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Solving the CARS dataset with Keras

This script runs an instance of AutoML on the CARS dataset. In this example we only want to test the Neural Architecture Search (NAS) capabilities of AutoGOAL. CARS is a classic numeric feature dataset.

Dataset	URL
Cars	https://archive.ics.uci.edu/ml/datasets/Car+Evaluation

As usual, we will need the AutoML class, a suitable logger, and semantic types from autogoal.kb.

from autogoal.ml import AutoML
from autogoal.search import RichLogger
from autogoal.kb import *

To restrict which types of algorithms can AutoML use, we will manually invoke find_classes.

from autogoal.contrib import find_classes

Experimentation

Instantiate the classifier. Note that the input and output types here are defined to match the problem statement, i.e., supervised classification from matrix-like features.

classifier = AutoML(
    input=(MatrixContinuousDense, Supervised[VectorCategorical]),
    output=VectorCategorical,

We will set cross_validation_steps=1 to reduce the time that we spend on each pipeline. Keep in mind this will increase the generalization error of the AutoML process.

    cross_validation_steps=1,

Since we only want to try neural networks, we restrict the contrib registry to algorithms matching with Keras.

    registry=find_classes("Keras"),
)

Basic logging configuration.

loggers = [RichLogger()]

Finally, loading the CARS dataset, running the AutoML instance, and printing the results.

from autogoal.datasets import cars
from sklearn.model_selection import train_test_split

X, y = cars.load()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

By default, this will run for 5 minutes.

classifier.fit(X_train, y_train, logger=loggers)
score = classifier.score(X_test, y_test)

Let's see what we got!

print(score)