Issue
I am trying to conduct grid search using scikit-learn RandomizedSearchCV
function together with Keras KerasClassifier
wrapper for my unbalanced multi-class classification problem. However, when I try to give class_weight
as an input, the fit method gives me the following error:
RuntimeError: Cannot clone object <keras.wrappers.scikit_learn.KerasClassifier object at 0x000002AA3C676710>, as the constructor either does not set or modifies parameter class_weight
Below are the functions that I use to build the KerasClassifier
and the script for RandomizedSearchCV
:
build_fn:
import keras as k
def build_keras_model(loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'], optimiser = 'adam',
learning_rate = 0.001, n_neurons = 30, n_layers = 1, n_classes = 3,
l1_reg = 0.001, l2_reg = 0.001, batch_norm = False, dropout = None,
input_shape = (8,)):
model = k.models.Sequential()
model.add(k.layers.Dense(n_neurons,
input_shape = input_shape,
kernel_regularizer = k.regularizers.l1_l2(l1 = l1_reg, l2 = l2_reg),
activation = 'relu'))
if batch_norm is True:
model.add(k.layers.BatchNormalization())
if dropout is not None:
model.add(k.layers.Dropout(dropout))
i = 1
while i < n_layers:
model.add(k.layers.Dense(n_neurons,
kernel_regularizer = k.regularizers.l1_l2(l1 = l1_reg, l2 = l2_reg),
activation = 'relu'))
if batch_norm is True:
model.add(k.layers.BatchNormalization())
if dropout is not None:
model.add(k.layers.Dropout(dropout))
i += 1
del i
model.add(k.layers.Dense(n_classes, activation = 'softmax'))
if optimiser == 'adam':
koptimiser = k.optimizers.Adam(lr = learning_rate)
elif optimiser == 'adamax':
koptimiser = k.optimizers.Adamax(lr = learning_rate)
elif optimiser == 'nadam':
koptimiser = k.optimizers.Nadam(lr = learning_rate)
else:
print('Unknown optimiser type')
model.compile(optimizer = koptimiser, loss = loss, metrics = metrics)
model.summary()
return model
Script:
import scipy as sp
from sklearn.utils.class_weight import compute_class_weight
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import RandomizedSearchCV
parameters = {
'optimiser': ['adam', 'adamax', 'nadam'],
'learning_rate': sp.stats.uniform(0.0005, 0.0015),
'epochs': sp.stats.randint(500, 1501),
'n_neurons': sp.stats.randint(20, 61),
'n_layers': sp.stats.randint(1, 3),
'n_classes': [3],
'batch_size': sp.stats.randint(1, 11),
'l1_reg': sp.stats.reciprocal(1e-3, 1e1),
'l2_reg': sp.stats.reciprocal(1e-3, 1e1),
'batch_norm': [False],
'dropout': [None],
'metrics': [['accuracy']],
'loss': ['sparse_categorical_crossentropy'],
'input_shape': [(training_features.shape[1],)]
}
class_weights = compute_class_weight('balanced', np.unique(training_targets),
training_targets[target_label[0]])
class_weights = dict(enumerate(class_weights))
keras_model = KerasClassifier(build_fn = build_keras_model, verbose = 0, class_weight = class_weights)
clf = RandomizedSearchCV(keras_model, parameters, n_iter = 1, scoring = 'f1_micro',
n_jobs = 1, cv = 5, random_state = random_state)
clf.fit(training_features, training_targets.values[:, 0])
model = clf.best_estimator_
Solution
To pass class_weights in this scenario with KerasClassifier
, the class_weights should be passed in the fit method and then will be forwarded to the keras model.
grid_result = clf.fit(training_features, training_targets.values[:, 0], class_weight=class_weights)
In older versions it was neccecary to pass them with the clf__ prefix:
grid_result = clf.fit(training_features, training_targets.values[:, 0], clf__class_weight=class_weights)
Answered By - ixeption
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