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MindSpore易点通·精讲系列--数据集加载之CSVDataset

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Dive Into MindSpore – CSVDataset For Dataset Load

MindSpore精讲系列 – 数据集加载之CSVDataset

本文开发环境

本文内容摘要

1. 先看API

老传统,先看看官方文档:

参数解读:

2. 数据准备

2.1 数据下载

说明:

数据下载地址:UCI Machine Learning Repository: Iris Data Set

使用如下命令下载数据iris.datairis.names到目标目录:

mkdir iris && cd iris
wget -c https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
wget -c https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.names

**备注:**如果受系统限制,无法使用wget命令,可以考虑用浏览器下载,下载地址见说明。

2.2 数据简介

Iris也称鸢尾花卉数据集,是一类多重变量分析的数据集。数据集包含150个数据集,分为3类,每类50个数据,每个数据包含4个属性。可通过花萼长度,花萼宽度,花瓣长度,花瓣宽度4个属性预测鸢尾花卉属于(Setosa,Versicolour,Virginica)三个种类中的哪一类。

更详细的介绍参见官方说明:

5. Number of Instances: 150 (50 in each of three classes)

6. Number of Attributes: 4 numeric, predictive attributes and the class

7. Attribute Information:
   1. sepal length in cm
   2. sepal width in cm
   3. petal length in cm
   4. petal width in cm
   5. class:
      -- Iris Setosa
      -- Iris Versicolour
      -- Iris Virginica

8. Missing Attribute Values: None

Summary Statistics:
	         Min  Max   Mean    SD   Class Correlation
   sepal length: 4.3  7.9   5.84  0.83    0.7826
    sepal width: 2.0  4.4   3.05  0.43   -0.4194
   petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)
    petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)

9. Class Distribution: 33.3% for each of 3 classes.

2.3 数据分配

这里对数据进行初步分配,分成训练集和测试集,分配比例为4:1。

相关处理代码如下:

from random import shuffle


def preprocess_iris_data(iris_data_file, train_file, test_file, header=True):
    cls_0 = "Iris-setosa"
    cls_1 = "Iris-versicolor"
    cls_2 = "Iris-virginica"

    cls_0_samples = []
    cls_1_samples = []
    cls_2_samples = []

    with open(iris_data_file, "r", encoding="UTF8") as fp:
        lines = fp.readlines()
        for line in lines:
            line = line.strip()
            if not line:
                continue
            if cls_0 in line:
                cls_0_samples.append(line)
                continue
            if cls_1 in line:
                cls_1_samples.append(line)
                continue
            if cls_2 in line:
                cls_2_samples.append(line)

    shuffle(cls_0_samples)
    shuffle(cls_1_samples)
    shuffle(cls_2_samples)

    print("number of class 0: {}".format(len(cls_0_samples)), flush=True)
    print("number of class 1: {}".format(len(cls_1_samples)), flush=True)
    print("number of class 2: {}".format(len(cls_2_samples)), flush=True)

    train_samples = cls_0_samples[:40] + cls_1_samples[:40] + cls_2_samples[:40]
    test_samples = cls_0_samples[40:] + cls_1_samples[40:] + cls_2_samples[40:]

    header_content = "Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Classes"

    with open(train_file, "w", encoding="UTF8") as fp:
        if header:
            fp.write("{}\n".format(header_content))
        for sample in train_samples:
            fp.write("{}\n".format(sample))

    with open(test_file, "w", encoding="UTF8") as fp:
        if header:
            fp.write("{}\n".format(header_content))
        for sample in test_samples:
            fp.write("{}\n".format(sample))


def main():
    iris_data_file = "{your_path}/iris/iris.data"
    iris_train_file = "{your_path}/iris/iris_train.csv"
    iris_test_file = "{your_path}/iris/iris_test.csv"

    preprocess_iris_data(iris_data_file, iris_train_file, iris_test_file)


if __name__ == "__main__":
    main()

将以上代码保存到preprocess.py文件,使用如下命令运行:

注意修改相关数据文件路径

python3 preprocess.py

输出内容如下:

number of class 0: 50
number of class 1: 50
number of class 2: 50

同时在目标目录生成iris_train.csviris_test.csv文件,目录内容如下所示:

.
├── iris.data
├── iris.names
├── iris_test.csv
└── iris_train.csv

3. 两种试错

下面通过几种**错误(带引号)**用法,来初步认识一下CSVDataset

3.1 column_defaults是哪样

首先,先来个简单加载,代码如下:

