Wallaroo MLOps API Essentials Guide: Assays V1 Management

How to use the Wallaroo API for Assays V1 Management

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.

IMPORTANT NOTE

Wallaroo provides two versions of Wallaroo Assays: Assays V1 and Assays V2. New installations of Wallaroo 2024.4 default to Assays V2.

See Configuring Assay Version for details on setting the SDK to match the Wallaroo Assay version. Note that Wallaroo Assays V1 are enabled by default and require no changes.

The Wallaroo MLOps API is only available for Wallaroo Assays V1 commands. For Wallaroo Assays V2, use the Wallaroo SDK.

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

Wallaroo MLOps API Assay V1 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.

Import Libraries

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

Field	Type	Description
features	String	Enabled features.
name	String	Name of the Wallaroo instance.
is_auth_enabled	Boolean	Whether authentication is enabled.

# 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

IMPORTANT NOTE

Wallaroo provides two versions of Wallaroo Assays: Assays V1 and Assays V2. New installations of Wallaroo 2024.4 default to Assays V2.

See Configuring Assay Version for details on setting the SDK to match the Wallaroo Assay version. Note that Wallaroo Assays V1 are enabled by default and require no changes.

The Wallaroo MLOps API is only available for Wallaroo Assays V1 commands. For Wallaroo Assays V2, use the Wallaroo SDK.

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

Field			Type	Description
baseline			Baseline (Required)	The baseline for the assay consisting of the following options. Options are `vector` and `fixed_window`.
	fixed_window		(AssayFixConfiguration) (Required)	The fixed configuration for the assay window baseline with the set baseline start and end dates.
		interval	String (Required)	The interval of data to gather for the baseline in the format `{Integer} {Unit`}, with `Unit` being `minutes`, `hours`, and `days`.
		model_name	String (Required)	The name of the model to track inference data for the baseline from.
		path	String (Required)	The input/output path in the format `{input
		pipeline_name	String (Required)	The name of the pipeline with the baseline data.
		start	String (Optional)	The DateTime of the baseline start date.
		width	String (Required)	The amount of data from inference results to gather in the format `{Integer} {Unit`}, with `Unit` being `minutes`, `hours`, and `days`.
		workspace_id	Integer (Required)	The numerical identifier of the workspace.
		workspace_id	List[String] (Optional)	A list of locations, including edge locations. An empty list `[]` defaults to the Wallaroo Ops pipeline location.
	vector		List[Float]	A numpy array of values for the baseline.
summarizer			(AssaySummerizer) (REQUIRED)	The summarizer type for the array aka “advanced settings” in the Wallaroo Dashboard UI.
	type		String (Required)	Type of summarizer.
		bin_mode	String (Required)	The binning model type. Values are: `Quantile` and `Equal`
		aggregation	String (Required)	Aggregation type. Values are: `Cumulative`, `Density`, and `Edges`.
		metric	String (Required)	Metric type. Values are: `PSI`, `MaxDiff` (Maximum Difference of Bins), and `SumDiff` (Sum of the Difference of Bins)
		num_bins	*Integer (Required)	The number of bins. Recommended values are between 5 and 14.
		type	String (Required)	Bin type. Values are: `UnivariateContinuous`
		bin_weights	List[float] (Optional*)	The weights assigned to the assay bins.
		bin_width	List[float] (Optional)	The width assigned to the assay bins.
		provided_edges	List[float] (Optional)	The edges used for the assay bins.

Create Baseline Summary Statistics Returns

Field	Type	Description
count	Integer	The number of inference results collected.
min	Float	The minimum values from the baseline.
max	Float	The maximum values from the baseline.
mean	Float	The mean values from the baseline.
median	Float	The median values from the baseline.
std	Float	The standard deviation score.
edges	List[Float]	The defined bin edges.
edges	List[String]	The bin names.
aggregated_values	List[Float]	The aggregated values of the baseline.
aggregation	String	Aggregation type. Values are: `Cumulative`, `Density`, and `Edges`.
start	String	The datetime stamp of when the baseline data started.
end	String	The datetime stamp of when the baseline data started.

