Automate your Model Documentation using H2O AutoDoc

H2O AutoDoc in Action

We know that H2O AutoDoc can automatically generate model documentation for supervised learning models created in H2O-3 and Scikit-Learn. Let’s see some of the ways by which we can generate the automatic report:

1. H2O AutoDoc for models created in H2O-3

H2O-3 is a fully open-source, distributed in-memory machine learning platform with linear scalability. The speed, quality, ease-of-use, and model-deployment for the various cutting-edge algorithms make H2O a highly sought-after API for big data data science. H2O also has an industry-leading AutoML functionality that can be used for automating the machine learning workflow.

The documentation can be generated in an editable word or a markdown format as follows:

from h2o_autodoc import Config
from h2o_autodoc import render_autodoc# get the H2O-3 model object required to create an H2O AutoDoc
model = h2o.get_model(“my_gbm_model”)# configure and render an AutoDoc
Config = Config(output_path=”full/path/AutoDoc_H2O3.docx”)
render_autodoc(h2o, config, model)

2. H2O AutoDoc for models created in Scikit-learn

Scikit-learn is an open-source software machine learning library for the Python programming language. It features various classification, regression, and clustering algorithms. The process to create automatic documentation for models created in scikit learn is also pretty similar to the ones created in H2O-3 and is as follows:

from h2o_autodoc import Config
from h2o_autodoc.scikit.autodoc import render_autodoc# build a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)# configure and render an AutoDoc
Config = Config(output_path=”full/path/AutoDoc_ScikitLearn.docx”)
render_autodoc(config, model, X_train, y_train)

3. Steam: H2O AutoDoc

The H2O AutoDoc is currently available in Steam, another H2O.ai product that allows you to launch or connect to an H2O Cluster securely. This version of the H2O AutoDoc leverages the Steam Python API. The code follows the same structure as the H2O AutoDoc Python API, and the generated report is almost identical. One difference is that the Steam version only supports H2O-3 models.

import h2osteam
from h2osteam.clients import H2oClient# login to steam
h2osteam.login(url=”https://steam.h2o.ai:9555", username=”user01", password=”token-here”, verify_ssl=True)
cluster = H2oClient.get_cluster(“test-cluster”)from h2osteam import AutoDocConfig# get H2O-3 objects using their keys
model = h2o.get_model(“gbm_model”)
train = h2o.get_frame(“CreditCard_TRAIN”)# use default configuration settings
config = AutoDocConfig()# specify the path to the output file
output_file_path = “autodoc_report.docx”# download an H2O AutoDoc
cluster.download_autodoc(model, config, train, output_file_path)

Documentation Features

H2O AutoDoc generates an editable Word document based on an automated template that includes several features. Some of the important ones have been tabulated below:

* For supported algorithms

Try H2O AutoDocDo you want to get your hands dirty and experience the power that H2O AutoDoc brings to your machine learning project? We have made it easy for you. You can :

• Register for the trial license here and then try H2O AutoDoc in your environment.
• Our team will reach out, provide a 30-day trial license, and help you get up and running.
• Experiment and use it with your H2O-3 and scikit-learn models.

Conclusion

H2O AutoDoc automatically generates comprehensive model documentation in minutes using out-of-the-box or custom templates. H2O AutoDoc saves data science teams weeks of tedious work and increases data science productivity by allowing them to focus on model building. H2O AutoDoc increases the consistency of model documentation by applying a standard template across all models and teams, which is essential for model governance, reproducibility, and compliance with regulations.