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Code  ›  auto-sklearn源码阅读(1):超参空间的构造_wx61090d1892228的技术博客_
random libsvm adaboost
https://blog.51cto.com/u_15321505/3261327
买醉的铁链
1 年前
源码哥的烦恼

这天，源码哥的老板丢给源码哥一个Excel表，说：

“小猿，这里有两个表，一个表是上个月用户的行为特征和留存情况，一个是这个月的用户特征，写个算法预测一下这个月的用户留存率，下班前给我。”

只见源码哥推了推眼镜，撸了撸衣袖，心想：不就是一个二分类监督学习任务吗？这有何难？ Jupyter启动 ！

可是源码哥打开Excel就犯了难，表格中有些用户的特征是有缺失的。只见源码哥手忙脚乱地用 sklearn.impute.SimpleImputer 处理完缺失值，又开始为用哪个分类器去拟合数据犯了难：”XGBoost好，SVM快，到底用哪个呢？“

源码哥靠丢硬币选择了一个模型，又开始肝起了超参：”SVM光是kernel就有4个，该从哪个开始呢？“

只见源码哥把键盘一摔，说：”这破程，老子不编了！ Jupyter ， 关闭 ！“

auto-sklearn的功能与作用域

虽然上文只是个段子，但这也许就是许多数据科学家和算法工程师的日常：数据预处理（缺失值处理、category数据独热码编码），特征预处理（特征缩放，重要特征选择，数据降维），模型选择（SVM，RandomForest），模型超参数优化。

那么问题来了，有什么方法能将上述过程自动呢？下面，就有请本文今天的主角登场： auto-sklearn !

auto-sklearn ( https://github.com/automl/auto-sklearn )是automl团队( http://www.automl.org/ )于2015年推出的一款自动化机器学习框架，它的 功能 是在Tabular Data的数据上，构件一条囊括 数据处理、特征处理、模型选择，模型超参优化的自动化机器学习Pipeline，简单来说，就是给他一个菜单，他自己会买菜，切菜，烹饪，最后把菜端到你面前让你品尝的贴心管家。

而 auto-sklearn 的作用域，就是 Tabular Data 上的 监督学习 。

Tabular Data : 每一行为一个样本，每列为特征。如果每个样本都有对应的标签（label），为 监督学习 。如果每个样本没有标签，为 无监督学习 。如果标签是连续的，例如根据一些特征预测某地的房价，称为 回归任务 。如果标签是离散的，例如根据一些特征判断西瓜的好坏，称为 分类任务 。

从一个例子入手

源码哥在关注了 人工智能源码阅读 ( aicodereview )公众号，看了这篇文章之后，不禁欣喜若狂：

”自从有了auto-sklearn，妈妈再也不用担心我的模型！“

于是源码哥在 ipython 中按照官方文档的介绍写下了如下代码： 

>>> import autosklearn.classification
>>> import sklearn.model_selection
>>> import pandas as pd
>>> import sklearn.metrics
>>> df = pd.read_excel("上月用户留存情况.xlsx")
>>> y = df.pop("用户是否留存").values
>>> X = df.values
>>> X_train, X_test, y_train, y_test = \
        sklearn.model_selection.train_test_split(X, y, random_state=1)
>>> automl = autosklearn.classification.AutoSklearnClassifier()
>>> automl.fit(X_train, y_train)
>>> y_hat = automl.predict(X_test)
>>> print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_hat))
 
Document Example: https://automl.github.io/auto-sklearn/master/#example
 

看着电脑屏幕上模型的指标不断地提升，源码哥不禁畅想起了未来：
 

出任CTO，迎娶白富美，走上人生巅峰…
 
auto-sklearn 的 Pipeline 

第二天，在得到了老板的表扬之后，源码哥不禁对auto-sklearn的底层原理产生了兴趣。到底是什么样的代码，能将数据和模型治理得服服帖帖呢？
 

在阅读了auto-sklearn的论文(http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf)之后，源码哥说：”auto-sklearn也没什么神奇的嘛，不就是个水管工吗“
auto-sklearn源码阅读(1):超参空间的构造_github_03
 为什么这么说呢？因为如果把数据想象成自来水，把数据预处理、特征预处理、估计器都想象成水管，模型的表现想象成水的流速，问题其实很简单，那就是选择什么样的水管（算法选择），将水管上的阀门拧到什么样的位置（超参选择），能让水流的最快。
 

但是两个不同的水管也许单独用起来都不咋地，但是拼在一起都奏效了，这就是一个组合问题了。
 

上图为论文中的图片，我们可以看到，数据的balacing我们可以选择做与不做，缺失值填充可以选择用中位数(median)或者平均数(mean)填充… ，最后的那根”管子“：估计器(estimator)是一根特殊的管子，他不仅要”选择什么样的水管“（算法选择）,还要知道”将水管上的阀门拧到什么样的位置“（超参选择）。
 
优化过程：贝叶斯优化 

搞懂了Pipeline的构建过程，源码哥不禁开始思考另外一个问题：这么多参数组合在一起，搜索空间将是一个非常巨大的空间，会出现组合爆炸的问题，auto-sklearn难道会像瞎猫抓耗子一样，到处乱撞吗？
 

