Log models

The Vectice API enables you to log all models used during development to the Vectice app. To log your model's data and supporting artifacts, use the following Model method to declare your model.

from vectice import Model

model = Model(name, library, technique, metrics, predictor, attachments)

Once your model is declared, log the model's artifacts to vectice:

iteration.log(model)

Example

Below is a full example showcasing the concepts mentioned above.

import vectice
from vectice import Model, Metric
from sklearn import ...

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

# Retrieve the project phase to begin the model development  
phase = connect.phase("PHA-XXX")

# Initialize iteration
iteration = phase.create_or_get_current_iteration()

# Model artifacts
model_name = "your-model-name"
model_library = "sklearn"
model_technique = "clustering"
model_metrics = [Metric("RMSE", 153), Metric("Clusters number", 5)]
model_predictor = my_sklearn_predictor
model_attachments = ["attachment1.png", "attachment2.png" ]

# Declaring your model and its artifacts
model = Model(name=model_name, library=model_library, technique=model_technique, metrics=model_metrics, predictor=model_predictor, attachments=model_attachments)

# Log the model to Vectice
iteration.log(model)

For an in-depth example, view the code example found in the Iterative Development guide.