为方便读者复现,这里将shuffle设置为False。

from mindspore.dataset import CSVDataset


def dataset_load(data_files):
    column_defaults = [float, float, float, float, str]
    column_names = ["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Classes"]

    dataset = CSVDataset(
        dataset_files=data_files,
        field_delim=",",
        column_defaults=column_defaults,
        column_names=column_names,
        num_samples=None,
        shuffle=False)

    data_iter = dataset.create_dict_iterator()
    item = None
    for data in data_iter:
        item = data
        break

    print("====== sample ======\n{}".format(item), flush=True)


def main():
    iris_train_file = "{your_path}/iris/iris_train.csv"

    dataset_load(data_files=iris_train_file)


if __name__ == "__main__":
    main()

将以上代码保存到load.py文件,运行命令:

注意修改数据文件路径

python3 load.py

纳尼,报错,来看看报错内容:

Traceback (most recent call last):
  File "/Users/kaierlong/Documents/Codes/OpenI/Dive_Into_MindSpore/code/chapter_01/csv_dataset.py", line 107, in <module>
    main()
  File "/Users/kaierlong/Documents/Codes/OpenI/Dive_Into_MindSpore/code/chapter_01/csv_dataset.py", line 103, in main
    dataset_load(data_files=iris_train_file)
  File "/Users/kaierlong/Documents/Codes/OpenI/Dive_Into_MindSpore/code/chapter_01/csv_dataset.py", line 75, in dataset_load
    dataset = CSVDataset(
  File "/Users/kaierlong/Documents/PyEnv/env_ms_1.7.0/lib/python3.9/site-packages/mindspore/dataset/engine/validators.py", line 1634, in new_method
    raise TypeError("column type in column_defaults is invalid.")
TypeError: column type in column_defaults is invalid.

看看引发报错的源码,mindspore/dataset/engine/validators.py 中1634行,相关代码如下:

        # check column_defaults
        column_defaults = param_dict.get('column_defaults')
        if column_defaults is not None:
            if not isinstance(column_defaults, list):
                raise TypeError("column_defaults should be type of list.")
            for item in column_defaults:
                if not isinstance(item, (str, int, float)):
                    raise TypeError("column type in column_defaults is invalid.")

3.1.1 报错分析

更多关于column_defaults参数的分析请参考6.1节。

还记得官方参数说明吗,不记得没关系,这里再列出来。

很显然,官方参数说明是数据类型,但是到mindspore/dataset/engine/validators.py代码里面,却检测的是数据实例类型。明确了这点,将代码:

column_defaults = [float, float, float, float, str]

修改为:

这里的数值取自iris.names文件,详情参考该文件。

column_defaults = [5.84, 3.05, 3.76, 1.20, "Classes"]

再次运行代码,再次报错:

WARNING: Logging before InitGoogleLogging() is written to STDERR
[ERROR] MD(13306,0x70000269b000,Python):2022-06-14-16:51:59.681.109 [mindspore/ccsrc/minddata/dataset/util/task_manager.cc:217] InterruptMaster] Task is terminated with err msg(more detail in info level log):Unexpected error. Invalid csv, csv file: /Users/kaierlong/Downloads/iris/iris_train.csv parse failed at line 1, type does not match.
Line of code : 506
File         : /Users/jenkins/agent-working-dir/workspace/Compile_CPU_X86_MacOS_PY39/mindspore/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/csv_op.cc

Traceback (most recent call last):
  File "/Users/kaierlong/Documents/Codes/OpenI/Dive_Into_MindSpore/code/chapter_01/csv_dataset.py", line 107, in <module>
    main()
  File "/Users/kaierlong/Documents/Codes/OpenI/Dive_Into_MindSpore/code/chapter_01/csv_dataset.py", line 103, in main
    dataset_load(data_files=iris_train_file)
  File "/Users/kaierlong/Documents/Codes/OpenI/Dive_Into_MindSpore/code/chapter_01/csv_dataset.py", line 90, in dataset_load
    for data in data_iter:
  File "/Users/kaierlong/Documents/PyEnv/env_ms_1.7.0/lib/python3.9/site-packages/mindspore/dataset/engine/iterators.py", line 147, in __next__
    data = self._get_next()
  File "/Users/kaierlong/Documents/PyEnv/env_ms_1.7.0/lib/python3.9/site-packages/mindspore/dataset/engine/iterators.py", line 211, in _get_next
    raise err
  File "/Users/kaierlong/Documents/PyEnv/env_ms_1.7.0/lib/python3.9/site-packages/mindspore/dataset/engine/iterators.py", line 204, in _get_next
    return {k: self._transform_tensor(t) for k, t in self._iterator.GetNextAsMap().items()}
RuntimeError: Unexpected error. Invalid csv, csv file: /Users/kaierlong/Downloads/iris/iris_train.csv parse failed at line 1, type does not match.
Line of code : 506
File         : /Users/jenkins/agent-working-dir/workspace/Compile_CPU_X86_MacOS_PY39/mindspore/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/csv_op.cc

