Wallaroo SDK Essentials Guide: Assays Management

How to create and manage Wallaroo Assays through the Wallaroo SDK

Features:

Observe

Model Insights and Interactive Analysis Introduction

Wallaroo provides the ability to perform interactive analysis so organizations can explore the 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 thresholds for persistent automatic assays as desired.

IMPORTANT NOTE
Model insights operates over time and is difficult to demo in a notebook without pre-canned data. We assume you have an active pipeline that has been running and making predictions over time and show you the code you may use to analyze your pipeline.

Monitoring tasks called assays monitors a model’s predictions or the 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.

Assay Details

Assays contain the following attributes:

Attribute	Default	Description
Name		The name of the assay. Assay names must be unique.
Baseline Data		Data that is known to be “typical” (typically distributed) and can be used to determine whether the distribution of new data has changed.
Schedule	Every 24 hours at 1 AM	Configure the start time and frequency of when the new analysis will run. New assays are configured to run a new analysis for every 24 hours starting at the end of the baseline period. This period can be configured through the SDK.
Group Results	Daily	How the results are grouped: Daily (Default), Every Minute, Weekly, or Monthly.
Metric	PSI	Population Stability Index (PSI) is an entropy-based measure of the difference between distributions. Maximum Difference of Bins measures the maximum difference between the baseline and current distributions (as estimated using the bins). Sum of the difference of bins sums up the difference of occurrences in each bin between the baseline and current distributions.
Threshold	0.1	Threshold for deciding the difference between distributions is similar(small) or different(large), as evaluated by the metric. The default of 0.1 is generally a good threshold when using PSI as the metric.
Number of Bins	5	Number of bins used to partition the baseline data. By default, the binning scheme is percentile (quantile) based. The binning scheme can be configured (see Bin Mode, below). Note that the total number of bins will include the set number plus the `left_outlier` and the `right_outlier`, so the total number of bins will be the total set + 2.
Bin Mode	Quantile	Specify the Binning Scheme. Available options are: Quantile binning defines the bins using percentile ranges (each bin holds the same percentage of the baseline data). Equal binning defines the bins using equally spaced data value ranges, like a histogram. Custom allows users to set the range of values for each bin, with the Left Outlier always starting at Min (below the minimum values detected from the baseline) and the Right Outlier always ending at Max (above the maximum values detected from the baseline).
Bin Weight	Equally Weighted	The weight applied to each bin. The bin weights can be either set to Equally Weighted (the default) where each bin is weighted equally, or Custom where the bin weights can be adjusted depending on which are considered more important for detecting model drift.

Model Insights via the Wallaroo Dashboard SDK

Assays generated through the Wallaroo SDK can be previewed, configured, and uploaded to the Wallaroo Ops instance. The following is a condensed version of this process. For full details see the Wallaroo SDK Essentials Guide: Assays Management guide.

Model drift detection with assays using the Wallaroo SDK follows this general process.

Define the Baseline: From either historical inference data for a specific model in a pipeline, or from a pre-determine array of data, a baseline is formed.
Assay Preview: Once the baseline is formed, we preview the assay and configure the different options until we have the the best method of detecting environment or model drift.
Create Assay: With the previews and configuration complete, we upload the assay. The assay will perform an analysis on a regular scheduled based on the configuration.
Get Assay Results: Retrieve the analyses and use them to detect model drift and possible sources.
Pause/Resume Assay: Pause or restart an assay as needed.

Define the Baseline

Assay baselines are defined with the wallaroo.client.build_assay method. Through this process we define the baseline from either a range of dates or pre-generated values.

wallaroo.client.build_assay take the following parameters:

Parameter	Type	Description
assay_name	String (Required) - required	The name of the assay. Assay names must be unique across the Wallaroo instance.
pipeline	wallaroo.pipeline.Pipeline (Required)	The pipeline the assay is monitoring.
model_name	String (Required)	The name of the model to monitor.
iopath	String (Required)	The input/output data for the model being tracked in the format `input/output field index`. Only one value is tracked for any assay. For example, to track the output of the model’s field `house_value` at index `0`, the `iopath` is `'output house_value 0`.
baseline_start	datetime.datetime (Optional)	The start time for the inferences to use as the baseline. Must be included with `baseline_end`. Cannot be included with `baseline_data`.
baseline_end	datetime.datetime (Optional)	The end time of the baseline window. the baseline. Windows start immediately after the baseline window and are run at regular intervals continuously until the assay is deactivated or deleted. Must be included with `baseline_start`. Cannot be included with `baseline_data`..
baseline_data	numpy.array (Optional)	The baseline data in numpy array format. Cannot be included with either `baseline_start` or `baseline_data`.

