoformat(),
    "workspace_id": workspace_id
}

!curl {wl.api_endpoint}/v1/api/assays/run_interactive \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{json.dumps(data)}'

[{"id":null,"assay_id":4,"window_start":"2024-07-29T19:48:46.944191Z","analyzed_at":"2024-07-29T19:54:57.167038277Z","elapsed_millis":26,"pipeline_id":null,"workspace_id":null,"workspace_name":null,"baseline_summary":{"count":1001,"min":1.5199184e-6,"max":1.0,"mean":0.006598251655689411,"median":0.00054195523,"std":0.07661715949339488,"edges":[1.5199184e-6,0.00026360154,0.00045093894,0.0006800592,0.00096952915,1.0,null],"edge_names":["left_outlier","q_20","q_40","q_60","q_80","q_100","right_outlier"],"aggregated_values":[0.0,0.1998001998001998,0.1998001998001998,0.2007992007992008,0.1998001998001998,0.1998001998001998,0.0],"aggregation":"Density","start":null,"end":null},"window_summary":{"count":1001,"min":1.519918441772461e-6,"max":1.0,"mean":0.0065982516233499475,"median":0.0005419552326202393,"std":0.07661715908141875,"edges":[1.5199184e-6,0.00026360154,0.00045093894,0.0006800592,0.00096952915,1.0,null],"edge_names":["left_outlier","e_2.64e-4","e_4.51e-4","e_6.80e-4","e_9.70e-4","e_1.00e0","right_outlier"],"aggregated_values":[0.0,0.1988011988011988,0.1998001998001998,0.2017982017982018,0.1988011988011988,0.2007992007992008,0.0],"aggregation":"Density","start":"2024-07-29T19:48:46.944191Z","end":"2024-07-29T19:49:46.944191Z"},"warning_threshold":null,"alert_threshold":0.25,"score":0.00001995545904121508,"scores":[0.0,5.007534289255001e-6,0.0,4.957831510618228e-6,5.007534289255001e-6,4.982558952086851e-6,0.0],"bin_index":null,"summarizer":{"type":"UnivariateContinuous","bin_mode":"Quantile","aggregation":"Density","metric":"PSI","num_bins":5,"bin_weights":null,"provided_edges":null},"status":"Ok","created_at":null},{"id":null,"assay_id":4,"window_start":"2024-07-29T19:49:46.944191Z","analyzed_at":"2024-07-29T19:54:57.167203127Z","elapsed_millis":31,"pipeline_id":null,"workspace_id":null,"workspace_name":null,"baseline_summary":{"count":1001,"min":1.5199184e-6,"max":1.0,"mean":0.006598251655689411,"median":0.00054195523,"std":0.07661715949339488,"edges":[1.5199184e-6,0.00026360154,0.00045093894,0.0006800592,0.00096952915,1.0,null],"edge_names":["left_outlier","q_20","q_40","q_60","q_80","q_100","right_outlier"],"aggregated_values":[0.0,0.1998001998001998,0.1998001998001998,0.2007992007992008,0.1998001998001998,0.1998001998001998,0.0],"aggregation":"Density","start":null,"end":null},"window_summary":{"count":1001,"min":1.519918441772461e-6,"max":1.0,"mean":0.0065982516233499475,"median":0.0005419552326202393,"std":0.07661715908141875,"edges":[1.5199184e-6,0.00026360154,0.00045093894,0.0006800592,0.00096952915,1.0,null],"edge_names":["left_outlier","e_2.64e-4","e_4.51e-4","e_6.80e-4","e_9.70e-4","e_1.00e0","right_outlier"],"aggregated_values":[0.0,0.1988011988011988,0.1998001998001998,0.2017982017982018,0.1988011988011988,0.2007992007992008,0.0],"aggregation":"Density","start":"2024-07-29T19:49:46.944191Z","end":"2024-07-29T19:50:46.944191Z"},"warning_threshold":null,"alert_threshold":0.25,"score":0.00001995545904121508,"scores":[0.0,5.007534289255001e-6,0.0,4.957831510618228e-6,5.007534289255001e-6,4.982558952086851e-6,0.0],"bin_index":null,"summarizer":{"type":"UnivariateContinuous","bin_mode":"Quantile","aggregation":"Density","metric":"PSI","num_bins":5,"bin_weights":null,"provided_edges":null},"status":"Ok","created_at":null}]

Get Assay Results

• Endpoint*: /v1/api/assays/get_results

Retrieve the results for an assay.

