一行代码不用写,就可以训练、测试、使用模型,这个 star 量 1.5k 的项目帮你做到

技术讨论 kira ⋅ 于 1周前 ⋅ 384 阅读
内容来源:计算机视觉研究院 作者:Edison_机器之心


igel 是 GitHub 上的一个热门工具,基于 scikit-learn 构建,支持 sklearn 的所有机器学习功能,如回归、分类和聚类。用户无需编写一行代码即可使用机器学习模型,只要有 yaml 或 json 文件,来描述你想做什么即可。
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一行代码不用写,就可以训练、测试和使用模型,还有这样的好事?

最近,软件工程师 Nidhal Baccouri 就在 GitHub 上开源了一个这样的机器学习工具——igel,并登上了 GitHub 热榜。目前,该项目 star 量已有 1.5k。

项目地址:https://github.com/nidhaloff/igel

该项目旨在为每一个人(包括技术和非技术人员)提供使用机器学习的便捷方式。

项目作者这样描述创建 igel 的动机:「有时候我需要一个用来快速创建机器学习原型的工具,不管是进行概念验证还是创建快速 draft 模型。我发现自己经常为写样板代码或思考如何开始而犯愁。于是我决定创建 igel。」

igel 基于 scikit-learn 构建,支持 sklearn 的所有机器学习功能,如回归、分类和聚类。用户无需编写一行代码即可使用机器学习模型,只要有 yaml 或 json 文件,来描述你想做什么即可。

其基本思路是在人类可读的 yaml 或 json 文件中将所有配置进行分组,包括模型定义、数据预处理方法等,然后让 igel 自动化执行一切操作。用户在 yaml 或 json 文件中描述自己的需求,之后 igel 使用用户的配置构建模型,进行训练,并给出结果和元数据。

igel 目前支持的所有配置如下所示:

# dataset operations
dataset:
    type: csv  # [str] -> type of your dataset
    read_data_options: # options you want to supply for reading your data (See the detailed overview about this in the next section)
        sep:  # [str] -> Delimiter to use.
        delimiter:  # [str] -> Alias for sep.
        header:     # [int, list of int] -> Row number(s) to use as the column names, and the start of the data.
        names:  # [list] -> List of column names to use
        index_col: # [int, str, list of int, list of str, False] -> Column(s) to use as the row labels of the DataFrame,
        usecols:    # [list, callable] -> Return a subset of the columns
        squeeze:    # [bool] -> If the parsed data only contains one column then return a Series.
        prefix:     # [str] -> Prefix to add to column numbers when no header, e.g. ‘X’ for X0, X1, …
        mangle_dupe_cols:   # [bool] -> Duplicate columns will be specified as ‘X’, ‘X.1’, …’X.N’, rather than ‘X’…’X’. Passing in False will cause data to be overwritten if there are duplicate names in the columns.
        dtype:  # [Type name, dict maping column name to type] -> Data type for data or columns
        engine:     # [str] -> Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.
        converters: # [dict] -> Dict of functions for converting values in certain columns. Keys can either be integers or column labels.
        true_values: # [list] -> Values to consider as True.
        false_values: # [list] -> Values to consider as False.
        skipinitialspace: # [bool] -> Skip spaces after delimiter.
        skiprows: # [list-like] -> Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file.
        skipfooter: # [int] -> Number of lines at bottom of file to skip
        nrows: # [int] -> Number of rows of file to read. Useful for reading pieces of large files.
        na_values: # [scalar, str, list, dict] ->  Additional strings to recognize as NA/NaN.
        keep_default_na: # [bool] ->  Whether or not to include the default NaN values when parsing the data.
        na_filter: # [bool] -> Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file.
        verbose: # [bool] -> Indicate number of NA values placed in non-numeric columns.
        skip_blank_lines: # [bool] -> If True, skip over blank lines rather than interpreting as NaN values.
        parse_dates: # [bool, list of int, list of str, list of lists, dict] ->  try parsing the dates
        infer_datetime_format: # [bool] -> If True and parse_dates is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them.
        keep_date_col: # [bool] -> If True and parse_dates specifies combining multiple columns then keep the original columns.
        dayfirst: # [bool] -> DD/MM format dates, international and European format.
        cache_dates: # [bool] -> If True, use a cache of unique, converted dates to apply the datetime conversion.
        thousands: # [str] -> the thousands operator
        decimal: # [str] -> Character to recognize as decimal point (e.g. use ‘,’ for European data).
        lineterminator: # [str] -> Character to break file into lines.
        escapechar: # [str] ->  One-character string used to escape other characters.
        comment: # [str] -> Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether. This parameter must be a single character.
        encoding: # [str] -> Encoding to use for UTF when reading/writing (ex. ‘utf-8’).
        dialect: # [str, csv.Dialect] -> If provided, this parameter will override values (default or not) for the following parameters: delimiter, doublequote, escapechar, skipinitialspace, quotechar, and quoting
        delim_whitespace: # [bool] -> Specifies whether or not whitespace (e.g. ' ' or '    ') will be used as the sep
        low_memory: # [bool] -> Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference.
        memory_map: # [bool] -> If a filepath is provided for filepath_or_buffer, map the file object directly onto memory and access the data directly from there. Using this option can improve performance because there is no longer any I/O overhead.

