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The C3 AI Suite provides researchers many tools to analyze data and build and deploy machine learning models. To get started using the C3 AI Suite, please follow the C3.ai DTI Quickstart guide. This guide explains how to connect to a C3.ai cluster, access data using C3.ai methods, and convert C3.ai method outputs to an easy-to-analyze form.

Please note, this guide covers how to run read-only queries on the C3 AI Suite. For more advanced topics, such as loading data, building metrics, or configuring and training machine learning models, please refer to the following wikis:

  • Data Integration (Not yet available)
  • Metrics (Not yet available)
  • Machine Learning (Not yet available)

Connect to a C3.ai cluster

There are several options to connect to a C3.ai Cluster:

Fetching Instances of Types

All data in the C3 AI Suite are stored in C3.ai Types.Users can access data from a C3.ai Type with the 'fetch' method. Behind the scenes, the 'fetch' method submits a query directly to the database underlying a C3.ai Type, and retrieves and presents query results to C3 AI Suite users.

The C3 AI Suite returns the 'fetch' query's response, which includes (1) data from the C3.ai Type itself; (2) Metadata for the 'fetch' query (e.g., the number of objects, whether additional data exists in the database) into the FetchResult type, for data analysis (see example below).

To learn more about the 'fetch' method, please see the following C3.ai Developer Documentation:

Users can also provide a FetchSpec (or parameters) to the 'fetch' method to describe particular data to retrieve (e.g., only retrieve gene sequences collected in Germany). The FetchSpec can be 'empty' (e.g., OutbreakLocation.fetch()), or contain several parameters to return a subset of the data.

Some example FetchSpec parameters include:

  • filter: Filter expression to return a subset of the data (e.g., age <= 20). Filter expressions must evaluate to a Boolean type (i.e., true or false)
  • limit: the maximum number of rows that should be returned. Be default, if no limit is specified, the C3 AI Suite returns 2,000 rows from the C3.ai Type. Specifying a limit is often helpful to debug a fetch 'method', without returning too many records.
  • include: Specifies the particular fields from a C3.ai Type to return to the FetchResult. By default, if no include spec is defined, all fields from the C3.ai Type will be returned.
  • order: Specifies the order to return the query's results (either "ascending" or "descending")

Note: Please see this C3.ai Developer Documentation for full list of FetchSpec parameters: https://developer.c3.ai/docs/7.12.17/type/FetchSpec

Examples of Fetch Calls

As an example, please see the DTI Housing Coverage Example here: https://github.com/c3aidti/HouseCoverageExample

In this example, the BlockInfo Type contains information aggregated about census blocks. We can fetch BlockInfo records, for which the 'prp_bf_lr' field exists (i.e., is not null). We can also retrieve these records in descending order by their 'id'.

BlockInfo.fetch({
	'limit': -1,
	'filter': 'exists(prp_bf_lr)',
	'order': 'descending(id)',
	'include': 'pct_i_l,pct_t_l,prp_res_lr,pop10_ha_lr,hu10_ha_lr,eroom_ha_lr,med10_age,prp_bf_lr',
})

You can run this same fetch in Python:

raw_data = c3.BlockInfo.fetch(spec={
    'limit': -1,
    'filter': 'exists(prp_bf_lr)',
    'order': 'descending(id)',
    'include': 'pct_i_l,pct_t_l,prp_res_lr,pop10_ha_lr,hu10_ha_lr,eroom_ha_lr,med10_age,prp_bf_lr'
})

Additional details on "Fetching in Python" are available in this C3.ai Developer Documentation: https://developer.c3.ai/docs/7.12.0/topic/ds-jupyter-notebooks

Additional examples of fetch calls can be found in our examples here:

Another useful fetch command is fetchCount. This function is nearly identical to the fetch commands above, but it just returns a count of the number of objects which match the fetch filter. This is useful when trying to determine whether a given search is refined enough.

In the Javascript Console this is:

BlockInfo.fetchCount({'filter': 'exists(prp_bf_lr)'})

The same in python is:

c3.BlockInfo.fetchCount(spec={'filter': 'exists(prp_bf_lr)'})


Converting Fetch results to usable forms in Jupyter Notebook

For most data analysis situations, FetchResults need to be massaged a little bit to be useful. Here, we show the typical ways FetchResults can be used.

