Structured Data Regression

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!export KERAS_BACKEND="torch"
!pip install autokeras

from sklearn.datasets import fetch_california_housing

import autokeras as ak

A Simple Example

The first step is to prepare your data. Here we use the California housing dataset as an example.

house_dataset = fetch_california_housing()
train_size = int(house_dataset.data.shape[0] * 0.9)

x_train = house_dataset.data[:train_size]
y_train = house_dataset.target[:train_size]
x_test = house_dataset.data[train_size:]
y_test = house_dataset.target[train_size:]

The second step is to run the StructuredDataRegressor. As a quick demo, we set epochs to 10. You can also leave the epochs unspecified for an adaptive number of epochs.

# Initialize the structured data regressor.
reg = ak.StructuredDataRegressor(
    overwrite=True, max_trials=3
)  # It tries 3 different models.
# Feed the structured data regressor with training data.
reg.fit(
    x_train,
    y_train,
    epochs=10,
)
# Predict with the best model.
predicted_y = reg.predict(x_test)
# Evaluate the best model with testing data.
print(reg.evaluate(x_test, y_test))

You can also specify the column names and types for the data as follows. The column_names is optional if the training data already have the column names, e.g. pandas.DataFrame, CSV file. Any column, whose type is not specified will be inferred from the training data.

# Initialize the structured data regressor.
reg = ak.StructuredDataRegressor(
    column_names=[
        "MedInc",
        "HouseAge",
        "AveRooms",
        "AveBedrms",
        "Population",
        "AveOccup",
        "Latitude",
        "Longitude",
    ],
    column_types={"MedInc": "numerical", "Latitude": "numerical"},
    max_trials=10,  # It tries 10 different models.
    overwrite=True,
)

Validation Data

By default, AutoKeras use the last 20% of training data as validation data. As shown in the example below, you can use validation_split to specify the percentage.

reg.fit(
    x_train,
    y_train,
    # Split the training data and use the last 15% as validation data.
    validation_split=0.15,
    epochs=10,
)

You can also use your own validation set instead of splitting it from the training data with validation_data.

split = 500
x_val = x_train[split:]
y_val = y_train[split:]
x_train = x_train[:split]
y_train = y_train[:split]
reg.fit(
    x_train,
    y_train,
    # Use your own validation set.
    validation_data=(x_val, y_val),
    epochs=10,
)

Reference

StructuredDataRegressor, AutoModel, StructuredDataBlock, DenseBlock, StructuredDataInput, RegressionHead,