ensemble.py

import pickle

class AdaBoostClassifier:
    '''A simple AdaBoost Classifier.'''

    def __init__(self, weak_classifier, n_weakers_limit):
        '''Initialize AdaBoostClassifier

        Args:
            weak_classifier: The class of weak classifier, which is recommend to be sklearn.tree.DecisionTreeClassifier.
            n_weakers_limit: The maximum number of weak classifier the model can use.
        '''
        pass

    def is_good_enough(self):
        '''Optional'''
        pass

    def fit(self,X,y):
        '''Build a boosted classifier from the training set (X, y).

        Args:
            X: An ndarray indicating the samples to be trained, which shape should be (n_samples,n_features).
            y: An ndarray indicating the ground-truth labels correspond to X, which shape should be (n_samples,1).
        '''
        pass


    def predict_scores(self, X):
        '''Calculate the weighted sum score of the whole base classifiers for given samples.

        Args:
            X: An ndarray indicating the samples to be predicted, which shape should be (n_samples,n_features).

        Returns:
            An one-dimension ndarray indicating the scores of differnt samples, which shape should be (n_samples,1).
        '''
        pass

    def predict(self, X, threshold=0):
        '''Predict the catagories for geven samples.

        Args:
            X: An ndarray indicating the samples to be predicted, which shape should be (n_samples,n_features).
            threshold: The demarcation number of deviding the samples into two parts.

        Returns:
            An ndarray consists of predicted labels, which shape should be (n_samples,1).
        '''
        pass

    @staticmethod
    def save(model, filename):
        with open(filename, "wb") as f:
            pickle.dump(model, f)

    @staticmethod
    def load(filename):
        with open(filename, "rb") as f:
            return pickle.load(f)