Baselines are created in one of two ways:

Date Range: The baseline_start and baseline_end retrieves the inference requests and results for the pipeline from the start and end period. This data is summarized and used to create the baseline.
Numpy Values: The baseline_data sets the baseline from a provided numpy array.

Define the Baseline Example

This example shows two methods of defining the baseline for an assay:

"assays from date baseline": This assay uses historical inference requests to define the baseline. This assay is saved to the variable assay_builder_from_dates.
"assays from numpy": This assay uses a pre-generated numpy array to define the baseline. This assay is saved to the variable assay_builder_from_numpy.

In both cases, the following parameters are used:

Parameter	Value
assay_name	`"assays from date baseline"` and `"assays from numpy"`
pipeline	`mainpipeline`: A pipeline with a ML model that predicts house prices. The output field for this model is `variable`.
model_name	`"houseprice-predictor"` - the model name set during model upload.
iopath	These assays monitor the model’s output field variable at index 0. From this, the `iopath` setting is `"output variable 0"`.

The difference between the two assays’ parameters determines how the baseline is generated.

"assays from date baseline": Uses the baseline_start and baseline_end to set the time period of inference requests and results to gather data from.
"assays from numpy": Uses a pre-generated numpy array as for the baseline data.

For each of our assays, we will set the time period of inference data to compare against the baseline data.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

# assay builder by baseline
assay_builder_from_numpy = wl.build_assay(assay_name="assays from numpy", 
                               pipeline=mainpipeline, 
                               model_name="house-price-estimator", 
                               iopath="output variable 0", 
                               baseline_data = small_results_baseline)

# set the width, interval, and time period 
assay_builder_from_numpy.add_run_until(datetime.datetime.now())
assay_builder_from_numpy.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)

assay_config_from_numpy = assay_builder_from_numpy.build()
assay_results_from_numpy = assay_config_from_numpy.interactive_run()

Baseline DataFrame

The method wallaroo.assay_config.AssayBuilder.baseline_dataframe returns a DataFrame of the assay baseline generated from the provided parameters. This includes:

metadata: The inference metadata with the model information, inference time, and other related factors.
in data: Each input field assigned with the label in.{input field name}.
out data: Each output field assigned with the label out.{output field name}

Note that for assays generated from numpy values, there is only the out data based on the supplied baseline data.

In the following example, the baseline DataFrame is retrieved.

display(assay_builder_from_dates.baseline_dataframe())

