TPOT机器学习

Question

我在Google上训练了一个回归TPOT算法，其中TPOT过程的输出是一些锅炉板Python代码，如下所示。

import numpy as np
import pandas as pd
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, make_union
from tpot.builtins import StackingEstimator
from tpot.export_utils import set_param_recursive

# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \
            train_test_split(features, tpot_data['target'], random_state=1)

# Average CV score on the training set was: -4.881434802676966
exported_pipeline = make_pipeline(
    StackingEstimator(estimator=ExtraTreesRegressor(bootstrap=False, max_features=0.9000000000000001, min_samples_leaf=1, min_samples_split=20, n_estimators=100)),
    ExtraTreesRegressor(bootstrap=True, max_features=0.9000000000000001, min_samples_leaf=6, min_samples_split=13, n_estimators=100)
)
# Fix random state for all the steps in exported pipeline
set_param_recursive(exported_pipeline.steps, 'random_state', 1)

exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)

有人知道Sklearn管道过程是什么样子的吗？当我改进锅炉板代码并使用IPython中的数据集运行它时，我可以看到管道过程中的输出，这些都在做什么？

Pipeline(steps=[('stackingestimator-1',
                 StackingEstimator(estimator=ExtraTreesRegressor(max_features=0.6500000000000001,
                                                                 min_samples_leaf=19,
                                                                 min_samples_split=14,
                                                                 random_state=1))),
                ('maxabsscaler', MaxAbsScaler()),
                ('stackingestimator-2',
                 StackingEstimator(estimator=ExtraTreesRegressor(max_features=0.4,
                                                                 min_samples_leaf=3,
                                                                 min_samples_split=7,
                                                                 random_state=1))),
                ('adaboostregressor',
                 AdaBoostRegressor(learning_rate=0.001, loss='exponential',
                                   n_estimators=100, random_state=1))])

结果看起来很好，只是好奇的管道处理如何工作，任何提示或链接到教程非常赞赏。我认为这个机械掌握教程对任何有兴趣了解更多关于TPOT的人也有一定的帮助。

Answer 1

StackingEstimator的文档非常糟糕，但相对简单:将estimator放在数据上，并将其预测作为一个新特性添加到dataset上。来源，github问题.

因此，您的管道适合原始输入的ExtraTreesRegressor，并将其预测附加到未来的数据集中。数据(原始+1预测)被缩放，然后另一个ExtraTreesRegressor是合适的(在缩放的原始和第一preds上，并且有不同的超参数)，它的预测也被附加到数据集上。最后，AdaBoostRegressor适合于缩放-原始+缩放-1-preds+2-preds。