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

	time	in.dense_input	out.dense_1	anomaly.count
0	2024-07-29 19:48:49.235	[-1.0603297501, 2.3544967095, -3.5638788326, 5...	[0.99300325]	0
1	2024-07-29 19:48:49.235	[-1.0603297501, 2.3544967095, -3.5638788326, 5...	[0.99300325]	0
2	2024-07-29 19:48:49.235	[-1.0603297501, 2.3544967095, -3.5638788326, 5...	[0.99300325]	0
3	2024-07-29 19:48:49.235	[-1.0603297501, 2.3544967095, -3.5638788326, 5...	[0.99300325]	0
4	2024-07-29 19:48:49.235	[0.5817662108, 0.097881551, 0.1546819424, 0.47...	[0.0010916889]	0
...	...	...	...	...
996	2024-07-29 19:48:49.235	[1.052355506, -0.7602601059, -0.3124601687, -0...	[0.00011596084]	0
997	2024-07-29 19:48:49.235	[-0.8464537996, -0.7608807925, 2.186072883, -0...	[0.0002785325]	0
998	2024-07-29 19:48:49.235	[1.0046377125, 0.0343666504, -1.3512533246, 0....	[0.0011070371]	0
999	2024-07-29 19:48:49.235	[0.4951101913, -0.2499369449, 0.4553345161, 0....	[0.0008533001]	0
1000	2024-07-29 19:48:49.235	[0.6118805301, 0.1726081102, 0.4310545502, 0.5...	[0.0012498498]	0

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

Field			Type	Description
id			Integer (Optional)	The numerical identifier for the assay.
name			String (Required)	The name of the assay.
pipeline_id			String (Required)	The numerical identifier the assay will be placed into.
pipeline_name			String (Required)	The name of the pipeline
active			Boolean (Required)	Indicates whether the assay will be active upon creation or not.
status			String (Required)	The status of the assay upon creation. Options are: `active`: the Assay is created and active. `created`: The assay is created but inactive.
iopath			String (Required)	The iopath of the assay in the format `“input
baseline			Baseline (Required)	The baseline for the assay consisting of the following options. Options are `fixed`, `calculated` and `static`.
	fixed		(AssayFixConfiguration) (Required)	The fixed configuration for the assay with the set baseline start and end dates.
		pipeline	String (Required)	The name of the pipeline with the baseline data.
		model	String (Required)	The name of the model used.
		start_at	String (Required)	The DateTime of the baseline start date.
		end_at	String (Required)	The DateTime of the baseline end date.
	calculated		(AssaysCreateJsonBodyBaselineType0CalculatedType0) (Required)	The calculated baseline submitted with numpy arrays. Declared with `calculated`. This includes the following fields.
		vector	(List[float]) (Required)	The numpy array of baseline values.
	static		AssaysCreateJsonBodyBaselineType1Static (Required)	The static baseline. These values are generated from the `fixed` and `calculated` baseline methods listed above, and can be used to achieve more granularity in the assay baseline creation.
		count	Integer (Required)	The number values used.
		min	Float (Required)	The minimum value of baseline values.
		max	Float (Required)	The maximum value of baseline values.
		mean	Float (Required)	The mean of the of baseline values.
		std	Float (Required)	The standard deviation of baseline values.
		edges	List[Float] (Required)	The edges defined from the baseline values or manually defined.
		aggregated_values	List[Float] (Required)	The aggregated values of each edge.
		aggregation	String (Required)	The type of baseline aggregation. Values are: `Cumulative`, `Density`, and `Edges`.
		start	String (Optional)	The DateTime of the baseline inference start date.
		end	String (Optional) The DateTime of the baseline inference end date.
window			AssayWindow (Required)	Assay window.
	pipeline		String (Required)	The name of the pipeline for the assay window.
	model		String (Required)	The name of the model used for the assay window.
	width		String (Required)	The width of the assay window.
	start		String (Required)	The DateTime of when to start the assay window.
	interval		String (Required)	The assay window interval.
	locations		List[String] (Required)	The locations included in the assay. If set to an empty set `[]` the Wallaroo Ops pipeline will be the only location used.
summarizer			(AssaySummerizer) (REQUIRED)	The summarizer type for the array aka “advanced settings” in the Wallaroo Dashboard UI.
	type		String (Required)	Type of summarizer.
		bin_mode	String (Required)	The binning model type. Values are: `Quantile` and `Equal`
		aggregation	String (Required)	Aggregation type. Values are: `Cumulative`, `Density`, and `Edges`.
		metric	String (Required)	Metric type. Values are: `PSI`, `MaxDiff` (Maximum Difference of Bins), and `SumDiff` (Sum of the Difference of Bins)
		num_bins	*Integer (Required)	The number of bins. Recommended values are between 5 and 14.
		type	String (Required)	Bin type. Values are: `UnivariateContinuous`
		bin_weights	List[float] (Optional*)	The weights assigned to the assay bins.
		bin_width	List[float] (Optional)	The width assigned to the assay bins.
		provided_edges	List[float] (Optional)	The edges used for the assay bins.
		add_outlier_edges	Boolean (Required)	Indicates whether to add outlier edges or not.
warning_threshold			Float (Required)	Optional warning threshold.
alert_threshold			Float (Required)	Alert threshold.
run_until			String (OPTIONAL)	DateTime of when to end the assay.
workspace_id			Integer (Required)	The workspace the assay is part of.
model_insights_url			String (OPTIONAL)	URL for model insights.

