CODEWITHSUNDEEP
machine-learningscikit-learnneural-networkadaboost
Arben7
Arben7

Reputation: 53

Using scikit-learn's MLPClassifier in AdaBoostClassifier

For a binary classification problem I want to use the MLPClassifier as the base estimator in the AdaBoostClassifier. However, this does not work because MLPClassifier does not implement sample_weight, which is required for AdaBoostClassifier (see here). Before that, I tried using a Keras model and the KerasClassifier within AdaBoostClassifier but that did also not work as mentioned here .

A way, which is proposed by User V1nc3nt is to build an own MLPclassifier in TensorFlow and take into account the sample_weight.

User V1nc3nt shared large parts of his code but since I have only limited experience with Tensorflow, I am not able to fill in the missing parts. Hence, I was wondering if anyone has found a working solution for building Adaboost ensembles from MLPs or can help me out in completing the solution proposed by V1nc3nt.

Thank you very much in advance!

Upvotes: 5

Views: 2646

Answers (1)

Venkatachalam
Venkatachalam

Reputation: 16966

Based on the references, which you had mentioned, I have modified MLPClassifier to accommodate sample_weights.

Try this!

from sklearn.neural_network import MLPClassifier
from sklearn.datasets import load_iris
from sklearn.ensemble import AdaBoostClassifier
import numpy as np

class customMLPClassifer(MLPClassifier):
    def resample_with_replacement(self, X_train, y_train, sample_weight):

        # normalize sample_weights if not already
        sample_weight = sample_weight / sample_weight.sum(dtype=np.float64)

        X_train_resampled = np.zeros((len(X_train), len(X_train[0])), dtype=np.float32)
        y_train_resampled = np.zeros((len(y_train)), dtype=int)
        for i in range(len(X_train)):
            # draw a number from 0 to len(X_train)-1
            draw = np.random.choice(np.arange(len(X_train)), p=sample_weight)

            # place the X and y at the drawn number into the resampled X and y
            X_train_resampled[i] = X_train[draw]
            y_train_resampled[i] = y_train[draw]

        return X_train_resampled, y_train_resampled


    def fit(self, X, y, sample_weight=None):
        if sample_weight is not None:
            X, y = self.resample_with_replacement(X, y, sample_weight)
        
        return self._fit(X, y, incremental=(self.warm_start and
                                            hasattr(self, "classes_")))


X, y = load_iris(return_X_y=True)
adabooster = AdaBoostClassifier(base_estimator=customMLPClassifer())

adabooster.fit(X, y)

Upvotes: 4

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