scikeras 0.1.8

Scikit-Learn API wrappers for Keras.

Scikit-Learn Wrapper for Keras

The goal of this project is to provide wrappers for Keras models so that they can be used as part of a Scikit-Learn workflow. These wrappers seeek to emulate the base classes found in sklearn.base.

This project was originally part of Keras itself, but to simplify maintenence and implementation it is now hosted in this repository.

Learn more about the Scikit-Learn API.

Learn more about Keras, TensorFlow's Python API.

Python versions supported: the maximum overlap between Scikit-Learn and TensorFlow. Currently, this means Python >=3.6 and <=3.8.

Installation

This package is available on PyPi:

pip install scikeras

The only dependencies are scikit-learn>=0.21.0 (older version may work, but are untested) and TensorFlow>=2.1.0.

Wrapper Classes

BaseWrapper

Base implementation that wraps Keras models for use with Scikit-Learn workflows. Inherit from this wrapper to build other types of estimators, for example a Transformer. Refer to KerasClassifier and KerasRegressor for inspiration.

KerasClassifier

Implements the Scikit-Learn classifier interface, akin to sklearn.base.ClassifierMixin. By default, scoring is done using sklearn.metrics.accuracy_score.

KerasRegressor

Implements the Scikit-Learn classifier interface, akin to sklearn.base.RegressorMixin. By default, scoring is done using sklearn.metrics.r2_score. Note that Keras does not have R2 as a built in loss function. A basic implementation of a Keras compatible R2 loss funciton is provided in KerasRegressor.root_mean_squared_error.

Basic Usage

To use the wrappers, you must specify how to build the Keras model. The wrappers support both Sequential and Functional API models. There are 2 basic options to specify how the model is built:

1. Prebuilt model: pass an existing Keras model object to the wrapper, which will be copied to avoid modifying the existing model. You must pass the prebuilt model via the build_fn parameter when initializing the wrapper.
2. Dynamically built model: pass a function that returns a model object. The model will not be built until fit is called. More details below.

Prebuilt models

Example usage:

from scikeras.wrappers import KerasRegressor
from some_module import keras_model_object

estimator = KerasRegressor(build_fn=keras_model_object)

estimator.fit(X, y)
estimator.score(X, y)

Dynamically built models

There are 2 ways to specify a model building function for dynamically built models:

1. Pass a callable function or an instance of a class implementing __call__ as the build_fn parameter.
2. Subclass the wrapper and implement __call__ in your class.

The logic for selecting which method to use is in BaseWrapper._check_build_fn. For either method, the ultimate function used to build the model is stored by reference in BaseWrapper.__call__. From now on this will be refered to as model building function.

The signature of the model building function will be used to dynamically determine which parameters should be passed. Parameters are chosen from the arguments of fit and from the public parameters of the wrapper instance (ex: n_classes_ or n_outputs_). For example, to create a Multi Layer Perceptron model that is able to dynamically set the input and output sizes as well as hidden layer sizes, you would add X and n_outputs_ to your model building function's signature:

from scikeras.wrappers import KerasRegressor


def model_building_function(X, n_outputs_, hidden_layer_sizes):
    """Dynamically build regressor."""
    model = Sequential()
    model.add(Dense(X.shape[1], activation="relu", input_shape=X.shape[1:]))
    for size in hidden_layer_sizes:
        model.add(Dense(size, activation="relu"))
    model.add(Dense(n_outputs_))
    model.compile("adam", loss="mean_squared_error")
    return model

estimator = KerasRegressor(build_fn=model_building_function, hidden_layer_sizes=[200, 100])

estimator.fit(X, y)
estimator.score(X, y)

Note that in this example, hidden_layer_sizes was specified as an keyword argument to Keras Regressor. The value of this keyword argument will be stored as estimator.hidden_layer_sizes and will be available to the Scikit-Learn API for use with sklearn.model_selection.GridSearchCV and other hyperparameter tuning methods. Because hidden_layer_sizes is a public attribute of estimator as well as an argument to model_build_function, it's value will be passed to model_build_function when fit is called.

Also note that the input itself, X was passed, allowing the input shape/size to be dynamically determined when fit is called. Finally, n_outputs_ is generated by KerasRegressor and KerasClassifier when fit is called and is stored as a public attribute in estimator.n_outputs_. Because this is a public attribute of estimator, model_build_function can request it simply by having a parameter with that name.