Get Assay Results Parameters

Get Assay Results Returns

Get Assay Results Examples

Retrieve the results from the assay created earlier.

Retrieve assay results via Requests.

# Get Assay Results

# Retrieve the token 
headers = wl.auth.auth_header()

assay_window_end = datetime.datetime.now(datetime.timezone.utc)

# print(f'{assay_window_end.isoformat()}+00:00')

endpoint = f"{wl.api_endpoint}/v1/api/assays/get_assay_results"

data = {
    'assay_id': assay_id,
    'start': assay_start.isoformat(),
    'end': assay_window_end.isoformat()
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
response

[{'id': 4,
  'assay_id': 4,
  'window_start': '2024-07-29T19:48:46.944191Z',
  'analyzed_at': '2024-07-29T19:50:07.144395Z',
  'elapsed_millis': 9,
  'pipeline_id': 1,
  'workspace_id': 6,
  'workspace_name': 'sample-api-workspace-assays',
  'baseline_summary': {'count': 1001,
   'min': 1.5199184e-06,
   'max': 1.0,
   'mean': 0.006598251655689411,
   'median': 0.00054195523,
   'std': 0.07661715949339488,
   'edges': [1.5199184e-06,
    0.00026360154,
    0.00045093894,
    0.0006800592,
    0.00096952915,
    1.0,
    None],
   'edge_names': ['left_outlier',
    'q_20',
    'q_40',
    'q_60',
    'q_80',
    'q_100',
    'right_outlier'],
   'aggregated_values': [0.0,
    0.1998001998001998,
    0.1998001998001998,
    0.2007992007992008,
    0.1998001998001998,
    0.1998001998001998,
    0.0],
   'aggregation': 'Density',
   'start': None,
   'end': None},
  'window_summary': {'count': 1001,
   'min': 1.519918441772461e-06,
   'max': 1.0,
   'mean': 0.0065982516233499475,
   'median': 0.0005419552326202393,
   'std': 0.07661715908141875,
   'edges': [1.5199184e-06,
    0.00026360154,
    0.00045093894,
    0.0006800592,
    0.00096952915,
    1.0,
    None],
   'edge_names': ['left_outlier',
    'e_2.64e-4',
    'e_4.51e-4',
    'e_6.80e-4',
    'e_9.70e-4',
    'e_1.00e0',
    'right_outlier'],
   'aggregated_values': [0.0,
    0.1988011988011988,
    0.1998001998001998,
    0.2017982017982018,
    0.1988011988011988,
    0.2007992007992008,
    0.0],
   'aggregation': 'Density',
   'start': '2024-07-29T19:48:46.944191Z',
   'end': '2024-07-29T19:49:46.944191Z'},
  'warning_threshold': None,
  'alert_threshold': 0.25,
  'score': 1.995546e-05,
  'scores': [0.0,
   5.007534289255001e-06,
   0.0,
   4.957831510618228e-06,
   5.007534289255001e-06,