	time	metadata	input_tensor_0	input_tensor_1	input_tensor_2	input_tensor_3	input_tensor_4	input_tensor_5	input_tensor_6	input_tensor_7	...	input_tensor_9	input_tensor_10	input_tensor_11	input_tensor_12	input_tensor_13	input_tensor_14	input_tensor_15	input_tensor_16	input_tensor_17	output_variable_0
0	1708013922866	{'last_model': '{"model_name":"house-price-estimator","model_sha":"e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6"}', 'pipeline_version': '', 'elapsed': [49610, 341497], 'dropped': [], 'partition': 'engine-58b565bf45-29xnm'}	4.0	3.00	3710.0	20000.0	2.0	0.0	2.0	5.0	...	2760.0	950.0	47.669600	-122.261000	3970.0	20000.0	79.0	0.0	0.0	1.514079e+06
1	1708013983808	{'last_model': '{"model_name":"house-price-estimator","model_sha":"e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6"}', 'pipeline_version': '', 'elapsed': [2517407, 2287534], 'dropped': [], 'partition': 'engine-58b565bf45-29xnm'}	3.0	2.50	1500.0	7420.0	1.0	0.0	0.0	3.0	...	1000.0	500.0	47.723598	-122.174004	1840.0	7272.0	42.0	0.0	0.0	4.196772e+05
2	1708013983808	{'last_model': '{"model_name":"house-price-estimator","model_sha":"e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6"}', 'pipeline_version': '', 'elapsed': [2517407, 2287534], 'dropped': [], 'partition': 'engine-58b565bf45-29xnm'}	4.0	2.50	2009.0	5000.0	2.0	0.0	0.0	3.0	...	2009.0	0.0	47.257702	-122.197998	2009.0	5182.0	0.0	0.0	0.0	3.208637e+05
3	1708013983808	{'last_model': '{"model_name":"house-price-estimator","model_sha":"e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6"}', 'pipeline_version': '', 'elapsed': [2517407, 2287534], 'dropped': [], 'partition': 'engine-58b565bf45-29xnm'}	3.0	1.75	1530.0	7245.0	1.0	0.0	0.0	4.0	...	1530.0	0.0	47.730999	-122.191002	1530.0	7490.0	31.0	0.0	0.0	4.319292e+05
4	1708013983808	{'last_model': '{"model_name":"house-price-estimator","model_sha":"e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6"}', 'pipeline_version': '', 'elapsed': [2517407, 2287534], 'dropped': [], 'partition': 'engine-58b565bf45-29xnm'}	3.0	1.75	1480.0	4800.0	2.0	0.0	0.0	4.0	...	1140.0	340.0	47.656700	-122.397003	1810.0	4800.0	70.0	0.0	0.0	5.361757e+05
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
496	1708013983808	{'last_model': '{"model_name":"house-price-estimator","model_sha":"e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6"}', 'pipeline_version': '', 'elapsed': [2517407, 2287534], 'dropped': [], 'partition': 'engine-58b565bf45-29xnm'}	4.0	2.25	2560.0	12100.0	1.0	0.0	0.0	4.0	...	1760.0	800.0	47.631001	-122.108002	2240.0	12100.0	38.0	0.0	0.0	7.019407e+05
497	1708013983808	{'last_model': '{"model_name":"house-price-estimator","model_sha":"e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6"}', 'pipeline_version': '', 'elapsed': [2517407, 2287534], 'dropped': [], 'partition': 'engine-58b565bf45-29xnm'}	2.0	1.00	1160.0	5000.0	1.0	0.0	0.0	4.0	...	1160.0	0.0	47.686501	-122.399002	1750.0	5000.0	77.0	0.0	0.0	4.508677e+05
498	1708013983808	{'last_model': '{"model_name":"house-price-estimator","model_sha":"e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6"}', 'pipeline_version': '', 'elapsed': [2517407, 2287534], 'dropped': [], 'partition': 'engine-58b565bf45-29xnm'}	4.0	2.50	1910.0	5000.0	2.0	0.0	0.0	3.0	...	1910.0	0.0	47.360802	-122.036003	2020.0	5000.0	9.0	0.0	0.0	2.962027e+05
499	1708013983808	{'last_model': '{"model_name":"house-price-estimator","model_sha":"e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6"}', 'pipeline_version': '', 'elapsed': [2517407, 2287534], 'dropped': [], 'partition': 'engine-58b565bf45-29xnm'}	3.0	1.50	1590.0	8911.0	1.0	0.0	0.0	3.0	...	1590.0	0.0	47.739399	-122.251999	1590.0	9625.0	58.0	0.0	0.0	4.371780e+05
500	1708013983808	{'last_model': '{"model_name":"house-price-estimator","model_sha":"e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6"}', 'pipeline_version': '', 'elapsed': [2517407, 2287534], 'dropped': [], 'partition': 'engine-58b565bf45-29xnm'}	4.0	2.75	2640.0	4000.0	2.0	0.0	0.0	5.0	...	1730.0	910.0	47.672699	-122.296997	1530.0	3740.0	89.0	0.0	0.0	7.184457e+05

501 rows × 21 columns

assay_builder_from_numpy.baseline_dataframe()

	output_variable_0
0	419677.20
1	320863.72
2	431929.20
3	536175.70
4	343304.63
...	...
495	701940.70
496	450867.70
497	296202.70
498	437177.97
499	718445.70

500 rows × 1 columns

Baseline Stats

The method wallaroo.assay.AssayAnalysis.baseline_stats() returns a pandas.core.frame.DataFrame of the baseline stats.

The baseline stats for each assay are displayed in the examples below.

assay_results_from_dates[0].baseline_stats()

	Baseline
count	501
min	236238.671875
max	1514079.375
mean	495193.231786
median	442168.125
std	226075.814267
start	None
end	None

assay_results_from_numpy[0].baseline_stats()

	Baseline
count	500
min	236238.67
max	1489624.3
mean	493155.46054
median	441840.425
std	221657.583536
start	None
end	None

Baseline Bins

The method wallaroo.assay.AssayAnalysis.baseline_bins a simple dataframe to with the edge/bin data for a baseline.

assay_results_from_dates[0].baseline_bins()

	b_edges	b_edge_names	b_aggregated_values	b_aggregation
0	2.362387e+05	left_outlier	0.000000	Density
1	2.962027e+05	q_20	0.203593	Density
2	4.159643e+05	q_40	0.195609	Density
3	4.640602e+05	q_60	0.203593	Density
4	6.821819e+05	q_80	0.197605	Density
5	1.514079e+06	q_100	0.199601	Density
6	inf	right_outlier	0.000000	Density

assay_results_from_numpy[0].baseline_bins()

	b_edges	b_edge_names	b_aggregated_values	b_aggregation
0	236238.67	left_outlier	0.000	Density
1	296202.70	q_20	0.204	Density
2	415964.30	q_40	0.196	Density
3	464057.38	q_60	0.200	Density
4	675545.44	q_80	0.200	Density
5	1489624.30	q_100	0.200	Density
6	inf	right_outlier	0.000	Density