源码哥的同事，算法大神小聪看出了源码哥的疑虑，对他说：当然不会随机搜索了，在auto-sklearn中，过去的搜索会对决定未来的搜索方向的。
 

小聪说，auto-sklearn用的是smac(https://github.com/automl/SMAC3)算法，是贝叶斯优化算法的一种。算法在刚初始化时，的确类似随机搜索，但是随着搜索的进行，算法知道的信息越来越多，就像诸葛亮一样，能预知下一次搜索哪个点模型的表现会最好。
 
 borgwang.github.io

小聪拿出一幅图对源码哥说：图中浅蓝色的表示95%置信区间的上下界，越宽表示对某个点预测的标准差越大，表示对这个点越不确定，就像是个臭皮匠。但随着搜索过的点越来越多，历史点（红叉）附近的标准差就会降低，表示对附近的点越确定，就像是个诸葛亮。就这样，三个臭皮匠，凑成一个诸葛亮，这个诸葛亮会拟合出一个参数空间映射到模型表现的函数，从这个空间中找一个点，作为下次的搜索点。
 

源码哥恍然大悟。但他还是追问了一句：这些都具体是怎么实现的呢？
 

小聪说：看完硬核部分，你就懂了。
 

基本运行入口：autosklearn.automl.AutoMLClassifier#fit
 在子类配置了一些必要的参数之后，调用父类的fit方法，即autosklearn.automl.AutoML#fit
 在调用loaded_data_manager = XYDataManager(...将X y进行管理之后，调用return self._fit(...
 
创建搜索空间 

self.configuration_space, configspace_path = self._create_search_space(
 进入对应区域：autosklearn.automl.AutoML#_create_search_space
 看到configuration_space = pipeline.get_configuration_space(
 进入对应区域：autosklearn.util.pipeline.get_configuration_space
 在这个函数中，配置了info字典之后，最后一段代码：
 

    if info['task'] in REGRESSION_TASKS:
        return _get_regression_configuration_space(info, include, exclude)
    else:
        return _get_classification_configuration_space(info, include, exclude)

  • 进入对应区域：autosklearn.util.pipeline._get_classification_configuration_space

最后一段代码：
 

    return SimpleClassificationPipeline(
        dataset_properties=dataset_properties,
        include=include, exclude=exclude).\
        get_hyperparameter_search_space()

  • 进入对应区域：autosklearn.pipeline.base.BasePipeline#get_hyperparameter_search_space

最后一段代码：
 

        if not hasattr(self, 'config_space') or self.config_space is None:
            self.config_space = self._get_hyperparameter_search_space(
                include=self.include_, exclude=self.exclude_,
                dataset_properties=self.dataset_properties_)
        return self.config_space

  • 进入对应区域：autosklearn.pipeline.classification.SimpleClassificationPipeline#_get_hyperparameter_search_space

至此，进过多次跳转与入栈，我们终于进入了”干货“最为丰富的区域了。
 看到如下代码：
 

        cs = self._get_base_search_space(
            cs=cs, dataset_properties=dataset_properties,
            exclude=exclude, include=include, pipeline=self.steps)

注意，这里的self.steps表示autosklearn想要优化出的Pipeline的所有节点。
 

  • 进入对应区域：autosklearn.pipeline.base.BasePipeline#_get_base_search_space

看到要获取matches，我们想知道matches是怎么来的：
 

  • 进入对应区域：autosklearn.pipeline.create_searchspace_util.get_match_array

在for node_name, node in pipeline:这个循环中，构造了一个很重要的变量：node_i_choices，他是一个2维列表。在原生形式中，维度1为7，表示7个Pipeline的结点。其中每个子列表表示可以选择的所有option
 我取前4个作为样例
 

node_i_choices[0]
Out[16]: 
[autosklearn.pipeline.components.data_preprocessing.one_hot_encoding.no_encoding.NoEncoding,
 autosklearn.pipeline.components.data_preprocessing.one_hot_encoding.one_hot_encoding.OneHotEncoder]
node_i_choices[1]
Out[17]: [Imputation(random_state=None, strategy='median')]
node_i_choices[2]
Out[18]: [VarianceThreshold(random_state=None)]
node_i_choices[3]
Out[19]: 
[autosklearn.pipeline.components.data_preprocessing.rescaling.minmax.MinMaxScalerComponent,
 autosklearn.pipeline.components.data_preprocessing.rescaling.none.NoRescalingComponent,
 autosklearn.pipeline.components.data_preprocessing.rescaling.normalize.NormalizerComponent,
 autosklearn.pipeline.components.data_preprocessing.rescaling.quantile_transformer.QuantileTransformerComponent,
 autosklearn.pipeline.components.data_preprocessing.rescaling.robust_scaler.RobustScalerComponent,
 autosklearn.pipeline.components.data_preprocessing.rescaling.standardize.StandardScalerComponent]