The model parameters generated while fitting that are used by various parts of the Scikit-Learn API are:

• n_outputs_: number of outputs. For regression, this is always y.shape[1].
• n_classes_: The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems).
• classes_: The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).

Subclassing wrappers

It may be convenient to subclass a wrapper to hardcode keyword arguments and defaults. In general, this is more compatible with the Scikit-Learn API. If the class also implements the model building function as __call__, this becaome a self-contained estimator that is fully compatible with the Scikit-Learn API. A brief example:

from scikeras.wrappers import KerasRegressor


class MLPRegressor(KerasRegressor):

    def __init__(self, hidden_layer_sizes=None):
        self.hidden_layer_sizes = hidden_layer_sizes
        super().__init__()   # this is very important!

    def __call__(self, X, n_outputs_, hidden_layer_sizes):
        """Dynamically build regressor."""
        if hidden_layer_sizes is None:
            hidden_layer_sizes = (100, )
        model = Sequential()
        model.add(Dense(X.shape[1], activation="relu", input_shape=X.shape[1:]))
        for size in hidden_layer_sizes:
            model.add(Dense(size, activation="relu"))
        model.add(Dense(n_outputs_))
        model.compile("adam", loss=KerasRegressor.root_mean_squared_error)
        return model

A couple of notes:

1. It is very important to call super().__init__() to properly register kwargs and the model building function.
2. You must assign all parameters to a public attribute of the same name and should not change it's value. To change the value from a default you can either (1) change the value in the model building function (as above) or save the parameter under another name (ex: _hidden_layer_sizes, remember to also use this name in the model building function).
3. You should set a default for all tunable arguments (in this case, hidden_layer_sizes=None) as this is expected by the Scikit-Learn API.
4. In the example above, no kwargs are accepted, and none are passed on. You may choose to accept and pass on keyword arguments. Once the __init__ method resolution reaches BaseWrapper, any keyword arguments that have not been consumed by child classes will be saved as instance attributes and will be accessible to the Scikit-Learn API. For example:

from scikeras.wrappers import KerasRegressor


class MLPRegressor(KerasRegressor):

    def __init__(self, hidden_layer_sizes=None, **kwargs):
        self.hidden_layer_sizes = hidden_layer_sizes
        super().__init__(**kwargs)   # this is very important!

    def __call__(self, X, n_outputs_, hidden_layer_sizes):
        ...

estimator = MLPRegressor(hidden_layer_sizes=[200], a_kwarg="saveme")

estimator.a_kwarg == "saveme"  # True

This interface allows for multiple layers of inheritence and consumption of arguments:

from scikeras.wrappers import KerasRegressor


class ChildMLPRegressor(MLPRegressor):

    def __init__(self, child_argument=None, **kwargs):
        self.child_argument = child_argument
        super().__init__(**kwargs)   # this is very important!

    def __call__(self, X, n_outputs_, hidden_layer_sizes):
        ...

estimator = ChildMLPRegressor(child_argument="hello", a_kwarg="saveme")

estimator.child_argument == "hello"  # True
estimator.a_kwarg == "saveme"  # True
estimator.hidden_layer_sizes == None  # True, the default in MLPRegressor is used

As long as super() is called from the child classes all the way up to BaseEstimator, all arguments will be registered for the Scikit-Learn API to use.

Passing arguments to Keras methods

This section refers to passing arguments to fit, predict, predict_proba, and score methods of Keras models (e.g., epochs, batch_size),

There are 2 ways to pass these argumements:

1. Pass directly to KerasClassifier.fit or KerasRegressor.fit (or score, etc.).
2. Pass as a keyword argument when initalizing KerasClassifier or KerasRegressor. This will allow the parameter to be tunable by the Scikit-Learn hyperparameter tuning API (GridSearchCV or RandomizedSearchCV).

Advanced Usage

Multi-output problems

Scikit-Learn supports a limited number of multi-ouput problems and does not support any multi-input problems. See Multiclass and multilabel algorithms for more details.

