Python 洗牌并拆分2个numpy数组，以保持它们之间的顺序

我有两个numpy数组X和Y，形状为X:[47502242243]和Y:[4750,1]

X是训练数据集，Y是每个条目的正确输出标签

我想将数据分为训练和测试，以验证我的机器学习模型。因此，我想随机拆分它们，以便在对X和Y应用随机拆分后，它们都具有正确的顺序。即，在拆分后，X的每一行都正确地具有其相应的标签不变

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我怎样才能达到上述目标

我就是这样做的

def split(x, y, train_ratio=0.7):
  x_size = x.shape[0]
  train_size = int(x_size * train_ratio)
  test_size = x_size - train_size
  train_indices = np.random.choice(x_size, size=train_size, replace=False)
  mask = np.zeros(x_size, dtype=bool)
  mask[train_indices] = True
  x_train, y_train = x[mask], y[mask]
  x_test, y_test = x[~mask], y[~mask]
  return (x_train, y_train), (x_test, y_test)

我只需为我的列车集（随机）选择所需数量的索引，剩余的将用于测试集

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然后使用遮罩选择列车和测试样本。

我将这样做

def split(x, y, train_ratio=0.7):
  x_size = x.shape[0]
  train_size = int(x_size * train_ratio)
  test_size = x_size - train_size
  train_indices = np.random.choice(x_size, size=train_size, replace=False)
  mask = np.zeros(x_size, dtype=bool)
  mask[train_indices] = True
  x_train, y_train = x[mask], y[mask]
  x_test, y_test = x[~mask], y[~mask]
  return (x_train, y_train), (x_test, y_test)

我只需为我的列车集（随机）选择所需数量的索引，剩余的将用于测试集

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然后使用掩码选择训练和测试样本。

您还可以使用scikit learn

训练测试分割

仅使用两行代码分割数据：

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33)

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您还可以使用scikit learn

训练测试分割

仅使用两行代码分割数据：

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33)

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sklearn.model\u selection.train\u test\u split

是一个不错的选择

但是要自己制作一个

import numpy as np

def my_train_test_split(X, Y, train_ratio=0.8):
    """return X_train, Y_train, X_test, Y_test"""
    n = X.shape[0]
    split = int(n * train_ratio)
    index = np.arange(n)
    np.random.shuffle(index)
    return X[index[:split]], Y[index[:split]], X[index[split:]], Y[index[split:]]

---

sklearn.model\u selection.train\u test\u split

是一个不错的选择

但是要自己制作一个

import numpy as np

def my_train_test_split(X, Y, train_ratio=0.8):
    """return X_train, Y_train, X_test, Y_test"""
    n = X.shape[0]
    split = int(n * train_ratio)
    index = np.arange(n)
    np.random.shuffle(index)
    return X[index[:split]], Y[index[:split]], X[index[split:]], Y[index[split:]]