Dataset and Model Insights Explainer Demo

This example demonstrates how to use the Dataset and Model Insights Explainer and how to retrieve problems from the interpretation.

import logging
import os

import daimojo
import webbrowser

from h2o_sonar import interpret
from h2o_sonar.explainers.dataset_and_model_insights_explainer import DatasetAndModelInsightsExplainer
from h2o_sonar.lib.api.models import ModelApi

# dataset
dataset_path = "../../data/predictive/creditcard100_pred_missing_values.csv"
target_col = "predictions"

# model
mojo_path = "../../data/predictive/models/creditcard-binomial.mojo"
mojo_model = daimojo.model(mojo_path)
model = ModelApi().create_model(
    model_src=mojo_model,
    target_col=target_col,
    used_features=list(mojo_model.feature_names),
)

# results
results_location = "./results"
os.makedirs(results_location, exist_ok=True)

# parameters
interpret.describe_explainer(DatasetAndModelInsightsExplainer)

{'id': 'h2o_sonar.explainers.dataset_and_model_insights_explainer.DatasetAndModelInsightsExplainer',
 'name': 'DatasetAndModelInsightsExplainer',
 'display_name': 'Dataset and model insights explainer',
 'tagline': 'DatasetAndModelInsightsExplainer.',
 'description': 'The explainer checks the dataset and model for various issues. For example, it provides problems and actions for missing values in the target column and a low number of unique values across columns of a dataset.',
 'brief_description': 'DatasetAndModelInsightsExplainer.',
 'model_types': ['iid'],
 'can_explain': ['regression', 'binomial', 'multinomial'],
 'explanation_scopes': ['global_scope'],
 'explanations': [{'explanation_type': 'global-text-explanation',
   'name': 'TextExplanation',
   'category': '',
   'scope': 'global',
   'has_local': '',
   'formats': []}],
 'keywords': [],
 'parameters': [],
 'metrics_meta': []}

Interpret

%%capture
interpretation = interpret.run_interpretation(
    dataset=dataset_path,
    model=model,
    target_col=target_col,
    results_location=results_location,
    explainers=[DatasetAndModelInsightsExplainer.explainer_id()],
    log_level=logging.INFO,
)

Interact with the Explainer Result

# retrieve the result
result = interpretation.get_explainer_result(DatasetAndModelInsightsExplainer.explainer_id())

# open interpretation HTML report in web browser
webbrowser.open(interpretation.result.get_html_report_location())

True

# summary
result.summary()

{'id': 'h2o_sonar.explainers.dataset_and_model_insights_explainer.DatasetAndModelInsightsExplainer',
 'name': 'DatasetAndModelInsightsExplainer',
 'display_name': 'Dataset and model insights explainer',
 'tagline': 'DatasetAndModelInsightsExplainer.',
 'description': 'The explainer checks the dataset and model for various issues. For example, it provides problems and actions for missing values in the target column and a low number of unique values across columns of a dataset.',
 'brief_description': 'DatasetAndModelInsightsExplainer.',
 'model_types': ['iid'],
 'can_explain': ['regression', 'binomial', 'multinomial'],
 'explanation_scopes': ['global_scope'],
 'explanations': [{'explanation_type': 'global-text-explanation',
   'name': 'Dataset and model insights explainer',
   'category': 'CUSTOM',
   'scope': 'global',
   'has_local': None,
   'formats': ['text/plain']}],
 'keywords': [],
 'parameters': [],
 'metrics_meta': []}

# parameters
result.params()

{}

print(
    f"Found {len(interpretation.result.problems)} problems with the dataset. "
    f"Please check the interpretation for further details."
)

Found 2 problems with the dataset. Please check the interpretation for further details.

Save the explainer log and data

# save the explainer log
result.log(path="./d_and_m_insights-demo.log")

!head d_and_m_insights-demo.log