DataVisualizer¶
The DataVisualizer component performs visualization on the data.
Pre-requisite: Explored dataset object saved into a path (in-path) or pushed into a repo.
Name |
data_visualizer |
Purpose |
To visualize data using xpresso Visualization Libaries |
Usage Scenarios |
It can be used to perform visualization on data. (refer Data Visualization Library |
Created By |
xpresso.ai Team |
Support e-mail |
|
Binary / Source / Both Versions |
Binary |
Docker Image Reference |
|
Type of component |
pipeline_job |
Usage Instructions |
|
Result |
Stores the visualization report into the location specified by out-path. Note: For saving data into NFS specify the out-path within mount_path |
Example |
Assume that a dataset has to be fetched from the data repository and visualized. Create a component of type ‘pipeline_job’, and deploy it using the Custom Docker image specified above. Create a pipeline using the component To visualize the dataset, the following parameters must be specified when an experiment is run on the pipeline: Run Name: <provide a unique run name> Pipeline Version: <select the version of the pipeline you want to run> repo-name: <name of data repository> (usually, the same as the solution name) branch-name: <name of branch from which to fetch data for exploration> commit-id: <commit ID of data to be fetched for exploration> target-attribute: <specify the name of the target attribute in your dataset, if any> out-path: <path in NFS mount where you want to store the results, e.g., ‘/data’> The visualization report will be stored here |
Deploy Solution Arguments:
Using explored data from mount path
Field |
Parameter key (refer run-parameters below) |
Description |
Mandatory? |
Dynamic arg required? |
Comments |
-component-name |
component_name |
The component name in the solution |
Yes |
Yes |
|
-visualizer-output-path |
visualizer_output_path |
The path where visualization results are saved |
Yes |
Yes |
Visualization results are saved here |
-visualizer-input-path |
visualizer_input_path |
Path of the file to load explored data for visualization |
Yes |
Yes |
|
-target-attribute |
target_attribute |
Name of the target variable in the dataset |
No |
Yes |
Applicable only to structured datasets (refer to Data Visualization Library |
Fetching explored data from the data versioning system
Field |
Parameter key (refer run-parameters below) |
Description |
Mandatory? |
Dynamic arg required? |
Comments |
-component-name |
component_name |
The component name in the solution |
Yes |
Yes |
|
-visualizer-output-path |
visualizer_output_path |
The path where visualization results are saved |
Yes |
Yes |
Visualization results are saved here |
-repo-name |
repo_name |
Name of the data versioning repository (usually, the same as the solution name) |
Yes |
Yes |
|
-branch-name |
branch_name |
Name of branch from which to fetch data for exploration |
Yes |
Yes |
|
-commit-id |
commit_id |
Commit ID of data to be fetched for exploration |
Yes |
Yes |
|
-target-attribute |
target_attribute |
Name of the target variable in the dataset |
No |
Yes |
Applicable only to structured datasets (refer to Data Visualization Library |
Dynamic-args:
Specify dynamic argument right after its static argument and check the “Dynamic” checkbox. The value of this dynamic arg should be a placeholder string. This string will appear on Run Experiment form where an actual run-time value for its static argument should be filled in.
Eg: If the static argument is -out-path, then it’s dynamic arg could be out_path. This out_path will be reflected as an input field in the Run Experiment form. Value to this input field can be a string-valued path which is the expected value for -out-path arg.
Run-parameters (file or commit ID):
While loading parameters from a file or data versioning repository use mentioned keys. For more details refer Guide For Dynamically Loading Run Parameters From File Or Data Versioning Repository