之后，matches_dimensions表示每个子列表的长度，用来构造一个高维张量matches
 

matches_dimensions
Out[20]: [2, 1, 1, 6, 1, 15, 15]

matches = np.ones(matches_dimensions, dtype=int)

    pipeline_idxs = [range(dim) for dim in matches_dimensions]
    for pipeline_instantiation_idxs in itertools.product(*pipeline_idxs):

可以理解为遍历这条Pipeline中所有的可能。
pipeline_instantiation_idxs表示某个Pipeline在matches中的坐标
 

pipeline_instantiation_idxs
Out[25]: (0, 0, 0, 0, 0, 0, 0)

            node_input = node.get_properties()['input']
            node_output = node.get_properties()['output']

node_input
Out[26]: (5, 6, 10)
node_output
Out[27]: (8,)

这个操作乍一看不理解，跳转get_properties函数我们看到：
 

                'input': (DENSE, SPARSE, UNSIGNED_DATA),
                'output': (PREDICTIONS,)}

应该是适应哪些类型。
 首先判断sparse与dense是否check：
 

            # First check if these two instantiations of this node can work
            # together. Do this in multiple if statements to maintain
            # readability
            if (data_is_sparse and SPARSE not in node_input) or \
                    not data_is_sparse and DENSE not in node_input:
                matches[pipeline_instantiation_idxs] = 0
                break

            # No need to check if the node can handle SIGNED_DATA; this is
            # always assumed to be true
            elif not dataset_is_signed and UNSIGNED_DATA not in node_input:
                matches[pipeline_instantiation_idxs] = 0
                break

后面的操作也差不多，反正就是检查这个Pipeline是否合理。源码很sophisticated，我暂时跳过。
 最后返回matches
 

  • 返回对应区域：autosklearn.pipeline.base.BasePipeline#_get_base_search_space:293

            if not is_choice:
                cs.add_configuration_space(node_name,
                                           node.get_hyperparameter_search_space(dataset_properties))
            # If the node isn't a choice, we have to figure out which of it's
            #  choices are actually legal choices
            else:
                choices_list = \
                    autosklearn.pipeline.create_searchspace_util.find_active_choices(
                        matches, node, node_idx,
                        dataset_properties,
                        include.get(node_name),
                        exclude.get(node_name)
                sub_config_space = node.get_hyperparameter_search_space(
                    dataset_properties, include=choices_list)
                cs.add_configuration_space(node_name, sub_config_space)

如果是选择性的结点，则进入else的部分，choices_list是所有的候选项
 

choices_list
Out[29]: ['no_encoding', 'one_hot_encoding']

我们再打印一下
 

sub_config_space
Out[30]: 
Configuration space object:
  Hyperparameters:
    __choice__, Type: Categorical, Choices: {no_encoding, one_hot_encoding}, Default: one_hot_encoding
    one_hot_encoding:minimum_fraction, Type: UniformFloat, Range: [0.0001, 0.5], Default: 0.01, on log-scale
    one_hot_encoding:use_minimum_fraction, Type: Categorical, Choices: {True, False}, Default: True
  Conditions:
    one_hot_encoding:minimum_fraction | one_hot_encoding:use_minimum_fraction == 'True'
    one_hot_encoding:use_minimum_fraction | __choice__ == 'one_hot_encoding'