Python

In python, generally, you get the 'objs' property from the FetchResults object, then call the toJson() function. This function returns an array of dictionaries each with keys equal to the requested properties of the fetched type. This works well with the pandas DataFrame constructor which accepts such an array. The returned DataFrame object can now be analyzed very easily. We show an example below.

A Code Example in Jupyter Notebook:

## continue from above ##
import pandas as pd
df = pd.DataFrame(raw_data.objs.toJson())
df.head()
df.drop('meta', axis=1, inplace=True)
df.drop('type', axis=1, inplace=True)
df.drop('version', axis=1, inplace=True)
df.drop('id', axis=1, inplace=True)
df.head()

Users can then use the resulting dataframe as they normally would.

ExpressionEngineFunctions

Several spots in the C3 API allow for the use of so-called 'ExpressionEngineFunctions'. These functions take a variety of arguments and perform a variety of processing tasks. For example, the function 'contains' takes two strings and checks whether the first argument contains the second argument. The function 'lowerCase' takes an input string, and returns that string with all uppercase letters replaced with lowercase ones. In addition to those string processing functions, many math functions exist as well such as 'log' which operates on a variety of input data types.

These functions are very helpful, and can be used in a number of places such as:

  • 'filter' field of FetchSpec
  • 'expression' field of CompoundMetric
  • 'value' field of tsDecl component of tsDecl Metrics.

Official C3 documentation for ExpressionEngineFunctions: https://developer.c3.ai/docs/7.12.0/type/ExpressionEngineFunction

Evaluating Metrics on Time series data

C3 can store timeseries data using many different types, however knowing the exact type of timeseries data isn't super important when it comes to evaluating so-called 'Metrics' on that data.

Normalization Process

Usually, timeseries data goes through a 'normalization' process, the purpose of which is to take non-uniform, and possibly multiple datasets and produce a single uniform timeseries which can be analyzed a little more easily in most cases. We copy here the list of normalization steps that are currently performed within the C3 platform, these are available from C3's official documentation here: https://developer.c3.ai/docs/7.12.0/guide/guide-c3aisuite-basic/ts-normalization-engine

  1. Drop data points with irregular dates. For example, dates where start date is after end date, dates are > 50 years apart, etc.
  2. Remove duplicate data points that might have been sent due to data loading issues or issues with IoT sensor hardware.
  3. Correctly apportion the values in the correct time interval in case of overlapping data points.
  4. Convert data points in various units into a homogenous unit utilizing C3's unit conversion capabilities.
  5. Automatic detection of the natural frequency of the data.
  6. Aggregate or disaggregate data into coarse or finer intervals to optimize for storage and accuracy.

Once the normalization process is complete, a single time series sampled at a uniform interval is given.

SimpleMetrics

Simple metrics form the 'base' of the Metrics system. They are defined on a specific Type and reference timeseries data stored within. Essentially, the Simple metric defines:

  1. The Type on which the metric is defined
  2. How to find the timeseries data on the Type
  3. Configuration of the Normalization engine
  4. The name of the metric

An example SimpleMetric is:

sample_met = c3.SimpleMetric({
	'id': 'SampleMetric_SampleType',
	'name': 'SampleMetric',
	'srcType': 'SampleType',
	'path': 'timeseriesValues',
	'expression': 'avg(avg(normalized.data.x))'
})

Another variety of SimpleMetric is a tsDecl Metric. These can be used to turn traditionally non-timeseries data such as event data or status data into timeseries. A tsDecl metric is the same as a SimpleMetric, but instead of an 'expression' field, a 'tsDecl' field is used. tsDecl metrics can sometimes provide some additional flexibility to define new metrics which the expression field may not support. The same example can be re-written as:

sample_met = c3.SimpleMetric({
	'id': 'SampleMetric_SampleType',
	'name': 'SampleMetric',
	'srcType': 'SampleType',
	'path': 'timeseriesValues',
	'tsDecl': {
		'data': 'data',
		'treatment': 'AVERAGE',
		'start': 'start',
		'value': 'value'
	}
})

Please note that the above examples do not have an example context in which they work. This will be updated soon with a version backed up by a working exercise.