Create Assay Returns

Field	Type	Description
assay_id	Integer	The id of the new assay.

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

Field	Type	Description
pipeline_id	Integer (Required)	The numerical ID of the pipeline.

List Assays Returns

A list of assays with the following fields.

Field			Type	Description
id			Integer	The numerical identifier for the assay.
name			String	The name of the assay.
active			Boolean	Whether the assay is Active or not.
status			String	The status of the assay. Options are: `active`: the Assay is created and active. `created`: The assay is created but inactive.
warning_threshold			Float (Required)	Optional warning threshold.
alert_threshold			Float (Required)	Alert threshold.
pipeline_id			String	The numerical identifier the assay will be placed into.
pipeline_name			String	The name of the pipeline
last_run			String	The DateTime for the last time the assay ran.
next_run			String	The DateTime for the next schedule assay run.
run_until			String	The DateTime when the assay will stop running.
updated_at			String	The DateTime when the assay was last updated.
iopath			String	The iopath of the assay in the format `“input
baseline			Baseline	The baseline for the assay. This is determined by how the assay was generated. See Create Assay Parameters for complete values.
window			AssayWindow	Assay window.
	model_name		String	The name of the model used for the assay window.
	pipeline_name		String	The name of the pipeline for the assay window.
	path		String	The iopath of the assay window.
	start		String	The DateTime of when to start the assay window.
	width		String	The width of the assay window.
	workspace_id		String	The width of the assay window.
	locations		List[String]	The locations included in the assay. An empty set indicates only the Wallaroo Ops deployment of the pipeline is used.
summarizer			(AssaySummerizer)	The summarizer type for the array aka “advanced settings” in the Wallaroo Dashboard UI.
	type		String	Type of summarizer.
		bin_mode	String	The binning model type. Values are: `Quantile` and `Equal`
		aggregation	String	Aggregation type. Values are: `Cumulative`, `Density`, and `Edges`.
		metric	String	Metric type. Values are: `PSI`, `MaxDiff` (Maximum Difference of Bins), and `SumDiff` (Sum of the Difference of Bins)
		num_bins	*Integer	The number of bins. Recommended values are between 5 and 14.
		bin_weights	*List[float]	The weights assigned to the assay bins.
		provided_edges	List[float]	The edges used for the assay bins.

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\": 0}","warning_threshold":null,"alert_threshold":0.25,"pipeline_id":1,"pipeline_name":"sample-api-pipeline-with-models","last_run":"2024-07-29T18:55:14.728247+00:00","next_run":"2024-07-29T18:54:42.973927+00:00","run_until":"2024-07-29T18:52:58.794037+00:00","updated_at":"2024-07-29T18:53:36.003392+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":"2024-07-29T18:52:42.973927Z","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":2,"name":"api assay example aqsf","active":false,"status":"{\"run_at\": \"2024-07-29T18:55:14.709022713+00:00\",  \"num_ok\": 0, \"num_warnings\": 0, \"num_alerts\": 0}","warning_threshold":null,"alert_threshold":0.25,"pipeline_id":1,"pipeline_name":"sample-api-pipeline-with-models","last_run":"2024-07-29T18:55:14.709023+00:00","next_run":"2024-07-29T18:54:42.973927+00:00","run_until":"2024-07-29T18:52:58.794037+00:00","updated_at":"2024-07-29T18:52:59.010348+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":"2024-07-29T18:52:42.973927Z","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":1,"name":"api assay example kmlr","active":false,"status":"created","warning_threshold":null,"alert_threshold":0.25,"pipeline_id":1,"pipeline_name":"sample-api-pipeline-with-models","last_run":null,"next_run":"2024-07-29T18:51:10.243304+00:00","run_until":"2024-07-29T18:51:10.026613+00:00","updated_at":"2024-07-29T18:51:10.245675+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":"2024-07-29T18:50:42.582024Z","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"}]

Activate or Deactivate Assay

Endpoint: /v1/api/assays/set_active

Activates or deactivates an existing assay.