Baseline Histogram Chart

The method wallaroo.assay_config.AssayBuilder.baseline_histogram returns a histogram chart of the assay baseline generated from the provided parameters.

assay_builder_from_dates.baseline_histogram()

Baseline KDE Chart

The method wallaroo.assay_config.AssayBuilder.baseline_kde returns a Kernel Density Estimation (KDE) chart of the assay baseline generated from the provided parameters.

assay_builder_from_dates.baseline_kde()

Baseline ECDF Chart

The method wallaroo.assay_config.AssayBuilder.baseline_ecdf returns a Empirical Cumulative Distribution Function (CDF) chart of the assay baseline generated from the provided parameters.

assay_builder_from_dates.baseline_ecdf()

Assay Preview

Now that the baseline is defined, we look at different configuration options and view how the assay baseline and results changes. Once we determine what gives us the best method of determining model drift, we can create the assay.

Analysis List Chart Scores

Analysis List scores show the assay scores for each assay result interval in one chart. Values that are outside of the alert threshold are colored red, while scores within the alert threshold are green.

Assay chart scores are displayed with the method wallaroo.assay.AssayAnalysisList.chart_scores(title: Optional[str] = None), with ability to display an optional title with the chart.

The following example shows retrieving the assay results and displaying the chart scores. From our example, we have two windows - the first should be green, and the second is red showing that values were outside the alert threshold.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates.chart_scores()

Analysis Chart

The method wallaroo.assay.AssayAnalysis.chart() displays a comparison between the baseline and an interval of inference data.

This is compared to the Chart Scores, which is a list of all of the inference data split into intervals, while the Analysis Chart shows the breakdown of one set of inference data against the baseline.

Score from the Analysis List Chart Scores and each element from the Analysis List DataFrame generates

The following fields are included.

Field	Type	Description
baseline mean	Float	The mean of the baseline values.
window mean	Float	The mean of the window values.
baseline median	Float	The median of the baseline values.
window median	Float	The median of the window values.
bin_mode	String	The binning mode used for the assay.
aggregation	String	The aggregation mode used for the assay.
metric	String	The metric mode used for the assay.
weighted	Bool	Whether the bins were manually weighted.
score	Float	The score from the assay window.
scores	List(Float)	The score from each assay window bin.
index	Integer/None	The window index. Interactive assay runs are `None`.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Quantile
aggregation = Density
metric = PSI
weighted = False
score = 0.0363497101644573
scores = [0.0, 0.027271477163285655, 0.003847844548034077, 0.000217023993714693, 0.002199485350158766, 0.0028138791092641195, 0.0]
index = None

Analysis List DataFrame

wallaroo.assay.AssayAnalysisList.to_dataframe() returns a DataFrame showing the assay results for each window aka individual analysis. This DataFrame contains the following fields:

Field	Type	Description
assay_id	Integer/None	The assay id. Only provided from uploaded and executed assays.
name	String/None	The name of the assay. Only provided from uploaded and executed assays.
iopath	String/None	The iopath of the assay. Only provided from uploaded and executed assays.
score	Float	The assay score.
start	DateTime	The DateTime start of the assay window.
min	Float	The minimum value in the assay window.
max	Float	The maximum value in the assay window.
mean	Float	The mean value in the assay window.
median	Float	The median value in the assay window.
std	Float	The standard deviation value in the assay window.
warning_threshold	Float/None	The warning threshold of the assay window.
alert_threshold	Float/None	The alert threshold of the assay window.
status	String	The assay window status. Values are: `OK`: The score is within accepted thresholds. `Warning`: The score has triggered the `warning_threshold` if exists, but not the `alert_threshold`. `Alert`: The score has triggered the the `alert_threshold`.

For this example, the assay analysis list DataFrame is listed.

From this tutorial, we should have 2 windows of dta to look at, each one minute apart. The first window should show status: OK, with the second window with the very large house prices will show status: alert

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates.to_dataframe()

	assay_id	name	iopath	score	start	min	max	mean	median	std	warning_threshold	alert_threshold	status
0	None			0.036350	2024-02-15T16:20:43.976756+00:00	2.362387e+05	1489624.250	5.177634e+05	4.486278e+05	227729.030050	None	0.25	Ok
1	None			8.868614	2024-02-15T16:22:43.976756+00:00	1.514079e+06	2016006.125	1.885772e+06	1.946438e+06	160046.727324	None	0.25	Alert