    split:  # split options
        test_size: 0.2  #[float] -> 0.2 means 20% for the test data, so 80% are automatically for training
        shuffle: true   # [bool] -> whether to shuffle the data before/while splitting
        stratify: None  # [list, None] -> If not None, data is split in a stratified fashion, using this as the class labels.

    preprocess: # preprocessing options
        missing_values: mean    # [str] -> other possible values: [drop, median, most_frequent, constant] check the docs for more
        encoding:
            type: oneHotEncoding  # [str] -> other possible values: [labelEncoding]
        scale:  # scaling options
            method: standard    # [str] -> standardization will scale values to have a 0 mean and 1 standard deviation  | you can also try minmax
            target: inputs  # [str] -> scale inputs. | other possible values: [outputs, all] # if you choose all then all values in the dataset will be scaled

# model definition
model:
    type: classification    # [str] -> type of the problem you want to solve. | possible values: [regression, classification, clustering]
    algorithm: NeuralNetwork    # [str (notice the pascal case)] -> which algorithm you want to use. | type igel algorithms in the Terminal to know more
    arguments:          # model arguments: you can check the available arguments for each model by running igel help in your terminal
    use_cv_estimator: false     # [bool] -> if this is true, the CV class of the specific model will be used if it is supported
    cross_validate:
        cv: # [int] -> number of kfold (default 5)
        n_jobs:   # [signed int] -> The number of CPUs to use to do the computation (default None)
        verbose: # [int] -> The verbosity level. (default 0)
    hyperparameter_search:
        method: grid_search   # method you want to use: grid_search and random_search are supported
        parameter_grid:     # put your parameters grid here that you want to use, an example is provided below
            param1: [val1, val2]
            param2: [val1, val2]
        arguments:  # additional arguments you want to provide for the hyperparameter search
            cv: 5   # number of folds
            refit: true   # whether to refit the model after the search
            return_train_score: false   # whether to return the train score
            verbose: 0      # verbosity level

# target you want to predict
target:  # list of strings: basically put here the column(s), you want to predict that exist in your csv dataset
    - put the target you want to predict here
    - you can assign many target if you are making a multioutput prediction

这款工具具备以下特性:

  • 支持所有机器学习 SOTA 模型(甚至包括预览版模型);

  • 支持不同的数据预处理方法;

  • 既能写入配置文件,又能提供灵活性和数据控制;

  • 支持交叉验证;

  • 支持 yaml 和 json 格式;

  • 支持不同的 sklearn 度量,进行回归、分类和聚类;

  • 支持多输出 / 多目标回归和分类;

  • 在并行模型构建时支持多处理。

如前所示,igel 支持回归、分类和聚类模型,包括我们熟悉的线性回归、贝叶斯回归、支持向量机、Adaboost、梯度提升等。
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igel 支持的回归、分类和聚类模型。

快速入门

为了让大家快速上手 igel,项目作者在「README」文件中提供了详细的入门指南。

运行以下命令可以获取 igel 的帮助信息:

$ igel --help

# or just

$ igel -h
"""
Take some time and read the output of help command. You ll save time later if you understand how to use igel.
"""

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第一步是提供一份 yaml 文件(你也可以使用 json)。你可以手动创建一个. yaml 文件并自行编辑。但如何你很懒,也可以选择使用 igel init 命令来快速启动:

"""
igel init <args>
possible optional args are: (notice that these args are optional, so you can also just run igel init if you want)
-type: regression, classification or clustering
-model: model you want to use
-target: target you want to predict

Example:
If I want to use neural networks to classify whether someone is sick or not using the indian-diabetes dataset,
then I would use this command to initialize a yaml file:
$ igel init -type "classification" -model "NeuralNetwork" -target "sick"
"""
$ igel init

运行该命令之后,当前的工作目录中就有了一个 igel.yaml 文档。你可以检查这个文件并进行修改,也可以一切从头开始。
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在下面这个例子中,作者使用随机森林来判断一个人是否患有糖尿病。他用到的数据集是著名的「Pima Indians Diabetes Database」。

# model definition
model:
    # in the type field, you can write the type of problem you want to solve. Whether regression, classification or clustering
    # Then, provide the algorithm you want to use on the data. Here I'm using the random forest algorithm
    type: classification
    algorithm: RandomForest     # make sure you write the name of the algorithm in pascal case
    arguments:
        n_estimators: 100   # here, I set the number of estimators (or trees) to 100
        max_depth: 30       # set the max_depth of the tree

# target you want to predict
# Here, as an example, I'm using the famous indians-diabetes dataset, where I want to predict whether someone have diabetes or not.
# Depending on your data, you need to provide the target(s) you want to predict here
target:
    - sick