Activate or Deactivate Assay Parameters

Field	Type	Description
id	Integer (Required)	The id of the new assay.
active	Boolean (Required)	True to activate the assay, False to deactivate it.

Activate or Deactivate Assay Returns

Field	Type	Description
id		The numerical id of the assay.
active	Boolean	True to activate the assay, False to deactivate it.

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

Field	Type	Description
workspace_id	Integer (Required)	Numerical id for the workspace the assay is in.
pipeline_name	String (Required)	Name of the pipeline the assay is in.
start	String (Optional)	DateTime for when the baseline starts.
end	String (Optional)	DateTime for when the baseline ends.
model_name	String (Optional)	Name of the model.
limit	Integer (Optional)	Maximum number of baselines to return.

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

[{'anomaly.count': 0,
  'in.dense_input': [-1.0603297501,
   2.3544967095,
   -3.5638788326,
   5.1387348926,
   -1.2308457019,
   -0.7687824608,
   -3.5881228109,
   1.8880837663,
   -3.2789674274,
   -3.9563254554,
   4.0993439118,
   -5.6539176395,
   -0.8775733373,
   -9.131571192,
   -0.6093537873,
   -3.7480276773,
   -5.0309125017,
   -0.8748149526,
   1.9870535692,
   0.7005485718,
   0.9204422758,
   -0.1041491809,
   0.3229564351,
   -0.7418141657,
   0.0384120159,
   1.0993439146,
   1.2603409756,
   -0.1466244739,
   -1.4463212439],
  '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': [0.99300325],
  'time': 1722279178343},
 {'anomaly.count': 0,
  'in.dense_input': [-1.0603297501,
   2.3544967095,
   -3.5638788326,
   5.1387348926,
   -1.2308457019,
   -0.7687824608,
   -3.5881228109,
   1.8880837663,
   -3.2789674274,
   -3.9563254554,
   4.0993439118,
   -5.6539176395,
   -0.8775733373,
   -9.131571192,
   -0.6093537873,
   -3.7480276773,
   -5.0309125017,
   -0.8748149526,
   1.9870535692,
   0.7005485718,
   0.9204422758,
   -0.1041491809,
   0.3229564351,
   -0.7418141657,
   0.0384120159,
   1.0993439146,
   1.2603409756,
   -0.1466244739,
   -1.4463212439],
  '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': [0.99300325],
  'time': 1722279178343},
 {'anomaly.count': 0,
  'in.dense_input': [-1.0603297501,
   2.3544967095,
   -3.5638788326,
   5.1387348926,
   -1.2308457019,
   -0.7687824608,
   -3.5881228109,
   1.8880837663,
   -3.2789674274,
   -3.9563254554,
   4.0993439118,
   -5.6539176395,
   -0.8775733373,
   -9.131571192,
   -0.6093537873,
   -3.7480276773,
   -5.0309125017,
   -0.8748149526,
   1.9870535692,
   0.7005485718,
   0.9204422758,
   -0.1041491809,
   0.3229564351,
   -0.7418141657,
   0.0384120159,
   1.0993439146,
   1.2603409756,
   -0.1466244739,
   -1.4463212439],
  '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': [0.99300325],
  'time': 1722279178343},
 {'anomaly.count': 0,
  'in.dense_input': [-1.0603297501,
   2.3544967095,
   -3.5638788326,
   5.1387348926,
   -1.2308457019,
   -0.7687824608,
   -3.5881228109,
   1.8880837663,
   -3.2789674274,
   -3.9563254554,
   4.0993439118,
   -5.6539176395,
   -0.8775733373,
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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

