Log datasets

Learn how to log your datasets to Vectice.

Make sure to complete the prerequisites before getting started with logging datasets to Vectice. To learn more, view our Getting Started with Vectice API guide.

The Vectice API enables you to log all datasets used during development to the Vectice app. This includes origin datasets, cleaned datasets, and modeling datasets.

Dataset type	Description
Origin datasets	Origin datasets refer to your datasets containing raw data.
Cleaned datasets	Cleaned datasets refer to your datasets that have been cleaned and prepared for data modeling or data analysis.
Modeling datasets	Modeling datasets combine training, testing, and validation data in a single dataset.

Dataset type

Description

Origin datasets

Origin datasets refer to your datasets containing raw data.

Cleaned datasets

Cleaned datasets refer to your datasets that have been cleaned and prepared for data modeling or data analysis.

Modeling datasets

Modeling datasets combine training, testing, and validation data in a single dataset.

Dataset logging

Now that you know more about the different dataset types and available resources, we will walk you through logging your dataset metadata.

Each dataset's metadata is logged at the iteration level. Use the static methods for each dataset type (i.e., origin(), clean(), and modeling() to log your datasets to Vectice.

Log origin dataset

To log the columnar data of your origin dataset, use any resource to log it to your current iteration. For this example, we will use FileResource() to log a local origin dataset:

from vectice import FileResource

# Log your origin dataset's supporting artifacts to your current iteration
origin_dataset = Dataset.origin(name="Iris Origin", resource=FileResource(paths="raw_iris.csv"))

iteration.log(origin_dataset)

Log cleaned dataset

To log the columnar data of your cleaned dataset, use any resource to log it to your current iteration. For this example, we will use FileResource() to log a local clean dataset:

from vectice import FileResource

# Log your clean dataset's supporting artifacts to your current iteration
clean_dataset = Dataset.clean(name="Iris Cleaned", resource=FileResource(paths="iris_cleaned.csv"))
                                
iteration.log(clean_dataset)

Log modeling dataset

To log the columnar data of your modeling datasets used for training, testing, and validation, use any resource to log it to your current iteration. We will use FileResource() to log this example dataset:

Training and testing resources are required to log a modeling dataset. Validation resources are optional.

from vectice import FileResource

training_resource = FileResource(paths="iris_training.csv")
testing_resource = FileResource(paths="iris_testing.csv")
validation_resource = FileResource(paths="iris_validation.csv") #optional

# Log your modeling dataset's supporting artifacts to your current iteration
modeling_dataset = Dataset.modeling(
    name="Modeling Dataset",
    training_resource=training_resource,
    testing_resource=testing_resource,
    validation_resource=validation_resource)
    
iteration.log(modeling_dataset)

Full code example

This example will demonstrate the full workflow of how to log your modeling datasets' supporting artifacts to Vectice.

from vectice import FileResource

# Connect to Vectice 
connect = vectice.connect(
    api_token = 'your-api-key',        # Paste your api key
    host = 'https://app.vectice.com',  # Paste your host
)

# Select your project phase
phase = connect.phase("PHA-xxxx") #Paste your own phase ID

# Create an iteration
iteration = phase.create_or_get_current_iteration()

# Wrap your datasets used for training, testing, and validation
training_resource = FileResource(paths="iris_training.csv")
testing_resource = FileResource(paths="iris_testing.csv")
validation_resource = FileResource(paths="iris_validation.csv")

# Log your modeling datasets
modeling_data = Dataset.modeling(
    name="Modeling Dataset",
    training_resource=training_resource,
    testing_resource=testing_resource,
    validation_resource=validation_resource)

iteration.log(modeling_data)

# Complete the iteration
iteration.complete()

Once your wrapped dataset is logged inside of Vectice, you can view datasets supporting artifacts by clicking on the Datasets tab in the Vectice app.

Best practices

When logging datasets, append the dataset type (Origin, Cleaned, and Modeling) to the end of the corresponding dataset name for easy identification when logged in the Vectice app.
Before cleaning or transforming your datasets, log your origin dataset to Vectice. This way, you can always refer back to it if needed.
As you iterate through different modeling approaches, log multiple versions of your modeling dataset. This will help you to keep track of the changes you have made and to compare the results of different approaches.
Document your work thoroughly, including your data sources, cleaning and modeling processes, and any assumptions or decisions you make. This will help you to communicate your work to others and to ensure that your analysis is transparent and reproducible. Document key milestones via:
- The Vectice app in your Phase documentation
- The Vectice API by adding an iteration note
Mark your most valuable iterations and assets in the Vectice app by selecting the star next to the corresponding iteration and assets before beginning the phase review. This will make it easier for stakeholders and subject matter experts to identify the iterations and assets in review.

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