StructuredDataRegressor

autokeras.StructuredDataRegressor(
    column_names=None,
    column_types=None,
    output_dim=None,
    loss="mean_squared_error",
    metrics=None,
    project_name="structured_data_regressor",
    max_trials=100,
    directory=None,
    objective="val_loss",
    tuner=None,
    overwrite=False,
    seed=None,
    max_model_size=None,
    **kwargs
)

AutoKeras structured data regression class.

Arguments

column_names List[str] | None: A list of strings specifying the names of the columns. The length of the list should be equal to the number of columns of the data excluding the target column. Defaults to None.
column_types Dict[str, str] | None: Dict. The keys are the column names. The values should either be 'numerical' or 'categorical', indicating the type of that column. Defaults to None. If not None, the column_names need to be specified. If None, it will be inferred from the data.
output_dim int | None: Int. The number of output dimensions. Defaults to None. If None, it will be inferred from the data.
loss str | Callable | keras.losses.Loss: A Keras loss function. Defaults to use 'mean_squared_error'.
metrics List[str | Callable | keras.metrics.Metric] | List[List[str | Callable | keras.metrics.Metric]] | Dict[str, str | Callable | keras.metrics.Metric] | None: A list of Keras metrics. Defaults to use 'mean_squared_error'.
project_name str: String. The name of the AutoModel. Defaults to 'structured_data_regressor'.
max_trials int: Int. The maximum number of different Keras Models to try. The search may finish before reaching the max_trials. Defaults to 100.
directory str | pathlib.Path | None: String. The path to a directory for storing the search outputs. Defaults to None, which would create a folder with the name of the AutoModel in the current directory.
objective str: String. Name of model metric to minimize or maximize, e.g. 'val_accuracy'. Defaults to 'val_loss'.
tuner str | Type[autokeras.engine.tuner.AutoTuner] | None: String or subclass of AutoTuner. If string, it should be one of 'greedy', 'bayesian', 'hyperband' or 'random'. It can also be a subclass of AutoTuner. If left unspecified, it uses a task specific tuner, which first evaluates the most commonly used models for the task before exploring other models.
overwrite bool: Boolean. Defaults to False. If False, reloads an existing project of the same name if one is found. Otherwise, overwrites the project.
seed int | None: Int. Random seed.
max_model_size int | None: Int. Maximum number of scalars in the parameters of a model. Models larger than this are rejected.
**kwargs: Any arguments supported by AutoModel.

[source]

fit

StructuredDataRegressor.fit(
    x=None, y=None, epochs=None, callbacks=None, validation_split=0.2, validation_data=None, **kwargs
)

Search for the best model and hyperparameters for the AutoModel.

Arguments

x: numpy.ndarray. Training data x. It can be string or numerical data.
y: numpy.ndarray. Training data y. It can be raw labels, one-hot encoded if more than two classes, or binary encoded for binary classification.
epochs: Int. The number of epochs to train each model during the search. If unspecified, we would use epochs equal to 1000 and early stopping with patience equal to 10.
callbacks: List of Keras callbacks to apply during training and validation.
validation_split: Float between 0 and 1. Defaults to 0.2. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling. This argument is not supported when x is a dataset.
validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. validation_data will override validation_split. The type of the validation data should be the same as the training data. The best model found would be fit on the training dataset without the validation data.
**kwargs: Any arguments supported by keras.Model.fit.

Returns

history: A Keras History object corresponding to the best model. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

[source]

predict

StructuredDataRegressor.predict(x, **kwargs)

Predict the output for a given testing data.

Arguments

x: numpy.ndarray. Testing data x. It can be string or numerical data.
**kwargs: Any arguments supported by keras.Model.predict.

Returns

A list of numpy.ndarray objects or a single numpy.ndarray. The predicted results.

[source]

evaluate

StructuredDataRegressor.evaluate(x, y=None, **kwargs)

Evaluate the best model for the given data.

Arguments

x: numpy.ndarray. Testing data x. It can be string or numerical data.
y: numpy.ndarray. Testing data y. It can be string labels or numerical values.
**kwargs: Any arguments supported by keras.Model.evaluate.

Returns

Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

[source]

export_model

StructuredDataRegressor.export_model()

Export the best Keras Model.

Returns

keras.Model instance. The best model found during the search, loaded with trained weights.