Wallaroo MLOps API Essentials Guide: Assays Management
How to use the Wallaroo API for Assays Management
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
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/createwith baseline generated from Create Baseline Summary Statistics.- For our example, we will be using the output of the field
dense_2at the index 0 for the iopath.
- For our example, we will be using the output of the field
- 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 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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
| 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
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'metadata.partition': 'engine-5d965d96f4-wxk8l',
'metadata.pipeline_version': '800fa3cd-607f-4550-90ec-227d262f8a45',
'out.dense_1': [0.00012585521],
'time': 1722279178343},
{'anomaly.count': 0,
'in.dense_input': [1.052355506,
-0.7602601059,
-0.3124601687,
-0.5580714587,
-0.6198353331,
0.6635428464,
-1.2171685083,
0.3144529308,
0.2360632058,
0.878209955,
-0.5518803042,
-0.2781328417,
-0.5675947058,
-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
| 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_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. |
| limit | Integer (OPTIONAL) | Maximum number of results to return. |
| pipeline_id | Integer (OPTIONAL) | Numerical id of the pipeline the assay is in. |
Get Assay Results Returns
| Field | Type | Description |
|---|---|---|
| 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. |
| baseline_summary | Object | Result from summarizing one sample. |
| window_summary | Object | Result from summarizing one sample collection. |
| alert_threshold | Float | Alert threshold. |
| score | Float | Assay final score. |
| scores | List[Float] | All assay scores from the values. |
| summarizer | Object | The assay summarizer. |
| status | String | The status of the assay. Values are active and created. |
| id | Integer | The workspace id. |
| assay_id | Integer | The assay id. |
| pipeline_id | Integer | The pipeline id. |
| alert_threshold | Float | Warning threshold. |
| bin_index | Integer | The bin index for the assay. |
| created_at | String | The date and time the assay was created. |
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"}]