我们打印一下特征处理部分：
 

Configuration space object:
  Hyperparameters:
    __choice__, Type: Categorical, Choices: {extra_trees_preproc_for_classification, fast_ica, feature_agglomeration, kernel_pca, kitchen_sinks, liblinear_svc_preprocessor, no_preprocessing, nystroem_sampler, pca, polynomial, random_trees_embedding, select_percentile_classification, select_rates}, Default: no_preprocessing
    extra_trees_preproc_for_classification:bootstrap, Type: Categorical, Choices: {True, False}, Default: False
    extra_trees_preproc_for_classification:criterion, Type: Categorical, Choices: {gini, entropy}, Default: gini
    extra_trees_preproc_for_classification:max_depth, Type: Constant, Value: None
    extra_trees_preproc_for_classification:max_features, Type: UniformFloat, Range: [0.0, 1.0], Default: 0.5
    extra_trees_preproc_for_classification:max_leaf_nodes, Type: Constant, Value: None
    extra_trees_preproc_for_classification:min_impurity_decrease, Type: Constant, Value: 0.0
    extra_trees_preproc_for_classification:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1
    extra_trees_preproc_for_classification:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2
    extra_trees_preproc_for_classification:min_weight_fraction_leaf, Type: Constant, Value: 0.0
    extra_trees_preproc_for_classification:n_estimators, Type: Constant, Value: 100
    fast_ica:algorithm, Type: Categorical, Choices: {parallel, deflation}, Default: parallel
    fast_ica:fun, Type: Categorical, Choices: {logcosh, exp, cube}, Default: logcosh
    fast_ica:n_components, Type: UniformInteger, Range: [10, 2000], Default: 100
    fast_ica:whiten, Type: Categorical, Choices: {False, True}, Default: False
    feature_agglomeration:affinity, Type: Categorical, Choices: {euclidean, manhattan, cosine}, Default: euclidean
    feature_agglomeration:linkage, Type: Categorical, Choices: {ward, complete, average}, Default: ward
    feature_agglomeration:n_clusters, Type: UniformInteger, Range: [2, 400], Default: 25
    feature_agglomeration:pooling_func, Type: Categorical, Choices: {mean, median, max}, Default: mean
    kernel_pca:coef0, Type: UniformFloat, Range: [-1.0, 1.0], Default: 0.0
    kernel_pca:degree, Type: UniformInteger, Range: [2, 5], Default: 3
    kernel_pca:gamma, Type: UniformFloat, Range: [3.0517578125e-05, 8.0], Default: 1.0, on log-scale
    kernel_pca:kernel, Type: Categorical, Choices: {poly, rbf, sigmoid, cosine}, Default: rbf
    kernel_pca:n_components, Type: UniformInteger, Range: [10, 2000], Default: 100
    kitchen_sinks:gamma, Type: UniformFloat, Range: [3.0517578125e-05, 8.0], Default: 1.0, on log-scale
    kitchen_sinks:n_components, Type: UniformInteger, Range: [50, 10000], Default: 100, on log-scale
    liblinear_svc_preprocessor:C, Type: UniformFloat, Range: [0.03125, 32768.0], Default: 1.0, on log-scale
    liblinear_svc_preprocessor:dual, Type: Constant, Value: False
    liblinear_svc_preprocessor:fit_intercept, Type: Constant, Value: True
    liblinear_svc_preprocessor:intercept_scaling, Type: Constant, Value: 1
    liblinear_svc_preprocessor:loss, Type: Categorical, Choices: {hinge, squared_hinge}, Default: squared_hinge
    liblinear_svc_preprocessor:multi_class, Type: Constant, Value: ovr
    liblinear_svc_preprocessor:penalty, Type: Constant, Value: l1
    liblinear_svc_preprocessor:tol, Type: UniformFloat, Range: [1e-05, 0.1], Default: 0.0001, on log-scale
    nystroem_sampler:coef0, Type: UniformFloat, Range: [-1.0, 1.0], Default: 0.0
    nystroem_sampler:degree, Type: UniformInteger, Range: [2, 5], Default: 3
    nystroem_sampler:gamma, Type: UniformFloat, Range: [3.0517578125e-05, 8.0], Default: 0.1, on log-scale
    nystroem_sampler:kernel, Type: Categorical, Choices: {poly, rbf, sigmoid, cosine}, Default: rbf
    nystroem_sampler:n_components, Type: UniformInteger, Range: [50, 10000], Default: 100, on log-scale
    pca:keep_variance, Type: UniformFloat, Range: [0.5, 0.9999], Default: 0.9999
    pca:whiten, Type: Categorical, Choices: {False, True}, Default: False
    polynomial:degree, Type: UniformInteger, Range: [2, 3], Default: 2
    polynomial:include_bias, Type: Categorical, Choices: {True, False}, Default: True
    polynomial:interaction_only, Type: Categorical, Choices: {False, True}, Default: False
    random_trees_embedding:bootstrap, Type: Categorical, Choices: {True, False}, Default: True
    random_trees_embedding:max_depth, Type: UniformInteger, Range: [2, 10], Default: 5
    random_trees_embedding:max_leaf_nodes, Type: Constant, Value: None
    random_trees_embedding:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1
    random_trees_embedding:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2
    random_trees_embedding:min_weight_fraction_leaf, Type: Constant, Value: 1.0
    random_trees_embedding:n_estimators, Type: UniformInteger, Range: [10, 100], Default: 10
    select_percentile_classification:percentile, Type: UniformFloat, Range: [1.0, 99.0], Default: 50.0