好了,这个错误我们到3.2部分进行分析。

3.2 header要不要

3.1中,我们根据对报错源码的分析,明确了column_defaults的正确用法,但是依然存在一个错误。

3.2.1 报错分析

根据报错信息,发现是mindspore/ccsrc/minddata/dataset/engine/datasetops/source/csv_op.cc中506行的报错,相关源码如下:

Status CsvOp::LoadFile(const std::string &file, int64_t start_offset, int64_t end_offset, int32_t worker_id) {
  CsvParser csv_parser(worker_id, jagged_rows_connector_.get(), field_delim_, column_default_list_, file);
  RETURN_IF_NOT_OK(csv_parser.InitCsvParser());
  csv_parser.SetStartOffset(start_offset);
  csv_parser.SetEndOffset(end_offset);

  auto realpath = FileUtils::GetRealPath(file.c_str());
  if (!realpath.has_value()) {
    MS_LOG(ERROR) << "Invalid file path, " << file << " does not exist.";
    RETURN_STATUS_UNEXPECTED("Invalid file path, " + file + " does not exist.");
  }

  std::ifstream ifs;
  ifs.open(realpath.value(), std::ifstream::in);
  if (!ifs.is_open()) {
    RETURN_STATUS_UNEXPECTED("Invalid file, failed to open " + file + ", the file is damaged or permission denied.");
  }
  if (column_name_list_.empty()) {
    std::string tmp;
    getline(ifs, tmp);
  }
  csv_parser.Reset();
  try {
    while (ifs.good()) {
      // when ifstream reaches the end of file, the function get() return std::char_traits<char>::eof()
      // which is a 32-bit -1, it's not equal to the 8-bit -1 on Euler OS. So instead of char, we use
      // int to receive its return value.
      int chr = ifs.get();
      int err = csv_parser.ProcessMessage(chr);
      if (err != 0) {
        // if error code is -2, the returned error is interrupted
        if (err == -2) return Status(kMDInterrupted);
        RETURN_STATUS_UNEXPECTED("Invalid file, failed to parse csv file: " + file + " at line " +
                                 std::to_string(csv_parser.GetTotalRows() + 1) +
                                 ". Error message: " + csv_parser.GetErrorMessage());
      }
    }
  } catch (std::invalid_argument &ia) {
    std::string err_row = std::to_string(csv_parser.GetTotalRows() + 1);
    RETURN_STATUS_UNEXPECTED("Invalid csv, csv file: " + file + " parse failed at line " + err_row +
                             ", type does not match.");
  } catch (std::out_of_range &oor) {
    std::string err_row = std::to_string(csv_parser.GetTotalRows() + 1);
    RETURN_STATUS_UNEXPECTED("Invalid csv, " + file + " parse failed at line " + err_row + " : value out of range.");
  }
  return Status::OK();
}

通过阅读上面的源码,发现源码中没有处理header行的代码,即默认所有行都是数据行。还记得2.3中数据分配部分的代码,我们写入了header信息,而CSVDataset并不提供处理header行的能力。

现在根据报错分析定位,对2.3的数据分配代码进行修改,将代码

preprocess_iris_data(iris_data_file, iris_train_file, iris_test_file)

修改为

preprocess_iris_data(iris_data_file, iris_train_file, iris_test_file, header=False)

再次运行preprocess.py文件,生成新的数据。

然后运行load.py文件(这里并不需要再改代码),输出内容如下:

说明:

  1. 为方便读者查看,这里对格式进行了人为处理,内容不变。
  2. 这里已经能够正确读取数据,数据包含5个字段。
  3. 数据字段名已经根据指定的column_names做了处理。
====== sample ======
{'Sepal.Length': Tensor(shape=[], dtype=Float32, value= 5.5), 'Sepal.Width': Tensor(shape=[], dtype=Float32, value= 4.2), 'Petal.Length': Tensor(shape=[], dtype=Float32, value= 1.4), 'Petal.Width': Tensor(shape=[], dtype=Float32, value= 0.2), 
'Classes': Tensor(shape=[], dtype=String, value= 'Iris-setosa')}

4. 正确示例

通过3中的两种试错,我们对CSVDataset有了初步认识,细心的读者可能会发现,3中依然有一个问题,那就是Classes字段没有进行数值化,下面我们就来介绍一种对其数值化的方法。

源码如下:

from mindspore.dataset import CSVDataset
from mindspore.dataset.text import Lookup, Vocab


def dataset_load(data_files):
    column_defaults = [5.84, 3.05, 3.76, 1.20, "Classes"]
    column_names = ["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Classes"]

    dataset = CSVDataset(
        dataset_files=data_files,
        field_delim=",",
        column_defaults=column_defaults,
        column_names=column_names,
        num_samples=None,
        shuffle=False)

    cls_to_id_dict = {"Iris-setosa": 0, "Iris-versicolor": 1, "Iris-virginica": 2}
    vocab = Vocab.from_dict(word_dict=cls_to_id_dict)
    lookup = Lookup(vocab)
    dataset = dataset.map(input_columns="Classes", operations=lookup)

    data_iter = dataset.create_dict_iterator()
    item = None
    for data in data_iter:
        item = data
        break

    print("====== sample ======\n{}".format(item), flush=True)


def main():
    iris_train_file = "{your_path}/iris/iris_train.csv"

    dataset_load(data_files=iris_train_file)


if __name__ == "__main__":
    main()

将以上代码保存到load.py文件,运行命令:

注意修改数据文件路径

python3 load.py

输出内容如下:

说明:

  1. 数据包含5个字段。
  2. Classes字段已经根据cls_to_id_dict = {"Iris-setosa": 0, "Iris-versicolor": 1, "Iris-virginica": 2}进行了数值化转换。
  3. 数值化转换用到了mindspore.dataset.text的有关方法,读者可以自行查阅,后续会出相关的解读文章。
====== sample ======
{'Sepal.Length': Tensor(shape=[], dtype=Float32, value= 5.5), 'Sepal.Width': Tensor(shape=[], dtype=Float32, value= 4.2), 'Petal.Length': Tensor(shape=[], dtype=Float32, value= 1.4), 'Petal.Width': Tensor(shape=[], dtype=Float32, value= 0.2), 
'Classes': Tensor(shape=[], dtype=Int32, value= 0)}

后续:

5. 本文总结

本文对MindSpore中的CSVDataset数据集接口进行了探索和示例展示。通过错误试探,发现目前CSVDataset的文档和功能还相对较弱,只能说是可用。

6. 问题改进

6.1 column_defaults文档错误

英文文档

column_defaults (list, optional) – List of default values for the CSV field (default=None). Each item in the list is either a valid type (float, int, or string). If this is not provided, treats all columns as string type.

中文文档

column_defaults (list, 可选) - 指定每个数据列的数据类型,有效的类型包括float、int或string。默认值:None,不指定。如果未指定该参数,则所有列的数据类型将被视为string。

这里中文翻译有误。其实英文API就有一定的歧义性,前面说了是每个字段的默认值(CSV文件中存在字段为空的情况),后面又说如果为空,则按照string类型处理,让人分不清究竟是数据类型实例还是数据类型。

**注意:**其实这里既有数据类型实例的意思,又有数据类型的意思。当指定了column_defaults参数,则字段的默认值为column_defaults中相应位置的值,字段的类型为column_defaults相应位置值的数据类型。例如:某CSV文件包含三个字段,指定column_defaults为[2.0, 1, “x”],则读取该文件时,三个字段的类型会被识别为float、int、str,如果某行中第二个字段为空,则就用默认值1填充。

6.2 不支持文件含有header

如题

6.3 不支持读取指定字段

如题,API层面不显式支持,不过可以通过后续的数据处理来支持。

7. 本文参考

本文为原创文章,版权归作者所有,未经授权不得转载!

标签:iris,--,精讲,dataset,column,file,易点通,csv,data
来源: https://www.cnblogs.com/skytier/p/16481392.html