Field			Type	Description
id			Integer	The numerical identifier for the assay.
name			String	The name of the assay.
active			Boolean	Whether the assay is Active or not.
status			String	The status of the assay. Options are: `active`: the Assay is created and active. `created`: The assay is created but inactive.
warning_threshold			Float (Required)	Optional warning threshold.
alert_threshold			Float (Required)	Alert threshold.
pipeline_id			String	The numerical identifier the assay will be placed into.
pipeline_name			String	The name of the pipeline
last_run			String	The DateTime for the last time the assay ran.
next_run			String	The DateTime for the next schedule assay run.
run_until			String	The DateTime when the assay will stop running.
updated_at			String	The DateTime when the assay was last updated.
iopath			String	The iopath of the assay in the format `“input
baseline			Baseline	The baseline for the assay. This is determined by how the assay was generated. See Create Assay Parameters for complete values.
window			AssayWindow	Assay window.
	model_name		String	The name of the model used for the assay window.
	pipeline_name		String	The name of the pipeline for the assay window.
	path		String	The iopath of the assay window.
	start		String	The DateTime of when to start the assay window.
	width		String	The width of the assay window.
	workspace_id		String	The width of the assay window.
	locations		List[String]	The locations included in the assay. An empty set indicates only the Wallaroo Ops deployment of the pipeline is used.
summarizer			(AssaySummerizer)	The summarizer type for the array aka “advanced settings” in the Wallaroo Dashboard UI.
	type		String	Type of summarizer.
		bin_mode	String	The binning model type. Values are: `Quantile` and `Equal`
		aggregation	String	Aggregation type. Values are: `Cumulative`, `Density`, and `Edges`.
		metric	String	Metric type. Values are: `PSI`, `MaxDiff` (Maximum Difference of Bins), and `SumDiff` (Sum of the Difference of Bins)
		num_bins	*Integer	The number of bins. Recommended values are between 5 and 14.
		bin_weights	*List[float]	The weights assigned to the assay bins.
		provided_edges	List[float]	The edges used for the assay bins.

Run Assay Interactively Returns

Field	Type	Description
name	String	The name of the assay.
pipeline_id	Integer	The numerical identifier of the assay.
pipeline_name	String	The name of the pipeline.
active	Boolean	Whether the assay is Active or not.
status	String	The status of the assay. Options are: `active`: the Assay is created and active. `created`: The assay is created but inactive.
baseline	Assay Baseline	The assay baseline as defined in Create Assay
window	Assay Window	The assay window as defined in Create Assay
summarizer	Assay Window	The summarizer as defined in Create Assay
alert_threshold	Float	Alert threshold.
created_at	String	The DateTime when the assay was created.
workspace_id	Integer	The numerical identifier of the workspace.
id	Integer	The numerical identifier of the assay.
warning_threshold	Float	Warning threshold.
last_window_start	String	Date and time the last window date range started.
run_until	String	Date and time the assay will run to.
last_run	String	Date and time the last window run.

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_assay_results

Retrieve the results for an assay.

Get Assay Results Parameters

Field	Type	Description
assay_id	Integer (REQUIRED)	Numerical id for the assay.
start	String (OPTIONAL)	DateTime for when the baseline starts.
end	String (OPTIONAL)	DateTime for when the baseline ends.
workspace_id	Integer (OPTIONAL)	Numerical id for the workspace the assay is associated with.
workspace_name	String (OPTIONAL)	Name of the workspace the assay is associated with.

Get Assay Results Returns

Field	Type	Description
id	Integer	Numerical id of the assay result.
assay_id	Integer	Numberical id of the assay requested.
window_start	String	Date and time the window start range.
analyzed_at	String	Date and time the analysis started.
elapsed_millis	Integer	How long the assay analysis took in milliseconds.
pipeline_id	Integer	The pipeline id.
workspace_id	Integer	The numerical id for the workspace the assay is assigned to.
workspace_name	String	Name of the workspace the assay is associated with.
baseline_summary	Object	Result from summarizing one sample.
window_summary	Object	Result from summarizing one sample collection.
warning_threshold	Float	Warning threshold.
alert_threshold	Float	Alert threshold.
score	Float	Assay final score.
scores	List[Float]	All assay scores from the values.
bin_index	Integer	The bin index for the assay.
summarizer	Object	The assay summarizer.
status	String	The status of the assay. Values are `active` and `created`.
created_at	String	The date and time the assay was created.

Get Assay Results Examples

Retrieve the results from the assay created earlier.

# 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}