    select_percentile_classification:score_func, Type: Categorical, Choices: {chi2, f_classif, mutual_info}, Default: chi2
    select_rates:alpha, Type: UniformFloat, Range: [0.01, 0.5], Default: 0.1
    select_rates:mode, Type: Categorical, Choices: {fpr, fdr, fwe}, Default: fpr
    select_rates:score_func, Type: Categorical, Choices: {chi2, f_classif}, Default: chi2
  Conditions:
    extra_trees_preproc_for_classification:bootstrap | __choice__ == 'extra_trees_preproc_for_classification'
    extra_trees_preproc_for_classification:criterion | __choice__ == 'extra_trees_preproc_for_classification'
    extra_trees_preproc_for_classification:max_depth | __choice__ == 'extra_trees_preproc_for_classification'
    extra_trees_preproc_for_classification:max_features | __choice__ == 'extra_trees_preproc_for_classification'
    extra_trees_preproc_for_classification:max_leaf_nodes | __choice__ == 'extra_trees_preproc_for_classification'
    extra_trees_preproc_for_classification:min_impurity_decrease | __choice__ == 'extra_trees_preproc_for_classification'
    extra_trees_preproc_for_classification:min_samples_leaf | __choice__ == 'extra_trees_preproc_for_classification'
    extra_trees_preproc_for_classification:min_samples_split | __choice__ == 'extra_trees_preproc_for_classification'
    extra_trees_preproc_for_classification:min_weight_fraction_leaf | __choice__ == 'extra_trees_preproc_for_classification'
    extra_trees_preproc_for_classification:n_estimators | __choice__ == 'extra_trees_preproc_for_classification'
    fast_ica:algorithm | __choice__ == 'fast_ica'
    fast_ica:fun | __choice__ == 'fast_ica'
    fast_ica:n_components | fast_ica:whiten == 'True'
    fast_ica:whiten | __choice__ == 'fast_ica'
    feature_agglomeration:affinity | __choice__ == 'feature_agglomeration'
    feature_agglomeration:linkage | __choice__ == 'feature_agglomeration'
    feature_agglomeration:n_clusters | __choice__ == 'feature_agglomeration'
    feature_agglomeration:pooling_func | __choice__ == 'feature_agglomeration'
    kernel_pca:degree | kernel_pca:kernel == 'poly'
    kernel_pca:kernel | __choice__ == 'kernel_pca'
    kernel_pca:n_components | __choice__ == 'kernel_pca'
    kitchen_sinks:gamma | __choice__ == 'kitchen_sinks'
    kitchen_sinks:n_components | __choice__ == 'kitchen_sinks'
    liblinear_svc_preprocessor:C | __choice__ == 'liblinear_svc_preprocessor'
    liblinear_svc_preprocessor:dual | __choice__ == 'liblinear_svc_preprocessor'
    liblinear_svc_preprocessor:fit_intercept | __choice__ == 'liblinear_svc_preprocessor'
    liblinear_svc_preprocessor:intercept_scaling | __choice__ == 'liblinear_svc_preprocessor'
    liblinear_svc_preprocessor:loss | __choice__ == 'liblinear_svc_preprocessor'
    liblinear_svc_preprocessor:multi_class | __choice__ == 'liblinear_svc_preprocessor'
    liblinear_svc_preprocessor:penalty | __choice__ == 'liblinear_svc_preprocessor'
    liblinear_svc_preprocessor:tol | __choice__ == 'liblinear_svc_preprocessor'
    nystroem_sampler:degree | nystroem_sampler:kernel == 'poly'
    nystroem_sampler:kernel | __choice__ == 'nystroem_sampler'
    nystroem_sampler:n_components | __choice__ == 'nystroem_sampler'
    pca:keep_variance | __choice__ == 'pca'
    pca:whiten | __choice__ == 'pca'
    polynomial:degree | __choice__ == 'polynomial'
    polynomial:include_bias | __choice__ == 'polynomial'
    polynomial:interaction_only | __choice__ == 'polynomial'
    preprocessor:kernel_pca:coef0 | preprocessor:kernel_pca:kernel in {'poly', 'sigmoid'}
    preprocessor:kernel_pca:gamma | preprocessor:kernel_pca:kernel in {'poly', 'rbf'}
    preprocessor:nystroem_sampler:coef0 | preprocessor:nystroem_sampler:kernel in {'poly', 'sigmoid'}
    preprocessor:nystroem_sampler:gamma | preprocessor:nystroem_sampler:kernel in {'poly', 'rbf', 'sigmoid'}
    random_trees_embedding:bootstrap | __choice__ == 'random_trees_embedding'
    random_trees_embedding:max_depth | __choice__ == 'random_trees_embedding'
    random_trees_embedding:max_leaf_nodes | __choice__ == 'random_trees_embedding'
    random_trees_embedding:min_samples_leaf | __choice__ == 'random_trees_embedding'
    random_trees_embedding:min_samples_split | __choice__ == 'random_trees_embedding'
    random_trees_embedding:min_weight_fraction_leaf | __choice__ == 'random_trees_embedding'
    random_trees_embedding:n_estimators | __choice__ == 'random_trees_embedding'
    select_percentile_classification:percentile | __choice__ == 'select_percentile_classification'
    select_percentile_classification:score_func | __choice__ == 'select_percentile_classification'
    select_rates:alpha | __choice__ == 'select_rates'
    select_rates:mode | __choice__ == 'select_rates'
    select_rates:score_func | __choice__ == 'select_rates'
  Forbidden Clauses:
    (Forbidden: preprocessor:feature_agglomeration:affinity in {'cosine', 'manhattan'} && Forbidden: preprocessor:feature_agglomeration:linkage == 'ward')
    (Forbidden: preprocessor:liblinear_svc_preprocessor:penalty == 'l1' && Forbidden: preprocessor:liblinear_svc_preprocessor:loss == 'hinge')

