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python - Python - 机器学习：从数组列表创建训练和测试集

问题描述

我想创建一个在 RAVDESS 数据集 ( https://smartlaboratory.org/ravdess/ ) 上训练的神经网络：想法是使用这个数据集来检测对我的应用程序的麦克风说话的人的情绪。

使用 librosa 和下面的 for 循环，我提取了要用于分析的标签和特征。

# I started with only one folder to fasten the operations
oneActorPath = '/content/drive/My Drive/RAVDESS/Audio_Speech_Actors_01-24/Actor_01/'
lst = []

# Loop through each folder to find the wavs

for subdir, dirs, files in os.walk(oneActorPath):
  for file in files:
    if file == '.DS_Store':
      continue
    else:
      # Check if the format of the file is valid
      try:
        #Load librosa array
        data, rate = librosa.load(os.path.join(subdir,file))
        #Using the name of the file I can understand the emotion that contains
        file = file[6:8]
        arr = data, file
        lst.append(arr)
        #print(list)

      # If is not valid, skip it
      except ValueError:
        continue

此循环的输出是以下格式的数组列表：

[(array([-8.1530527e-10,  8.9952795e-10, -9.1185753e-10, ...,
          0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
  '08'),
 (array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), '08'),
 (array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), '06'),
 (array([-0.00050612, -0.00057967, -0.00035985, ...,  0.        ,
          0.        ,  0.        ], dtype=float32), '05'),
 (array([ 6.8139506e-08, -2.3837963e-05, -2.4622474e-05, ...,
          3.1678758e-06, -2.4535689e-06,  0.0000000e+00], dtype=float32),
  '05'),
 (array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
          6.9306935e-07, -6.6020442e-07,  0.0000000e+00], dtype=float32),
  '04'),
 (array([-7.30260945e-05, -1.18022966e-04, -1.08280736e-04, ...,
          8.83421380e-05,  4.97258679e-06,  0.00000000e+00], dtype=float32),
  '06'),
 (array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), '07'),
 (array([ 2.3406714e-05,  3.1186773e-05,  4.9467826e-06, ...,
          1.2180173e-07, -9.2944845e-08,  0.0000000e+00], dtype=float32),
  '01'),
 (array([ 1.1845550e-06, -1.6399191e-06,  2.5565218e-06, ...,
         -8.7445065e-09,  5.9859917e-09,  0.0000000e+00], dtype=float32),
  '04'),
 (array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), '03'),
 (array([-1.3284328e-05, -7.4090644e-07,  7.2679302e-07, ...,
          0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
  '07'),
 (array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
          5.0694009e-08, -3.4546797e-08,  0.0000000e+00], dtype=float32),
  '03'),
 (array([ 1.5591205e-07, -1.5845627e-07,  1.5362870e-07, ...,
          0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
  '01'),
 (array([0., 0., 0., ..., 0., 0., 0.], dtype=float32), '03'),
 (array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.1608539e-05,
         8.2463991e-09, 0.0000000e+00], dtype=float32), '03'),
 (array([-3.6192148e-07, -1.4590451e-05, -5.3999561e-06, ...,
         -1.9935460e-05, -3.4417746e-05,  0.0000000e+00], dtype=float32),
  '02'),
 (array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
         -2.5319534e-07,  2.6521766e-07,  0.0000000e+00], dtype=float32),
  '02'),
 (array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
         -2.5055220e-08,  1.2936166e-08,  0.0000000e+00], dtype=float32)
...

上面列表的每个元素的第二个元素（第一行中的'08'）表示根据下面的字典的数据集的标签

emotions = {
    "neutral": "01",
    "calm": "02",
    "happy": "03",
    "sad": "04",
    "angry": "05", 
    "fearful": "06", 
    "disgust": "07", 
    "surprised": "08"
}

至此，我有了标签和数据：如何拆分此数据集以获得训练集和测试集？

EDIT1：我需要了解如何从该结构中获取 X 和 y 以在数据上使用 train_test_split。

标签： pythonarraysdictionaryfor-loopmachine-learning

解决方案

您可以使用 scikit-learn 的train_test_split功能（相关文档）。文档中的示例非常简单：

import numpy as np
from sklearn.model_selection import train_test_split

X, y = np.arange(10).reshape((5, 2)), range(5)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

在您的情况下，您可能需要进行一些数据操作以从输出列表中获取X和y向量：

X, y = zip(*lst)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

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