Multi Table Pipelines

In the previous section we explored the most simple use cases, where the datasets consisted in a single table.

In this section we will cover cases where the dataset consist on multiple tables related by foreign keys.

Multi Table Classification Pipeline

In this example, we will be using the WikiQA dataset, which contains 4 different tables with simple parent/child relationships, and which we will load using the mlblocks.dataset.load_wikiqa function.

In our pipeline, we will be using the DeepFeatureSynthesis primitive from featuretools for feature extraction over the various tables that we have and later on apply an XGBClassifier on the resulting feature matrix.

Note how in this example we need to pass some additional information to the pipeline for the DFS primitive for it to know what the relationships between the multiple tables are.

from mlblocks import MLPipeline
from mlblocks.datasets import load_wikiqa

dataset = load_wikiqa()
dataset.describe()

X_train, X_test, y_train, y_test = dataset.get_splits(1)

primitives = [
    'featuretools.dfs',
    'xgboost.XGBClassifier'
]
pipeline = MLPipeline(primitives)

pipeline.fit(X_train, y_train, entities=dataset.entities,
             relationships=dataset.relationships, target_entity='data')

predictions = pipeline.predict(X_test, entities=dataset.entities,
              relationships=dataset.relationships, target_entity='data')

dataset.score(y_test, predictions)