我们打印一下模型超参部分：




    

 

Configuration space object:
  Hyperparameters:
    __choice__, Type: Categorical, Choices: {adaboost, bernoulli_nb, decision_tree, extra_trees, gaussian_nb, gradient_boosting, k_nearest_neighbors, lda, liblinear_svc, libsvm_svc, multinomial_nb, passive_aggressive, qda, random_forest, sgd}, Default: random_forest
    adaboost:algorithm, Type: Categorical, Choices: {SAMME.R, SAMME}, Default: SAMME.R
    adaboost:learning_rate, Type: UniformFloat, Range: [0.01, 2.0], Default: 0.1, on log-scale
    adaboost:max_depth, Type: UniformInteger, Range: [1, 10], Default: 1
    adaboost:n_estimators, Type: UniformInteger, Range: [50, 500], Default: 50
    bernoulli_nb:alpha, Type: UniformFloat, Range: [0.01, 100.0], Default: 1.0, on log-scale
    bernoulli_nb:fit_prior, Type: Categorical, Choices: {True, False}, Default: True
    decision_tree:criterion, Type: Categorical, Choices: {gini, entropy}, Default: gini
    decision_tree:max_depth_factor, Type: UniformFloat, Range: [0.0, 2.0], Default: 0.5
    decision_tree:max_features, Type: Constant, Value: 1.0
    decision_tree:max_leaf_nodes, Type: Constant, Value: None
    decision_tree:min_impurity_decrease, Type: Constant, Value: 0.0
    decision_tree:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1
    decision_tree:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2
    decision_tree:min_weight_fraction_leaf, Type: Constant, Value: 0.0
    extra_trees:bootstrap, Type: Categorical, Choices: {True, False}, Default: False
    extra_trees:criterion, Type: Categorical, Choices: {gini, entropy}, Default: gini
    extra_trees:max_depth, Type: Constant, Value: None
    extra_trees:max_features, Type: UniformFloat, Range: [0.0, 1.0], Default: 0.5
    extra_trees:max_leaf_nodes, Type: Constant, Value: None
    extra_trees:min_impurity_decrease, Type: Constant, Value: 0.0
    extra_trees:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1
    extra_trees:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2
    extra_trees:min_weight_fraction_leaf, Type: Constant, Value: 0.0
    extra_trees:n_estimators, Type: Constant, Value: 100
    gradient_boosting:early_stop, Type: Categorical, Choices: {off, train, valid}, Default: off
    gradient_boosting:l2_regularization, Type: UniformFloat, Range: [1e-10, 1.0], Default: 1e-10, on log-scale
    gradient_boosting:learning_rate, Type: UniformFloat, Range: [0.01, 1.0], Default: 0.1, on log-scale
    gradient_boosting:loss, Type: Constant, Value: auto
    gradient_boosting:max_bins, Type: Constant, Value: 256
    gradient_boosting:max_depth, Type: Constant, Value: None
    gradient_boosting:max_iter, Type: UniformInteger, Range: [32, 512], Default: 100
    gradient_boosting:max_leaf_nodes, Type: UniformInteger, Range: [3, 2047], Default: 31, on log-scale
    gradient_boosting:min_samples_leaf, Type: UniformInteger, Range: [1, 200], Default: 20, on log-scale
    gradient_boosting:n_iter_no_change, Type: UniformInteger, Range: [1, 20], Default: 10
    gradient_boosting:scoring, Type: Constant, Value: loss
    gradient_boosting:tol, Type: Constant, Value: 1e-07
    gradient_boosting:validation_fraction, Type: UniformFloat, Range: [0.01, 0.4], Default: 0.1
    k_nearest_neighbors:n_neighbors, Type: UniformInteger, Range: [1, 100], Default: 1, on log-scale
    k_nearest_neighbors:p, Type: Categorical, Choices: {1, 2}, Default: 2
    k_nearest_neighbors:weights, Type: Categorical, Choices: {uniform, distance}, Default: uniform
    lda:n_components, Type: UniformInteger, Range: [1, 250], Default: 10
    lda:shrinkage, Type: Categorical, Choices: {None, auto, manual}, Default: None
    lda:shrinkage_factor, Type: UniformFloat, Range: [0.0, 1.0], Default: 0.5
    lda:tol, Type: UniformFloat, Range: [1e-05, 0.1], Default: 0.0001, on log-scale
    liblinear_svc:C, Type: UniformFloat, Range: [0.03125, 32768.0], Default: 1.0, on log-scale
    liblinear_svc:dual, Type: Constant, Value: False
    liblinear_svc:fit_intercept, Type: Constant, Value: True
    liblinear_svc:intercept_scaling, Type: Constant, Value: 1
    liblinear_svc:loss, Type: Categorical, Choices: {hinge, squared_hinge}, Default: squared_hinge
    liblinear_svc:multi_class, Type: Constant, Value: ovr
    liblinear_svc:penalty, Type: Categorical, Choices: {l1, l2}, Default: l2
    liblinear_svc:tol, Type: UniformFloat, Range: [1e-05, 0.1], Default: 0.0001, on log-scale