注意,作者将 n_estimators 和 max_depth 传递给了模型,用作模型的附加参数。如果你不提供参数,模型就会使用默认参数。你不需要记住每个模型的参数。相反,你可以在终端运行 igel models 进入交互模式。在交互模式下,系统会提示你输入你想要使用的模型以及你想要解决的问题的类型。接下来,Igel 将展示出有关模型的信息和链接。通过该链接,你可以看到可用参数列表以及它们的使用方法。

igel 的使用方式应该是从终端(igel CLI):

在终端运行以下命令来拟合 / 训练模型,你需要提供数据集和 yaml 文件的路径。

$ igel fit --data_path 'path_to_your_csv_dataset.csv' --yaml_file 'path_to_your_yaml_file.yaml'

# or shorter

$ igel fit -dp 'path_to_your_csv_dataset.csv' -yml 'path_to_your_yaml_file.yaml'

"""
That's it. Your "trained" model can be now found in the model_results folder
(automatically created for you in your current working directory).
Furthermore, a description can be found in the description.json file inside the model_results folder.
"""

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接下来,你可以评估训练 / 预训练好的模型:

$ igel evaluate -dp 'path_to_your_evaluation_dataset.csv'
"""
This will automatically generate an evaluation.json file in the current directory, where all evaluation results are stored
"""

file
如果你对评估结果比较满意,就可以使用这个训练 / 预训练好的模型执行预测。

$ igel predict -dp 'path_to_your_test_dataset.csv'
"""
This will generate a predictions.csv file in your current directory, where all predictions are stored in a csv file
"""

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你可以使用一个「experiment」命令将训练、评估和预测结合到一起:

$ igel experiment -DP "path_to_train_data path_to_eval_data path_to_test_data" -yml "path_to_yaml_file"

"""
This will run fit using train_data, evaluate using eval_data and further generate predictions using the test_data
"""

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当然,如果你想写代码也是可以的:

交互模式

交互模式是 v0.2.6 及以上版本中新添加的,该模式可以让你按照自己喜欢的方式写参数。

也就是说,你可以使用 fit、evaluate、predict、experiment 等命令而无需指定任何额外的参数,比如:

igel fit

如果你只是编写这些内容并点击「enter」,系统将提示你提供额外的强制参数。0.2.5 及以下版本会报错,所以你需要使用 0.2.6 及以上版本。
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如 demo 所示,你不需要记住这些参数,igel 会提示你输入这些内容。具体而言,Igel 会提供一条信息,解释你需要输入哪个参数。括号之间的值表示默认值。

端到端训练示例

项目作者给出了使用 igel 进行端到端训练的完整示例,即使用决策树算法预测某人是否患有糖尿病。你需要创建一个 yaml 配置文件,数据集可以在 examples 文件夹中找到。

拟合 / 训练模型:

model:
    type: classification
    algorithm: DecisionTree

target:
    - sick
$ igel fit -dp path_to_the_dataset -yml path_to_the_yaml_file

现在,igel 将拟合你的模型,并将其保存在当前目录下的 model_results 文件夹中。

评估模型:

现在开始评估预训练模型。Igel 从 model_results 文件夹中加载预训练模型并进行评估。你只需要运行 evaluate 命令并提供评估数据的路径即可。

$ igel evaluate -dp path_to_the_evaluation_dataset

Igel 进行模型评估,并将 statistics/results 保存在 model_results 文件夹中的 evaluation.json 文件中。

预测:

这一步使用预训练模型预测新数据。这由 igel 自动完成,你只需提供预测数据的路径即可。

$ igel predict -dp path_to_the_new_dataset

Igel 使用预训练模型执行预测,并将其保存在 model_results 文件夹中的 predictions.csv 文件中。

高阶用法

你还可以通过在 yaml 文件中提供某些预处理方法或其他操作来执行它们。关于 yaml 配置文件请参考 GitHub 详细介绍。在下面的示例中,将数据拆分为训练集 80%,验证 / 测试集 20%。同样,数据在拆分时会被打乱。

此外,可以通过用均值替换缺失值来对数据进行预处理:

# dataset operations
dataset:
    split:
        test_size: 0.2
        shuffle: True
        stratify: default

    preprocess: # preprocessing options
        missing_values: mean    # other possible values: [drop, median, most_frequent, constant] check the docs for more
        encoding:
            type: oneHotEncoding  # other possible values: [labelEncoding]
        scale:  # scaling options
            method: standard    # standardization will scale values to have a 0 mean and 1 standard deviation  | you can also try minmax
            target: inputs  # scale inputs. | other possible values: [outputs, all] # if you choose all then all values in the dataset will be scaled

# model definition
model:
    type: classification
    algorithm: RandomForest
    arguments:
        # notice that this is the available args for the random forest model. check different available args for all supported models by running igel help
        n_estimators: 100
        max_depth: 20

# target you want to predict
target:
    - sick

然后,可以通过运行 igel 命令来拟合模型:

$ igel fit -dp path_to_the_dataset -yml path_to_the_yaml_file

评估:

$ igel evaluate -dp path_to_the_evaluation_dataset

预测:

$ igel predict -dp path_to_the_new_dataset

感谢“机器之心”的无私奉献!

/End.


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