Analysis List Full DataFrame

wallaroo.assay.AssayAnalysisList.to_full_dataframe() returns a DataFrame showing all values, including the inputs and outputs from the assay results for each window aka individual analysis. This DataFrame contains the following fields:

pipeline_id	warning_threshold	bin_index	created_at

Field	Type	Description
window_start	DateTime	The date and time when the window period began.
analyzed_at	DateTime	The date and time when the assay analysis was performed.
elapsed_millis	Integer	How long the analysis took to perform in milliseconds.
baseline_summary_count	Integer	The number of data elements from the baseline.
baseline_summary_min	Float	The minimum value from the baseline summary.
baseline_summary_max	Float	The maximum value from the baseline summary.
baseline_summary_mean	Float	The mean value of the baseline summary.
baseline_summary_median	Float	The median value of the baseline summary.
baseline_summary_std	Float	The standard deviation value of the baseline summary.
baseline_summary_edges_{0…n}	Float	The baseline summary edges for each baseline edge from 0 to number of edges.
summarizer_type	String	The type of summarizer used for the baseline. See `wallaroo.assay_config` for other summarizer types.
summarizer_bin_weights	List / None	If baseline bin weights were provided, the list of those weights. Otherwise, `None`.
summarizer_provided_edges	List / None	If baseline bin edges were provided, the list of those edges. Otherwise, `None`.
status	String	The assay window status. Values are: `OK`: The score is within accepted thresholds. `Warning`: The score has triggered the `warning_threshold` if exists, but not the `alert_threshold`. `Alert`: The score has triggered the the `alert_threshold`.
id	Integer/None	The id for the window aka analysis. Only provided from uploaded and executed assays.
assay_id	Integer/None	The assay id. Only provided from uploaded and executed assays.
pipeline_id	Integer/None	The pipeline id. Only provided from uploaded and executed assays.
warning_threshold	Float	The warning threshold set for the assay.
warning_threshold	Float	The warning threshold set for the assay.
bin_index	Integer/None	The bin index for the window aka analysis.
created_at	Datetime/None	The date and time the window aka analysis was generated. Only provided from uploaded and executed assays.

For this example, full DataFrame from an assay preview is generated.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates.to_full_dataframe()

	window_start	analyzed_at	elapsed_millis	baseline_summary_count	baseline_summary_min	baseline_summary_max	baseline_summary_mean	baseline_summary_median	baseline_summary_std	baseline_summary_edges_0	...	summarizer_type	summarizer_bin_weights	summarizer_provided_edges	status	id	assay_id	pipeline_id	warning_threshold	bin_index	created_at
0	2024-02-15T16:20:43.976756+00:00	2024-02-15T16:26:42.266029+00:00	82	501	236238.671875	1514079.375	495193.231786	442168.125	226075.814267	236238.671875	...	UnivariateContinuous	None	None	Ok	None	None	None	None	None	None
1	2024-02-15T16:22:43.976756+00:00	2024-02-15T16:26:42.266134+00:00	83	501	236238.671875	1514079.375	495193.231786	442168.125	226075.814267	236238.671875	...	UnivariateContinuous	None	None	Alert	None	None	None	None	None	None

2 rows × 86 columns

Analysis Compare Basic Stats

The method wallaroo.assay.AssayAnalysis.compare_basic_stats returns a DataFrame comparing one set of inference data against the baseline.

This is compared to the Analysis List DataFrame, which is a list of all of the inference data split into intervals, while the Analysis Compare Basic Stats shows the breakdown of one set of inference data against the baseline.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].compare_basic_stats()

	Baseline	Window	diff	pct_diff
count	501.0	1000.0	499.000000	99.600798
min	236238.671875	236238.671875	0.000000	0.000000
max	1514079.375	1489624.25	-24455.125000	-1.615181
mean	495193.231786	517763.394625	22570.162839	4.557850
median	442168.125	448627.8125	6459.687500	1.460912
std	226075.814267	227729.03005	1653.215783	0.731266
start	None	2024-02-15T16:20:43.976756+00:00	NaN	NaN
end	None	2024-02-15T16:21:43.976756+00:00	NaN	NaN

Configure Assays

Before creating the assay, configure the assay and continue to preview it until the best method for detecting drift is set. The following options are available.

Score Metric

The score is a distance between the baseline and the analysis window. The larger the score, the greater the difference between the baseline and the analysis window. The following methods are provided determining the score:

PSI (Default) - Population Stability Index (PSI).
MAXDIFF: Maximum difference between corresponding bins.
SUMDIFF: Mum of differences between corresponding bins.

The metric type used is updated with the wallaroo.assay_config.AssayBuilder.add_metric(metric: wallaroo.assay_config.Metric) method.

