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from tensorflow import keras |
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from sklearn.datasets import make_classification |
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from sklearn.model_selection import train_test_split |
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from hyperactive import BaseExperiment |
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X, y = make_classification(n_samples=1000, n_features=20, random_state=42) |
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X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) |
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class KerasMultiLayerPerceptron(BaseExperiment): |
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""" |
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A class for creating and evaluating a Keras-based Multi-Layer Perceptron (MLP) model. |
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This class inherits from BaseExperiment and is designed to build a simple MLP |
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using Keras, compile it with the Adam optimizer, and train it on the provided |
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training data. The model consists of one hidden dense layer with configurable |
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size and activation function, followed by an output layer with a sigmoid |
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activation for binary classification. |
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Attributes: |
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X_train (array-like): Training feature data. |
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X_val (array-like): Validation feature data. |
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y_train (array-like): Training target data. |
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y_val (array-like): Validation target data. |
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Methods: |
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_score(**params): Builds, compiles, and trains the MLP model using the |
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specified parameters for the hidden layer, and returns the validation |
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accuracy. |
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""" |
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def __init__(self, X_train, X_val, y_train, y_val): |
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super().__init__() |
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self.X_train = X_train |
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self.X_val = X_val |
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self.y_train = y_train |
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self.y_val = y_val |
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def _score(self, **params): |
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dense_layer_0 = params["dense_layer_0"] |
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activation_layer_0 = params["activation_layer_0"] |
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model = keras.Sequential( |
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[ |
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keras.layers.Dense( |
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dense_layer_0, |
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activation=activation_layer_0, |
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input_shape=(20,), |
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), |
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keras.layers.Dense(1, activation="sigmoid"), |
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] |
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) |
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model.compile( |
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optimizer=keras.optimizers.Adam(learning_rate=0.01), |
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loss="binary_crossentropy", |
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metrics=["accuracy"], |
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) |
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model.fit( |
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self.X_train, |
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self.y_train, |
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batch_size=32, |
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epochs=10, |
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validation_data=(self.X_val, self.y_val), |
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) |
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return model.evaluate(X_val, y_val)[1] |
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SimonBlanke /
Hyperactive