These wrappers suppport all of the multi-output types that Scikit-Learn supports out of the box. So for example, you can create a model that has multiple sigmoid output layers, resulting in a multiple binary classification problem. This type of problem is denoted multilabel-indicator in Scikit-Learn. Another example is a model with multiple softmax outputs, resulting in what is known as a multiouput-multiclass classification. There are many ways to pair up a Keras model with a Scikit-Learn output type, summarized below:

	Number of targets	Target cardinality	Valid type_of_target	Keras Output Mode	Keras Output Type
Multiclass classification	1	>2	‘multiclass'	Single softmax	Numpy array
Multilabel classification	>1	2 (0 or 1)	'multilabel-indicator'	Multiple sigmoid	List of arrays
Multioutput regression	>1	Continuous	'continuous-multioutput'	Single	Single array
Multioutput- multiclass classification	>1	>2	'multiclass-multioutput'	Multiple softmax	List of arrays

This table mirrors the Scikit-Learn multi-output documentation.

As noted above, Keras returns a list of arrays in many cases. This list is joined back into a single array by _post_process_y.

Output pre-processing

Conversion from Scikit-Learn formatted y and Keras formatted y are done in the wrappers _pre_process_y and _post_process_y methods. The signatures are:

_pre_process_y

Signature: _pre_process_y(y: np.array) -> np.array, dict Inputs:

• y always a single numpy.array Outputs:
• y: a single numpy.array for a single Keras output or a list of numpy.array for a Keras model with multiple outputs
• extra_args: a dictionary containing extra parameters determined within _pre_process_y such as classes_. If used within fit, these parameters will overwrite instance parameters of the same name.

_post_process_y

Signature: _post_process_y(y: np.array) -> np.array, dict Inputs:

• y raw output form the Keras model's predict method, can be an array or list of arrays. Outputs:
• y: a single numpy.array. For classificaiton, this should contain class predictions.
• extra_args: for regression, this parameter is unused. For classification, this parameter contains prediction probabilities under the key class_probabilities.

To support a custom mapping from Keras to Scikit-Learn, you can subclass a wrapper and modify _pre_process_y and _post_process_y. For example, to support a mixed binary/multiclass classification as a multioutput-multiclass problem:

class FunctionalAPIMultiOutputClassifier(KerasClassifier):
    """Functional API Classifier with 2 outputs of different type.
    """

    def __call__(self, X, n_classes_):
        inp = Input((4,))

        x1 = Dense(100)(inp)

        binary_out = Dense(1, activation="sigmoid")(x1)
        cat_out = Dense(n_classes_[1], activation="softmax")(x1)

        model = Model([inp], [binary_out, cat_out])
        losses = ["binary_crossentropy", "categorical_crossentropy"]
        model.compile(optimizer="adam", loss=losses, metrics=["accuracy"])

        return model

    def _post_process_y(self, y):
        """To support targets of different type, we need to post-precess each one
           manually, there is no way to determine the types accurately.

           Takes KerasClassifier._post_process_y as a starting point and
           hardcodes the post-processing.
        """
        classes_ = self.classes_

        class_predictions = [
            classes_[0][np.where(y[0] > 0.5, 1, 0)],
            classes_[1][np.argmax(y[1], axis=1)],
        ]

        class_probabilities = np.squeeze(np.column_stack(y))

        y = np.squeeze(np.column_stack(class_predictions))

        extra_args = {"class_probabilities": class_probabilities}

        return y, extra_args

    def score(self, X, y):
        """Taken from sklearn.multiouput.MultiOutputClassifier
        """
        y_pred = self.predict(X)
        return np.mean(np.all(y == y_pred, axis=1))

The default implementation of _pre_process_y for KerasClassifier attempts to automatically determine the type of problem using sklearn.utils.multiclass.type_of_target. You may need to override this method if it is unable to determine the correct type for your data. The default implementation is provided as a static method so that you can test it without needing to instantiate a KerasClassifier.