    libsvm_svc:C, Type: UniformFloat, Range: [0.03125, 32768.0], Default: 1.0, on log-scale
    libsvm_svc:coef0, Type: UniformFloat, Range: [-1.0, 1.0], Default: 0.0
    libsvm_svc:degree, Type: UniformInteger, Range: [2, 5], Default: 3
    libsvm_svc:gamma, Type: UniformFloat, Range: [3.0517578125e-05, 8.0], Default: 0.1, on log-scale
    libsvm_svc:kernel, Type: Categorical, Choices: {rbf, poly, sigmoid}, Default: rbf
    libsvm_svc:max_iter, Type: Constant, Value: -1
    libsvm_svc:shrinking, Type: Categorical, Choices: {True, False}, Default: True
    libsvm_svc:tol, Type: UniformFloat, Range: [1e-05, 0.1], Default: 0.001, on log-scale
    multinomial_nb:alpha, Type: UniformFloat, Range: [0.01, 100.0], Default: 1.0, on log-scale
    multinomial_nb:fit_prior, Type: Categorical, Choices: {True, False}, Default: True
    passive_aggressive:C, Type: UniformFloat, Range: [1e-05, 10.0], Default: 1.0, on log-scale
    passive_aggressive:average, Type: Categorical, Choices: {False, True}, Default: False
    passive_aggressive:fit_intercept, Type: Constant, Value: True
    passive_aggressive:loss, Type: Categorical, Choices: {hinge, squared_hinge}, Default: hinge
    passive_aggressive:tol, Type: UniformFloat, Range: [1e-05, 0.1], Default: 0.0001, on log-scale
    qda:reg_param, Type: UniformFloat, Range: [0.0, 1.0], Default: 0.0
    random_forest:bootstrap, Type: Categorical, Choices: {True, False}, Default: True
    random_forest:criterion, Type: Categorical, Choices: {gini, entropy}, Default: gini
    random_forest:max_depth, Type: Constant, Value: None
    random_forest:max_features, Type: UniformFloat, Range: [0.0, 1.0], Default: 0.5
    random_forest:max_leaf_nodes, Type: Constant, Value: None
    random_forest:min_impurity_decrease, Type: Constant, Value: 0.0
    random_forest:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1
    random_forest:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2
    random_forest:min_weight_fraction_leaf, Type: Constant, Value: 0.0
    random_forest:n_estimators, Type: Constant, Value: 100
    sgd:alpha, Type: UniformFloat, Range: [1e-07, 0.1], Default: 0.0001, on log-scale
    sgd:average, Type: Categorical, Choices: {False, True}, Default: False
    sgd:epsilon, Type: UniformFloat, Range: [1e-05, 0.1], Default: 0.0001, on log-scale
    sgd:eta0, Type: UniformFloat, Range: [1e-07, 0.1], Default: 0.01, on log-scale
    sgd:fit_intercept, Type: Constant, Value: True
    sgd:l1_ratio, Type: UniformFloat, Range: [1e-09, 1.0], Default: 0.15, on log-scale
    sgd:learning_rate, Type: Categorical, Choices: {optimal, invscaling, constant}, Default: invscaling
    sgd:loss, Type: Categorical, Choices: {hinge, log, modified_huber, squared_hinge, perceptron}, Default: log
    sgd:penalty, Type: Categorical, Choices: {l1, l2, elasticnet}, Default: l2
    sgd:power_t, Type: UniformFloat, Range: [1e-05, 1.0], Default: 0.5
    sgd:tol, Type: UniformFloat, Range: [1e-05, 0.1], Default: 0.0001, on log-scale
  Conditions:
    adaboost:algorithm | __choice__ == 'adaboost'
    adaboost:learning_rate | __choice__ == 'adaboost'
    adaboost:max_depth | __choice__ == 'adaboost'
    adaboost:n_estimators | __choice__ == 'adaboost'
    bernoulli_nb:alpha | __choice__ == 'bernoulli_nb'
    bernoulli_nb:fit_prior | __choice__ == 'bernoulli_nb'
    decision_tree:criterion | __choice__ == 'decision_tree'
    decision_tree:max_depth_factor | __choice__ == 'decision_tree'
    decision_tree:max_features | __choice__ == 'decision_tree'
    decision_tree:max_leaf_nodes | __choice__ == 'decision_tree'
    decision_tree:min_impurity_decrease | __choice__ == 'decision_tree'
    decision_tree:min_samples_leaf | __choice__ == 'decision_tree'
    decision_tree:min_samples_split | __choice__ == 'decision_tree'
    decision_tree:min_weight_fraction_leaf | __choice__ == 'decision_tree'
    extra_trees:bootstrap | __choice__ == 'extra_trees'
    extra_trees:criterion | __choice__ == 'extra_trees'
    extra_trees:max_depth | __choice__ == 'extra_trees'
    extra_trees:max_features | __choice__ == 'extra_trees'
    extra_trees:max_leaf_nodes | __choice__ == 'extra_trees'
    extra_trees:min_impurity_decrease | __choice__ == 'extra_trees'
    extra_trees:min_samples_leaf | __choice__ == 'extra_trees'
    extra_trees:min_samples_split | __choice__ == 'extra_trees'
