C3 AI Evaluate Analytics
The Evaluate Analytics node provides an interface through which users can evaluate and aggregate complex analytics associated with one or more C3 AI objects. The output of the C3 AI Evaluate Analytics is a dataset on which additional operations can be performed.
Prerequisites
The user must be in the appropriate Admin Group and have the correct roles to be authorized to read data from the selected C3 Type. By default, C3 AI follows the principle of least privilege and users may not have been granted access to retrieve data from C3 Types. Please speak to the C3 AI Support Team to ensure proper setup.
Configuration
| Field | Description |
|---|---|
| Name default=none | A user-specified node name displayed in the canvas |
| Interval Required | Interval of evaluation. This will set the "x-axis" or index to an equally spaced timestamp with specified interval unit |
| Allowed values are SECOND, MINUTE, QUARTER HOUR, HOUR, DAY, MONTH, YEAR. | |
| Start Required | The start date of the evaluation period |
| The initial date for the timeseries. | |
| End Required | The end date of the evaluation period |
| The last date for the timeseries. | |
| Assign Timezone default=none | Specify a timezone to be assigned to the timeseries. UTC is the assumed default |
Show Missing Column default=Off | The metrics, timeseries, or features to be retrieved from the selected C3 Type. |
| Include gaps and unavailable results for each analytic in the output dataset. Value is between 0 (no data missing) to 100 (all data missing) | |
| BatchSize Override default=none | Advanced configuration for controlling parallelism of data retrieval |
| Break a metric evaluation into smaller or larger pieces by controlling the number of data points to be retrieved in each "batch". | |
Flatten Timeseries default=On | Group output by source record and analytic or output to a "flat" list |
| Flattened returns a flat list where each object/timestamp combination is its own row. |
Node Inputs/Outputs
| Input | A node that has "expressions" as an output. This includes the C3 AI Analytics node, C3 AI Custom TS Decl, C3 AI Expression, C3 AI Expression Select, or C3 AI Custom Analytic node. This node may take MULTIPLE nodes as inputs. |
|---|---|
| Output | Visual Notebooks returns a table, called a dataframe, from the C3 AI Application Data Model that contains all relevant data. Columns are labeled and include a symbol that specifies the data type of that column. |
Figure 1: Example node setup. Note the node must have at least two preceding nodes: a "C3 AI Type" followed by a "C3 AI Analytics" node (or one of the options listed above) to properly return a dataframe.
Figure 2: Example dataframe returned by running C3 Evaluate Analytics node.
Examples
Examples assume the "ServicePoint" Type is being used. A ServicePoint represents a service delivery point or customer using electricity in their home.
Selecting a daily interval with the default configuration results in a dataframe with a datapoint for each day and each Service Point over the evaluation window (the start and end date) for each timeseries/feature selected.
Figure 3: Properties Panel Configuration
Figure 4: Results from running the node
Selecting a monthly interval results in a dataframe with a datapoint for each Service Point and month over the evaluation window.
Figure 5: Properties Panel Configuration
Figure 6: Results from running the node
Selecting a monthly interval with Show Missing toggled on results in a dataframe with additional columns for each feature selected highlighting the availability of data. 100% of the data is missing in this example until October 2022
Figure 7: Properties Panel Configuration
Figure 8: Results from running the node
Toggling Flatten Timeseries off results in a dataframe where each row represents an instance of the selected Type (the ServicePoint id in this case) and the columns are arrays with the timeseries data. Use a Flatten Timeseries node to create a dataframe where each row represents a unique object/timestamp.
Figure 9: Properties Panel Configuration
Figure 10: Results from running the node