The following three charts use each of the metrics. Note how the scores change based on the score type used.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set metric PSI mode
assay_builder_from_dates.summarizer_builder.add_metric(wallaroo.assay_config.Metric.PSI)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Quantile
aggregation = Density
metric = PSI
weighted = False
score = 0.0363497101644573
scores = [0.0, 0.027271477163285655, 0.003847844548034077, 0.000217023993714693, 0.002199485350158766, 0.0028138791092641195, 0.0]
index = None

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set metric MAXDIFF mode
assay_builder_from_dates.summarizer_builder.add_metric(wallaroo.assay_config.Metric.MAXDIFF)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Quantile
aggregation = Density
metric = MaxDiff
weighted = False
score = 0.06759281437125747
scores = [0.0, 0.06759281437125747, 0.028391217564870255, 0.006592814371257472, 0.02139520958083832, 0.02439920159680639, 0.0]
index = 1

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set metric SUMDIFF mode
assay_builder_from_dates.summarizer_builder.add_metric(wallaroo.assay_config.Metric.SUMDIFF)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Quantile
aggregation = Density
metric = SumDiff
weighted = False
score = 0.07418562874251496
scores = [0.0, 0.06759281437125747, 0.028391217564870255, 0.006592814371257472, 0.02139520958083832, 0.02439920159680639, 0.0]
index = None

Alert Threshold

Assay alert thresholds are modified with the wallaroo.assay_config.AssayBuilder.add_alert_threshold(alert_threshold: float) method. By default alert thresholds are 0.1.

The following example updates the alert threshold to 0.5.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

assay_builder_from_dates.add_alert_threshold(0.5)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates.to_dataframe()

	assay_id	name	iopath	score	start	min	max	mean	median	std	warning_threshold	alert_threshold	status
0	None			0.036350	2024-02-15T16:20:43.976756+00:00	2.362387e+05	1489624.250	5.177634e+05	4.486278e+05	227729.030050	None	0.5	Ok
1	None			8.868614	2024-02-15T16:22:43.976756+00:00	1.514079e+06	2016006.125	1.885772e+06	1.946438e+06	160046.727324	None	0.5	Alert

Number of Bins

Number of bins sets how the baseline data is partitioned. The total number of bins includes the set number plus the left_outlier and the right_outlier, so the total number of bins will be the total set + 2.

The number of bins is set with the wallaroo.assay_config.UnivariateContinousSummarizerBuilder.add_num_bins(num_bins: int) method.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# Set the number of bins
# update number of bins here
assay_builder_from_dates.summarizer_builder.add_num_bins(10)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Quantile
aggregation = Density
metric = PSI
weighted = False
score = 0.05250979748389363
scores = [0.0, 0.009076998929542533, 0.01924002322223739, 0.0021945246367443406, 0.0016700458183385653, 0.005779503770625584, 0.002393429678215835, 0.002942858220315506, 0.00010651192741915124, 0.00046961759334670583, 0.008636283687108028, 0.0]
index = None

Binning Mode

Binning Mode defines how the bins are separated. Binning modes are modified through the wallaroo.assay_config.UnivariateContinousSummarizerBuilder.add_bin_mode(bin_mode: bin_mode: wallaroo.assay_config.BinMode, edges: Optional[List[float]] = None).

Available bin_mode values from wallaroo.assay_config.Binmode are the following:

QUANTILE (Default): Based on percentages. If num_bins is 5 then quintiles so bins are created at the 20%, 40%, 60%, 80% and 100% points.
EQUAL: Evenly spaced bins where each bin is set with the formula min - max / num_bins
PROVIDED: The user provides the edge points for the bins.

If PROVIDED is supplied, then a List of float values must be provided for the edges parameter that matches the number of bins.

The following examples are used to show how each of the binning modes effects the bins.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# update binning mode here
assay_builder_from_dates.summarizer_builder.add_bin_mode(wallaroo.assay_config.BinMode.QUANTILE)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Quantile
aggregation = Density
metric = PSI
weighted = False
score = 0.0363497101644573
scores = [0.0, 0.027271477163285655, 0.003847844548034077, 0.000217023993714693, 0.002199485350158766, 0.0028138791092641195, 0.0]
index = None

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# update binning mode here
assay_builder_from_dates.summarizer_builder.add_bin_mode(wallaroo.assay_config.BinMode.EQUAL)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Equal
aggregation = Density
metric = PSI
weighted = False
score = 0.013362603453760629
scores = [0.0, 0.0016737762070682225, 1.1166481947075492e-06, 0.011233704798893194, 1.276169365380064e-07, 0.00045387818266796784, 0.0]
index = None

The following example manually sets the bin values.