For more detail, see the C3 documentation on SimpleMetrics here: https://developer.c3.ai/docs/7.12.0/guide/guide-c3aisuite-basic/metrics-simple-metrics, and tsDecl metrics here: https://developer.c3.ai/docs/7.12.17/guide/guide-c3aisuite-basic/metrics-tsdecl-metrics

CompoundMetrics

Compound metrics are generally easier to define and use as they operate on already defined metrics either Simple or Compound. They essentially just consist of and id/name, and an expression defining the metric in terms of constants and already defined metrics. If you try and execute a CompoundMetric on a type for which some necessary SimpleMetric is not defined, you'll get an error.

Essentially, a CompoundMetric defines:

  1. The name/id of the metric
  2. The expression defining the metric

An example CompoundMetric is:

sample_compound_met = c3.CompoundMetric.make({
	'id': 'CompoundMetric',
	'name': 'CompoundMetric',
	'expression': 'window("AVG", SimpleMetric, 0, 7)',
})

Please note that the above example does not have an example context in which it will work. This will be updated soon with a version backed up by a working exercise.

For more detail, see the C3 documentation on CompoundMetrics here: https://developer.c3.ai/docs/7.12.0/guide/guide-c3aisuite-basic/metrics-compound-metrics

Evaluating Metrics

Types on which you can evaluate a metric mixin the Type 'MetricEvaluatable' (C3 Docs here: https://developer.c3.ai/docs/7.12.0/type/MetricEvaluatable)

Finding Metrics

Not all SimpleMetrics are defined on all types.
This bestows the function 'listMetrics' (among others) to that type, so if you're unsure what kind of metrics are available for a given type, execute that function to get a list, for example:

Javascript:

var metrics = SmartBulb.listMetrics()
c3Grid(metrics)

Python:

pd.DataFrame(c3.SmartBulb.listMetrics().toJson())

Once you have the metric you want to evaluate in mind, you can evaluate it.

Evaluating Metrics

With a metric in mind, you can use the 'evalMetrics' API function which is brought in with the MetricEvaluatable type to actually evaluate the metric. The evalMetrics function takes an 'EvalMetricsSpec' type which contains the following:

  1. list of Type ids you want the metrics to be evaluated on
  2. A list of metrics you want to be evaluated
  3. A start date (in ISO 8601 format)
  4. An end date (in ISO 8601 format)
  5. An evaluation interval

Such an evaluation in Python might look like this:

spec = c3.EvalMetricsSpec({
	'ids': [ 'A', 'B', 'C' ],
	'expressions': [ 'SampleMetric', 'SampleMetric2' ],
	'start': '2019-01-01',
	'end': '2019-05-01',
	'interval': 'DAY',
})

results = c3.SampleType.evalMetrics(spec=spec)

The results are in the form of a 'EvalMetricsResult'. By itself, this type isn't easily usable, however C3 offers the type 'Dataset' which is better suited for data analysis.
We can then convert the EvalMetricsResult to a Dataset using a convenient helper function and then in the case of Python to a pandas DataFrame using another
helper function.

ds = c3.Dataset.fromEvalMetricsResult(result=results)
df = c3.Dataset.toPandas(dataset=ds)

If you're in the browser using Javascript, you can utilize the 'c3Viz' console function to display the result of eval metrics. The whole evaluation might look like this:

var spec = EvalMetricsSpec(
	ids= ['A', 'B', 'C' ],
	expressions= [ 'SampleMetric', 'SampleMetric2' ],
	start= '2019-01-01',
	end= '2019-05-01',
	interval= 'DAY')

var results = SampleType.evalMetrics(spec)
c3Viz(results)

Bonus: An additional function is available as well: evalMetric, Have a look at the MetricEvaluatable documentation to see how it differs from evalMetrics: https://developer.c3.ai/docs/7.12.0/type/MetricEvaluatable

Conclusion

To get started quickly, focus on 'CompoundMetrics'. They're the easiest to use, and for most cases, the 'AVG' treatment works well.

Official C3 documentation:

Review and Next Steps

For most data exploration, you'll find yourself 'Fetching' and running 'evalMetrics'. This guide provides a good foundation for these activities. This first set of activities might be described as 'Read-Only'. Here you're using the C3 AI Suite as simply a readable database and API. The next set of things to learn would be 'Write' type operations. How do you define new types? How do you 'persist' new instances of a type? How do you clean the databases in your tag up? And so on. These will be the subject of a planned DTI Guide.

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