Cheat sheet for NEW users

This cheat sheet provides a quick overview of Vectice's 3 simple API calls to help you get started auto-documenting your datasets, models and notes.

Be sure to create your API key first!

Install Vectice

Start by installing the Vectice library.

# Install Vectice latest package
pip install vectice

# Install a specific version of the Vectice package
pip install vectice==<version number>

# Install reticulate if not already installed
install.packages("reticulate")

# Install the latest Vectice library
reticulate::py_install("vectice", pip = TRUE)

# Or install a specific version of Vectice
reticulate::py_install("vectice==<desired_version_number>", pip = TRUE)

Connect to Vectice

Get started by connecting to the Vectice API and starting an iteration.

#import and connect to Vectice
import vectice
connect = vectice.connect(api_token="your-api-key") #Paste your API key

# Connect to your project phase using your phase ID
phase = connect.phase("PHA-XXX") #You can fetch the relevant phase ID from your chosen Vectice project in the UI.

#Create an iteration
iteration = phase.create_or_get_current_iteration()

#import and connect to Vectice
vectice <- reticulate::import("vectice")
vct <- vectice$connect(api_token="your-api-key") #Paste your API key

# Connect to your project phase using your phase ID
phase = connect$phase("PHA-XXX") #You can fetch the relevant phase ID from your chosen Vectice project in the UI.

#Create an iteration
iter <- vct$phase("PHA-XXX")$create_or_get_current_iteration()// Some code

Auto-document your assets

Auto-document your assets to begin documenting your work in the Vectice app.

# Auto-document your first comments or notes
iteration.log("this is a comment")

# Auto-document your first dataset from a local file
clean_dataset = Dataset.clean(name=name, resource=FileResource(paths, dataframes), attachments=attachments)
iteration.log(clean_dataset)

# Auto-document your first model
model = Model(library, technique, metrics, properties, name, attachments, predictor)
iteration.log(model)

# Auto-document your first comments or notes
iter$log("this is a comment")

# Auto-document your first dataset from a local file
ds_resource <- vectice$FileResource(paths, dataframes)
raw_ds <- vectice$Dataset$origin(name=name, resource=ds_resource, attachments = attachments)
iter$log(raw_ds)

# Auto-document your first model
model <- vectice$Model(library, technique, metrics, properties, name, attachments, predictor)
iter$log(model)

Close your iteration

Once you are done logging your assets for an iteration, mark it complete.

# Completes and closes the current iteration once you are happy with it
iteration.complete()

# Completes and closes the current iteration once you are happy with it
iter$complete()

Additional resources

PreviousInteract with the Vectice API NextInstall Vectice library