The values in this dataset run from 200000 to 1500000. We can specify the bins with the BinMode.PROVIDED and specifying a list of floats with the right hand / upper edge of each bin and optionally the lower edge of the smallest bin. If the lowest edge is not specified the threshold for left outliers is taken from the smallest value in the baseline dataset.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

edges = [200000.0, 400000.0, 600000.0, 800000.0, 1500000.0, 2000000.0]

# update binning mode here
assay_builder_from_dates.summarizer_builder.add_bin_mode(wallaroo.assay_config.BinMode.PROVIDED, edges)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Provided
aggregation = Density
metric = PSI
weighted = False
score = 0.01005936099521711
scores = [0.0, 0.0030207963288415803, 0.00011480201840874194, 0.00045327555974347976, 0.0037119550613212583, 0.0027585320269020493, 0.0]
index = None

Aggregation Options

Assay aggregation options are modified with the wallaroo.assay_config.AssayBuilder.add_aggregation(aggregation: wallaroo.assay_config.Aggregation) method. The following options are provided:

Aggregation.DENSITY (Default): Count the number/percentage of values that fall in each bin.
Aggregation.CUMULATIVE: Empirical Cumulative Density Function style, which keeps a cumulative count of the values/percentages that fall in each bin.

The following example demonstrate the different results between the two.

#Aggregation.DENSITY - the default

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

assay_builder_from_dates.summarizer_builder.add_aggregation(wallaroo.assay_config.Aggregation.DENSITY)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Quantile
aggregation = Density
metric = PSI
weighted = False
score = 0.0363497101644573
scores = [0.0, 0.027271477163285655, 0.003847844548034077, 0.000217023993714693, 0.002199485350158766, 0.0028138791092641195, 0.0]
index = None

#Aggregation.CUMULATIVE

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

assay_builder_from_dates.summarizer_builder.add_aggregation(wallaroo.assay_config.Aggregation.CUMULATIVE)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Quantile
aggregation = Cumulative
metric = PSI
weighted = False
score = 0.17698802395209584
scores = [0.0, 0.06759281437125747, 0.03920159680638724, 0.04579441117764471, 0.02439920159680642, 0.0, 0.0]
index = None

Inference Interval and Inference Width

The inference interval aka window interval sets how often to run the assay analysis. This is set from the wallaroo.assay_config.AssayBuilder.window_builder.add_interval method to collect data expressed in time units: “hours=24”, “minutes=1”, etc.

For example, with an interval of 1 minute, the assay collects data every minute. Within an hour, 60 intervals of data is collected.

We can adjust the interval and see how the assays change based on how frequently they are run.

The width sets the time period from the wallaroo.assay_config.AssayBuilder.window_builder.add_width method to collect data expressed in time units: “hours=24”, “minutes=1”, etc.

For example, an interval of 1 minute and a width of 1 minute collects 1 minutes worth of data every minute. An interval of 1 minute with a width of 5 minutes collects 5 minute of inference data every minute.

By default, the interval and width is 24 hours.

For this example, we’ll adjust the width and interval from 1 minute to 5 minutes and see how the number of analyses and their score changes.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates.chart_scores()

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=5).add_interval(minutes=5).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates.chart_scores()

Add Run Until and Add Inference Start

For previewing assays, setting wallaroo.assay_config.AssayBuilder.add_run_until sets the end date and time for collecting inference data. When an assay is uploaded, this setting is no longer valid - assays run at the Inference Interval until the assay is paused.

Setting the wallaroo.assay_config.WindowBuilder.add_start sets the start date and time to collect inference data. When an assay is uploaded, this setting is included, and assay results will be displayed starting from that start date at the Inference Interval until the assay is paused. By default, add_start begins 24 hours after the assay is uploaded unless set in the assay configuration manually.

For the following example, the add_run_until setting is set to datetime.datetime.now() to collect all inference data from assay_window_start up until now, and the second example limits that example to only two minutes of data.

# inference data that includes all of the data until now 

assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(datetime.datetime.now())
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()
assay_results_from_dates.chart_scores()

# inference data that includes all of the data until now 

assay_builder_from_dates = wl.build_assay(assay_name="assays from date baseline", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and time period 
assay_builder_from_dates.add_run_until(assay_window_start+datetime.timedelta(seconds=120))
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)
assay_config_from_dates = assay_builder_from_dates.build()
assay_results_from_dates = assay_config_from_dates.interactive_run()

assay_results_from_dates.chart_scores()

Create Assay

With the assay previewed and configuration options determined, we officially create it by uploading it to the Wallaroo instance.

Once it is uploaded, the assay runs an analysis based on the window width, interval, and the other settings configured.

Assays are uploaded with the wallaroo.assay_config.upload() method. This uploads the assay into the Wallaroo database with the configurations applied and returns the assay id. Note that assay names must be unique across the Wallaroo instance; attempting to upload an assay with the same name as an existing one will return an error.

wallaroo.assay_config.upload() returns the assay id for the assay.

Typically we would just call wallaroo.assay_config.upload() after configuring the assay. For the example below, we will perform the complete configuration in one window to show all of the configuration steps at once before creating the assay.