Multi-input problems

As mentioned above, Scikit-Learn does not support multi-input problems since X must be a sinlge numpy.array. However, in order to extend this functionality, the wrappers provide a _pre_process_X method that allows mapping a single numpy.arary to a list of numpy.array for multi-input Keras models. For example:

class FunctionalAPIMultiInputClassifier(KerasClassifier):
    """Functional API Classifier with 2 inputs.
    """

    def __call__(self, n_classes_):
        inp1 = Input((1,))
        inp2 = Input((3,))

        x1 = Dense(100)(inp1)
        x2 = Dense(100)(inp2)

        x3 = Concatenate(axis=-1)([x1, x2])

        cat_out = Dense(n_classes_, activation="softmax")(x3)

        model = Model([inp1, inp2], [cat_out])
        losses = ["categorical_crossentropy"]
        model.compile(optimizer="adam", loss=losses, metrics=["accuracy"])

        return model

    @staticmethod  # _pre_process_X does not *need* to be a static method
    def _pre_process_X(X):
        """To support multiple inputs, a custom method must be defined.
        """
        return [X[:, 0], X[:, 1:4]], dict()

Note that similar to _pre_process_y, _pre_process_X returns the modified X along with a dictionary of extra parameters. This dictionary is currently unused, but is kept for symmetry with _pre_process_x and future flexibility.

Custom scorers

To override the function used for scoring, set the _scorer attribute of the wrapper to point to a scoring function with the signature scorer(y_true: np.array, y_pred: np.array) -> float.

Callbacks

The wrappers fully support Keras Callbacks. For general information on Callbacks, see the TensorFlow documenation.

Like any other parameter, callbacks will be passed to the model building function as long as the parameter and attribute names match. Callbacks may be hardcoded in a subclassed model, passed as a parameter to fit, passed as a parameter to the wrapper initializer or created dynamically during wrapper initialization.

Here is a simple example of a custom callback in use:

class SentinalCallback(keras.callbacks.Callback):
    """
    Callback class that sets an internal value once it's been acessed.
    """

    called = 0

    def __init__(self, tolerance):
        # we don't do anything with this, just an example
        self.tolerance = tolerance


    def on_train_begin(self, logs=None):
        """Increments counter."""
        self.called += 1


class ClassifierWithCallback(wrappers.KerasClassifier):
    """Must be defined at top level to be picklable.
    """

    def __init__(self, tolerance, hidden_dim=None):
        self.callbacks = [SentinalCallback(tolerance)]
        self.hidden_dim = hidden_dim
        super().__init__()

    def __call__(self, hidden_dim):
        return build_fn_clf(hidden_dim)

In this simple case, the callback is dynamically created during wrapper initialization. This allows great flexibility. In this example, the parameter tolerance can be tuned using Scikit-Learn hyperparameter tuning tools.

Model serialization

Several functions within Scikit-Learn require estimators to be picklable. In order to enable this, a combination of architechrure, weights and training config serialization are used. At the moment, whole model serialization can only be done to/from file, which is not appropriate for this use case. For more information on saving Keras models see the TensorFlow documentation.

In order to support model serialization, the wrappers __getstate__ and __setstate__ methods detect TensorFlow objects as long as they are attributes of the estimator or nested inside simple iterables (lists, tuples), dictionaries or simple classes. More complex nesting may cause failures.

The important thing is that models subclassed from tensorflow.keras.Model must register themselves as serializable. The easiest way to achieve this is to use the tensoflow.keras.utils.register_keras_serializable decorator. For more information, see the TensoFlow documentation here.

Random states

If the wrappers have a random_state parameter (set via **skparams or by subclassing), they will use it almost like ScikitLearn estimators use random_state. The only difference is that TensorFlow requires setting the random seed system-wide, so there may be side effects, for example if training models simulteneously in a multi-threaded enviroment.

Contributing

See CONTRIBUTING.md

History

0.1.8 (2020-06-30)

• Add support for partial fitting and warm starts (#17, thank you @stsievert!).
• Add support for random states via the random_state parameter (#27).
• Scikit-Learn SLEP10 compliance (#26).
• Fix unnecessary data reshaping warnings (#23).

0.1.7 (2020-05-18)

• Versioning fix.

0.1.6 (2020-05-18)

• Rename repo.
• Derive BaseWrapper from BaseEstimator.
• Python 3.8 support.

0.1.5.post1 (2020-05-13)

• Deprecate Python 3.5 (#11).
• Fix prebuilt model bug (#5).
• Clean up serialization (#7).
• Implement scikit-learn estimator tests (#10).

0.1.4 (2020-04-12)

• Offload output type detection for classification to sklearn.utils.multiclass.type_of_target.
• Add documentation.
• Some file cleanup.

0.1.3 (2020-04-11)

• First release on PyPI.