   4.982558952086851e-06,
   0.0],
  'bin_index': None,
  'summarizer': {'type': 'UnivariateContinuous',
   'bin_mode': 'Quantile',
   'aggregation': 'Density',
   'metric': 'PSI',
   'num_bins': 5,
   'bin_weights': None,
   'provided_edges': None},
  'status': 'Ok',
  'created_at': '2024-07-29T19:50:07.156553Z'},
 {'id': 5,
  'assay_id': 4,
  'window_start': '2024-07-29T19:49:46.944191Z',
  'analyzed_at': '2024-07-29T19:51:14.769817Z',
  'elapsed_millis': 11,
  'pipeline_id': 1,
  'workspace_id': 6,
  'workspace_name': 'sample-api-workspace-assays',
  'baseline_summary': {'count': 1001,
   'min': 1.5199184e-06,
   'max': 1.0,
   'mean': 0.006598251655689411,
   'median': 0.00054195523,
   'std': 0.07661715949339488,
   'edges': [1.5199184e-06,
    0.00026360154,
    0.00045093894,
    0.0006800592,
    0.00096952915,
    1.0,
    None],
   'edge_names': ['left_outlier',
    'q_20',
    'q_40',
    'q_60',
    'q_80',
    'q_100',
    'right_outlier'],
   'aggregated_values': [0.0,
    0.1998001998001998,
    0.1998001998001998,
    0.2007992007992008,
    0.1998001998001998,
    0.1998001998001998,
    0.0],
   'aggregation': 'Density',
   'start': None,
   'end': None},
  'window_summary': {'count': 1001,
   'min': 1.519918441772461e-06,
   'max': 1.0,
   'mean': 0.0065982516233499475,
   'median': 0.0005419552326202393,
   'std': 0.07661715908141875,
   'edges': [1.5199184e-06,
    0.00026360154,
    0.00045093894,
    0.0006800592,
    0.00096952915,
    1.0,
    None],
   'edge_names': ['left_outlier',
    'e_2.64e-4',
    'e_4.51e-4',
    'e_6.80e-4',
    'e_9.70e-4',
    'e_1.00e0',
    'right_outlier'],
   'aggregated_values': [0.0,
    0.1988011988011988,
    0.1998001998001998,
    0.2017982017982018,
    0.1988011988011988,
    0.2007992007992008,
    0.0],
   'aggregation': 'Density',
   'start': '2024-07-29T19:49:46.944191Z',
   'end': '2024-07-29T19:50:46.944191Z'},
  'warning_threshold': None,
  'alert_threshold': 0.25,
  'score': 1.995546e-05,
  'scores': [0.0,
   5.007534289255001e-06,
   0.0,
   4.957831510618228e-06,
   5.007534289255001e-06,
   4.982558952086851e-06,
   0.0],
  'bin_index': None,
  'summarizer': {'type': 'UnivariateContinuous',
   'bin_mode': 'Quantile',
   'aggregation': 'Density',
   'metric': 'PSI',
   'num_bins': 5,
   'bin_weights': None,
   'provided_edges': None},
  'status': 'Ok',
  'created_at': '2024-07-29T19:51:14.784044Z'}]