# Build the assay, based on the start and end of our baseline time, 
# and tracking the output variable index 0
assay_builder_from_dates = wl.build_assay(assay_name="assays creation example", 
                                          pipeline=mainpipeline, 
                                          model_name="house-price-estimator", 
                                          iopath="output variable 0",
                                          baseline_start=assay_baseline_start, 
                                          baseline_end=assay_baseline_end)

# set the width, interval, and assay start date and time
assay_builder_from_dates.window_builder().add_width(minutes=1).add_interval(minutes=1).add_start(assay_window_start)

# add other options
assay_builder_from_dates.summarizer_builder.add_aggregation(wallaroo.assay_config.Aggregation.CUMULATIVE)
assay_builder_from_dates.summarizer_builder.add_metric(wallaroo.assay_config.Metric.MAXDIFF)
assay_builder_from_dates.add_alert_threshold(0.5)

assay_id = assay_builder_from_dates.upload()

The assay is now visible through the Wallaroo UI by selecting the workspace, then the pipeline, then Insights.

Get Assay Results

Once an assay is created the assay runs an analysis based on the window width, interval, and the other settings configured.

Assay results are retrieved with the wallaroo.client.get_assay_results method, which takes the following parameters:

Parameter	Type	Description
assay_id	Integer (Required)	The numerical id of the assay.
start	Datetime.Datetime (Required)	The start date and time of historical data from the pipeline to start analyses from.
end	Datetime.Datetime (Required)	The end date and time of historical data from the pipeline to limit analyses to.

IMPORTANT NOTE: This process requires that additional historical data is generated from the time the assay is created to when the results are available. To add additional inference data, use the Assay Test Data section above.

assay_results = wl.get_assay_results(assay_id=assay_id,
                     start=assay_window_start,
                     end=datetime.datetime.now())

assay_results.chart_scores()

assay_results[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Quantile
aggregation = Cumulative
metric = MaxDiff
weighted = False
score = 0.067592815
scores = [0.0, 0.06759281437125747, 0.03920159680638724, 0.04579441117764471, 0.02439920159680642, 0.0, 0.0]
index = 1

List and Retrieve Assay

If the assay id is not already know, it is retrieved from the wallaroo.client.list_assays() method. Select the assay to retrieve data for and retrieve its id with wallaroo.assay.Assay._id method.

wl.list_assays()

name	active	status	warning_threshold	alert_threshold	pipeline_name
assays creation example	True	{"run_at": "2024-02-15T16:40:53.212979206+00:00", "num_ok": 0, "num_warnings": 0, "num_alerts": 0}	None	0.5	assay-demonstration-tutorial

retrieved_assay = wl.list_assays()[0]

live_assay_results = wl.get_assay_results(assay_id=retrieved_assay._id,
                     start=assay_window_start,
                     end=datetime.datetime.now())

live_assay_results.chart_scores()

live_assay_results[0].chart()

baseline mean = 495193.23178642715
window mean = 517763.394625
baseline median = 442168.125
window median = 448627.8125
bin_mode = Quantile
aggregation = Cumulative
metric = MaxDiff
weighted = False
score = 0.067592815
scores = [0.0, 0.06759281437125747, 0.03920159680638724, 0.04579441117764471, 0.02439920159680642, 0.0, 0.0]
index = 1

Pause and Resume Assay

Assays are paused and started with the wallaroo.assay.Assay.turn_off and wallaroo.assay.Assay.turn_on methods.

For the following, we retrieve an assay from the wallaroo instance and pause it, then list the assays to verify its setting Active is False.

display(wl.list_assays())
retrieved_assay = wl.list_assays()[0]

name	active	status	warning_threshold	alert_threshold	pipeline_name
assays creation example	True	{"run_at": "2024-02-15T16:40:53.212979206+00:00", "num_ok": 0, "num_warnings": 0, "num_alerts": 0}	None	0.5	assay-demonstration-tutorial

Now we pause the assay, and show the assay list to verify it is no longer active.

retrieved_assay.turn_off()
display(wl.list_assays())

name	active	status	warning_threshold	alert_threshold	pipeline_name
assays creation example	False	{"run_at": "2024-02-15T16:40:53.212979206+00:00", "num_ok": 0, "num_warnings": 0, "num_alerts": 0}	None	0.5	assay-demonstration-tutorial

We resume the assay and verify its setting Active is True.

retrieved_assay.turn_on()
display(wl.list_assays())

name	active	status	warning_threshold	alert_threshold	pipeline_name
assays creation example	True	{"run_at": "2024-02-15T16:40:53.212979206+00:00", "num_ok": 0, "num_warnings": 0, "num_alerts": 0}	None	0.5	assay-demonstration-tutorial

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