关于神经网络:MindSpore易点通精讲系列数据集加载之CSVDataset

Dive Into MindSpore – CSVDataset For Dataset LoadMindSpore精讲系列 – 数据集加载之CSVDataset本文开发环境Ubuntu 20.04Python 3.8MindSpore 1.7.0本文内容摘要先看API数据筹备两种试错正确示例本文总结问题改良本文参考1. 先看API老传统，先看看官网文档：

参数解读：dataset_files – 数据集文件门路，能够单文件也能够是文件列表filed_delim – 字段宰割符，默认为","column_defaults – 一个巨坑的参数，留待前面解读column_names – 字段名，用于后续数据字段的keynum_paraller_workers – 不再解释shuffle – 是否打乱数据，三种抉择[False, Shuffle.GLOBAL, Shuffle.FILES]Shuffle.GLOBAL – 混洗文件和文件中的数据，默认Shuffle.FILES – 仅混洗文件num_shards – 不再解释shard_id – 不再解释cache – 不再解释2. 数据筹备2.1 数据下载阐明：数据下载地址：UCI Machine Learning Repository: Iris Data Set应用如下命令下载数据iris.data和iris.names到目标目录：mkdir iris && cd iris
wget -c https://archive.ics.uci.edu/m...
wget -c https://archive.ics.uci.edu/m...
备注：如果受零碎限度，无奈应用wget命令，能够思考用浏览器下载，下载地址见阐明。2.2 数据简介Iris也称鸢尾花卉数据集，是一类多重变量剖析的数据集。数据集蕴含150个数据集，分为3类，每类50个数据，每个数据蕴含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）三个品种中的哪一类。更具体的介绍参见官网阐明：5. Number of Instances: 150 (50 in each of three classes)

Number of Attributes: 4 numeric, predictive attributes and the class
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
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!)

Class Distribution: 33.3% for each of 3 classes.
2.3 数据调配这里对数据进行初步调配，分成训练集和测试集，分配比例为4:1。相干解决代码如下：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()

将以上代码保留到preprocess.py文件，应用如下命令运行：留神批改相干数据文件门路python3 preprocess.py
输入内容如下：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.csv和iris_test.csv文件，目录内容如下所示：.
├── iris.data
├── iris.names
├── iris_test.csv
└── iris_train.csv

两种试错上面通过几种谬误（带引号）用法，来初步认识一下CSVDataset。3.1 column_defaults是哪样首先，先来个简略加载，代码如下：为不便读者复现，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 = Nonefor data in data_iter:    item = data    breakprint("====== 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()

将以上代码保留到load.py文件，运行命令：留神批改数据文件门路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，相干代码如下： # 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 报错剖析更多对于column_defaults参数的剖析请参考6.1节。还记得官网参数阐明吗，不记得没关系，这里再列出来。column_defaults (list, 可选) - 指定每个数据列的数据类型，无效的类型包含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。很显然，官网参数阐明是数据类型，然而到mindspore/dataset/engine/validators.py代码外面，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]
批改为：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，这个谬误咱们到3.2局部进行剖析。3.2 header要不要在3.1中，咱们依据对报错源码的剖析，明确了column_defaults的正确用法，然而仍然存在一个谬误。3.2.1 报错剖析依据报错信息，发现是mindspore/ccsrc/minddata/dataset/engine/datasetops/source/csv_op.cc中506行的报错，相干源码如下：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();
}
通过浏览下面的源码，发现源码中没有解决header行的代码，即默认所有行都是数据行。还记得2.3中数据调配局部的代码，咱们写入了header信息，而CSVDataset并不提供解决header行的能力。当初依据报错剖析定位，对2.3的数据调配代码进行批改，将代码preprocess_iris_data(iris_data_file, iris_train_file, iris_test_file)
批改为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。而后运行load.py文件（这里并不需要再改代码），输入内容如下：阐明：为不便读者查看，这里对格局进行了人为解决，内容不变。这里曾经可能正确读取数据，数据蕴含5个字段。数据字段名曾经依据指定的column_names做了解决。====== 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')}

正确示例通过3中的两种试错，咱们对CSVDataset有了初步意识，仔细的读者可能会发现，3中仍然有一个问题，那就是Classes字段没有进行数值化，上面咱们就来介绍一种对其数值化的办法。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 = Nonefor data in data_iter:    item = data    breakprint("====== 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()

将以上代码保留到load.py文件，运行命令：留神批改数据文件门路python3 load.py
输入内容如下：阐明：数据蕴含5个字段。Classes字段曾经依据cls_to_id_dict = {"Iris-setosa": 0, "Iris-versicolor": 1, "Iris-virginica": 2}进行了数值化转换。数值化转换用到了mindspore.dataset.text的无关办法，读者能够自行查阅，后续会出相干的解读文章。====== 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：这里还存在其余字段的数据归一化，就留待读者去尝试了。数值化转换局部，也能够通过在数据调配局部减少代码来提前转换，读者也能够进行尝试。5. 本文总结本文对MindSpore中的CSVDataset数据集接口进行了摸索和示例展现。通过谬误试探，发现目前CSVDataset的文档和性能还绝对较弱，只能说是可用。6. 问题改良6.1 column_defaults文档谬误英文文档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，无效的类型包含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就有肯定的歧义性，后面说了是每个字段的默认值（CSV文件中存在字段为空的状况），前面又说如果为空，则依照string类型解决，让人分不清到底是数据类型实例还是数据类型。留神：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中相应地位的值，字段的类型为column_defaults相应地位值的数据类型。例如：某CSV文件蕴含三个字段，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辨认为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 不反对文件含有header如题6.3 不反对读取指定字段如题，API层面不显式反对，不过能够通过后续的数据处理来反对。7. 本文参考mindspore.dataset.CSVDatasetmindspore/ccsrc/minddata/dataset/engine/datasetops/source/csv_op.cc本文为原创文章，版权归作者所有，未经受权不得转载！