过采样（代码实现）

过采样

 

import numpy as np
import itertools

y_origin = [1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1]
y_origin = np.array(y_origin)
NUM_LABELS = 2
def to_one_hot(data, depth):
    return (np.arange(depth)==data[:, None]).astype(np.int32)

def over_sample( y_origin, threshold):
    y = to_one_hot(y_origin, NUM_LABELS)
    y_counts = np.sum(y, axis=0)
    sample_ratio = threshold / y_counts * y
    sample_ratio = np.max(sample_ratio, axis=1)
    sample_ratio = np.maximum(sample_ratio, 1)

    index = ratio_sample(sample_ratio)
    # x_token_train = [x_token_train[i] for i in index]

    return y_origin[index]


def ratio_sample(ratio):
    sample_times = np.floor(ratio).astype(int)

    # random sample ratio < 1 (decimal part)
    sample_ratio = ratio - sample_times
    random = np.random.uniform(size=sample_ratio.shape)
    index = np.where(sample_ratio > random)
    index = index[0].tolist()

    # over sample fixed integer times
    row_num = sample_times.shape[0]
    for row_index, times in zip(range(row_num), sample_times):
        index.extend(itertools.repeat(row_index, times))

    return index

over_y = over_sample(y_origin, 24)