validation_data parameter instead of validation_split?

[jasonrichdarmawan]

If we use validation_split, there will be data leakage. Therefore, we need to make sure the validation set is constant by using validation_data parameter, instead of validation_split.

def get_model(hidden_layer_dim, meta):
    n_features_in_ = meta["n_features_in_"]
    X_shape_ = meta["X_shape_"]
    n_classes_ = meta["n_classes_"]
    
    model = tf.keras.models.Sequential()
    model.add(
        tf.keras.layers.Dense(
            units=4, input_shape=X_shape_[1:],
            kernel_initializer=tf.keras.initializers.HeNormal(seed=0)
        )
    )
    model.add(
        tf.keras.layers.BatchNormalization()
    )
    model.add(
        tf.keras.layers.LeakyReLU(alpha=0.3)
    )
    model.add(
        tf.keras.layers.Dense(units=1, activation="sigmoid")
    )
    
    return model

callback = tf.keras.callbacks.EarlyStopping(
    monitor="val_f1_score",
    mode="max",
    patience=10,
    min_delta=0.0001,
    restore_best_weights=True
)

clf = KerasClassifier(
    model=get_model,
    loss="binary_crossentropy",
    hidden_layer_dim=100,
    metrics=tfa.metrics.F1Score,
    metrics__num_classes=1,
    metrics__average='macro',
    metrics__threshold=0.3,
    epochs=100,
    callbacks=[callback],
    validation_split=0.1,
    verbose=False
)

def sel_score(X, y):
    return mutual_info_classif(
        X=X, y=y,
        discrete_features=[False, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True],
        random_state=0
    )

pipe = Pipeline(
    steps=[
        ('composer', composer),
        ('sel', SelectKBest(
            score_func=sel_score,
            k=17,  
        )),
        ('clf', clf)
    ]
)

params = {
    "clf__hidden_layer_dim": [8],
    "clf__loss": ["binary_crossentropy"],
    "clf__optimizer": ["sgd", "adam"],
    "clf__metrics__threshold": [0.3]
}
gs = GridSearchCV(
    pipe, params, refit=False, cv=3, 
    scoring=make_scorer(
        score_func=f1_score,
        average='macro',
        labels=['Yes']
    ),
    n_jobs=-1
)

gs.fit(X_train_val, y_train_val)

[xxl4tomxu98]

Wonder if there is an answer for this thread?

[adriangb]

Have you tried passing in fit__validation_data to the .fit() call?

[adriangb]

https://adriangb.com/scikeras/stable/advanced.html#routed-parameters

[xxl4tomxu98]

Have you tried passing in fit__validation_data to the .fit() call?

fit__validation_data is not in the fit() parameter for scikeras. Only fit__validation_split is in.