    extra_trees:min_weight_fraction_leaf | __choice__ == 'extra_trees'
    extra_trees:n_estimators | __choice__ == 'extra_trees'
    gradient_boosting:early_stop | __choice__ == 'gradient_boosting'
    gradient_boosting:l2_regularization | __choice__ == 'gradient_boosting'
    gradient_boosting:learning_rate | __choice__ == 'gradient_boosting'
    gradient_boosting:loss | __choice__ == 'gradient_boosting'
    gradient_boosting:max_bins | __choice__ == 'gradient_boosting'
    gradient_boosting:max_depth | __choice__ == 'gradient_boosting'
    gradient_boosting:max_iter | __choice__ == 'gradient_boosting'
    gradient_boosting:max_leaf_nodes | __choice__ == 'gradient_boosting'
    gradient_boosting:min_samples_leaf | __choice__ == 'gradient_boosting'
    gradient_boosting:n_iter_no_change | gradient_boosting:early_stop in {'valid', 'train'}
    gradient_boosting:scoring | __choice__ == 'gradient_boosting'
    gradient_boosting:tol | __choice__ == 'gradient_boosting'
    gradient_boosting:validation_fraction | gradient_boosting:early_stop == 'valid'
    k_nearest_neighbors:n_neighbors | __choice__ == 'k_nearest_neighbors'
    k_nearest_neighbors:p | __choice__ == 'k_nearest_neighbors'
    k_nearest_neighbors:weights | __choice__ == 'k_nearest_neighbors'
    lda:n_components | __choice__ == 'lda'
    lda:shrinkage | __choice__ == 'lda'
    lda:shrinkage_factor | lda:shrinkage == 'manual'
    lda:tol | __choice__ == 'lda'
    liblinear_svc:C | __choice__ == 'liblinear_svc'
    liblinear_svc:dual | __choice__ == 'liblinear_svc'
    liblinear_svc:fit_intercept | __choice__ == 'liblinear_svc'
    liblinear_svc:intercept_scaling | __choice__ == 'liblinear_svc'
    liblinear_svc:loss | __choice__ == 'liblinear_svc'
    liblinear_svc:multi_class | __choice__ == 'liblinear_svc'
    liblinear_svc:penalty | __choice__ == 'liblinear_svc'
    liblinear_svc:tol | __choice__ == 'liblinear_svc'
    libsvm_svc:C | __choice__ == 'libsvm_svc'
    libsvm_svc:coef0 | libsvm_svc:kernel in {'poly', 'sigmoid'}
    libsvm_svc:degree | libsvm_svc:kernel == 'poly'
    libsvm_svc:gamma | __choice__ == 'libsvm_svc'
    libsvm_svc:kernel | __choice__ == 'libsvm_svc'
    libsvm_svc:max_iter | __choice__ == 'libsvm_svc'
    libsvm_svc:shrinking | __choice__ == 'libsvm_svc'
    libsvm_svc:tol | __choice__ == 'libsvm_svc'
    multinomial_nb:alpha | __choice__ == 'multinomial_nb'
    multinomial_nb:fit_prior | __choice__ == 'multinomial_nb'
    passive_aggressive:C | __choice__ == 'passive_aggressive'
    passive_aggressive:average | __choice__ == 'passive_aggressive'
    passive_aggressive:fit_intercept | __choice__ == 'passive_aggressive'
    passive_aggressive:loss | __choice__ == 'passive_aggressive'
    passive_aggressive:tol | __choice__ == 'passive_aggressive'
    qda:reg_param | __choice__ == 'qda'
    random_forest:bootstrap | __choice__ == 'random_forest'
    random_forest:criterion | __choice__ == 'random_forest'
    random_forest:max_depth | __choice__ == 'random_forest'
    random_forest:max_features | __choice__ == 'random_forest'
    random_forest:max_leaf_nodes | __choice__ == 'random_forest'
    random_forest:min_impurity_decrease | __choice__ == 'random_forest'
    random_forest:min_samples_leaf | __choice__ == 'random_forest'
    random_forest:min_samples_split | __choice__ == 'random_forest'
    random_forest:min_weight_fraction_leaf | __choice__ == 'random_forest'
    random_forest:n_estimators | __choice__ == 'random_forest'
    sgd:alpha | __choice__ == 'sgd'
    sgd:average | __choice__ == 'sgd'
    sgd:epsilon | sgd:loss == 'modified_huber'
    sgd:eta0 | sgd:learning_rate in {'invscaling', 'constant'}
    sgd:fit_intercept | __choice__ == 'sgd'
    sgd:l1_ratio | sgd:penalty == 'elasticnet'
    sgd:learning_rate | __choice__ == 'sgd'
    sgd:loss | __choice__ == 'sgd'
    sgd:penalty | __choice__ == 'sgd'
    sgd:power_t | sgd:learning_rate == 'invscaling'
    sgd:tol | __choice__ == 'sgd'
  Forbidden Clauses:
    (Forbidden: liblinear_svc:penalty == 'l1' && Forbidden: liblinear_svc:loss == 'hinge')
    (Forbidden: liblinear_svc:dual == 'False' && Forbidden: liblinear_svc:penalty == 'l2' && Forbidden: liblinear_svc:loss == 'hinge')
    (Forbidden: liblinear_svc:dual == 'False' && Forbidden: liblinear_svc:penalty == 'l1')

至此，我们基本搞定出了构造超参的方法。
 
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