Retrieve assay results via curl.

data = {
    'assay_id': assay_id,
    'start': assay_start.isoformat(),
    'end': assay_window_end.isoformat()
}

!curl {wl.api_endpoint}/v1/api/assays/get_assay_results \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{json.dumps(data)}'

[{"id":4,"assay_id":4,"window_start":"2024-07-29T19:48:46.944191Z","analyzed_at":"2024-07-29T19:50:07.144395Z","elapsed_millis":9,"pipeline_id":1,"workspace_id":6,"workspace_name":"sample-api-workspace-assays","baseline_summary":{"count":1001,"min":1.5199184e-6,"max":1.0,"mean":0.006598251655689411,"median":0.00054195523,"std":0.07661715949339488,"edges":[1.5199184e-6,0.00026360154,0.00045093894,0.0006800592,0.00096952915,1.0,null],"edge_names":["left_outlier","q_20","q_40","q_60","q_80","q_100","right_outlier"],"aggregated_values":[0.0,0.1998001998001998,0.1998001998001998,0.2007992007992008,0.1998001998001998,0.1998001998001998,0.0],"aggregation":"Density","start":null,"end":null},"window_summary":{"count":1001,"min":1.519918441772461e-6,"max":1.0,"mean":0.0065982516233499475,"median":0.0005419552326202393,"std":0.07661715908141875,"edges":[1.5199184e-6,0.00026360154,0.00045093894,0.0006800592,0.00096952915,1.0,null],"edge_names":["left_outlier","e_2.64e-4","e_4.51e-4","e_6.80e-4","e_9.70e-4","e_1.00e0","right_outlier"],"aggregated_values":[0.0,0.1988011988011988,0.1998001998001998,0.2017982017982018,0.1988011988011988,0.2007992007992008,0.0],"aggregation":"Density","start":"2024-07-29T19:48:46.944191Z","end":"2024-07-29T19:49:46.944191Z"},"warning_threshold":null,"alert_threshold":0.25,"score":0.00001995546,"scores":[0.0,5.007534289255001e-6,0.0,4.957831510618228e-6,5.007534289255001e-6,4.982558952086851e-6,0.0],"bin_index":null,"summarizer":{"type":"UnivariateContinuous","bin_mode":"Quantile","aggregation":"Density","metric":"PSI","num_bins":5,"bin_weights":null,"provided_edges":null},"status":"Ok","created_at":"2024-07-29T19:50:07.156553Z"},{"id":5,"assay_id":4,"window_start":"2024-07-29T19:49:46.944191Z","analyzed_at":"2024-07-29T19:51:14.769817Z","elapsed_millis":11,"pipeline_id":1,"workspace_id":6,"workspace_name":"sample-api-workspace-assays","baseline_summary":{"count":1001,"min":1.5199184e-6,"max":1.0,"mean":0.006598251655689411,"median":0.00054195523,"std":0.07661715949339488,"edges":[1.5199184e-6,0.00026360154,0.00045093894,0.0006800592,0.00096952915,1.0,null],"edge_names":["left_outlier","q_20","q_40","q_60","q_80","q_100","right_outlier"],"aggregated_values":[0.0,0.1998001998001998,0.1998001998001998,0.2007992007992008,0.1998001998001998,0.1998001998001998,0.0],"aggregation":"Density","start":null,"end":null},"window_summary":{"count":1001,"min":1.519918441772461e-6,"max":1.0,"mean":0.0065982516233499475,"median":0.0005419552326202393,"std":0.07661715908141875,"edges":[1.5199184e-6,0.00026360154,0.00045093894,0.0006800592,0.00096952915,1.0,null],"edge_names":["left_outlier","e_2.64e-4","e_4.51e-4","e_6.80e-4","e_9.70e-4","e_1.00e0","right_outlier"],"aggregated_values":[0.0,0.1988011988011988,0.1998001998001998,0.2017982017982018,0.1988011988011988,0.2007992007992008,0.0],"aggregation":"Density","start":"2024-07-29T19:49:46.944191Z","end":"2024-07-29T19:50:46.944191Z"},"warning_threshold":null,"alert_threshold":0.25,"score":0.00001995546,"scores":[0.0,5.007534289255001e-6,0.0,4.957831510618228e-6,5.007534289255001e-6,4.982558952086851e-6,0.0],"bin_index":null,"summarizer":{"type":"UnivariateContinuous","bin_mode":"Quantile","aggregation":"Density","metric":"PSI","num_bins":5,"bin_weights":null,"provided_edges":null},"status":"Ok","created_at":"2024-07-29T19:51:14.784044Z"}]

# undeploy the pipeline and deactivate assay

pipeline.undeploy()

data = {
    'id': assay_id,
    'active': False
}

!curl {wl.api_endpoint}/v1/api/assays/set_active \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{json.dumps(data)}'

{"id":4,"active":false}

# undeploy the pipeline and deactivate assay generated from curl

pipeline.undeploy()

data = {
    'id': 3,
    'active': False
}

!curl {wl.api_endpoint}/v1/api/assays/set_active \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{json.dumps(data)}'

{"id":3,"active":false}