有什么方法可以从 auto-sklearn 的独立 python 脚本中提取自动生成的机器学习管道？
以下是使用 auto-sklearn 的示例代码：
import autosklearn.classification
import sklearn.cross_validation
import sklearn.datasets
import sklearn.metrics
digits = sklearn.datasets.load_digits()
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = sklearn.cross_validation.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))
以某种方式生成自动等效的python代码会很好。
相比之下，使用 TPOT 时，我们可以获得如下的独立管道：
from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, train_size=0.75, test_size=0.25)
tpot = TPOTClassifier(generations=5, population_size=20, verbosity=2)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot-mnist-pipeline.py')
并且在检查tpot-mnist-pipeline.py整个 ML 管道时可以看到：
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
# NOTE: Make sure that the class is labeled 'class' in the data file
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR')
features = tpot_data.view((np.float64, len(tpot_data.dtype.names)))
features = np.delete(features, tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_classes, testing_classes = train_test_split(features, tpot_data['class'], random_state=42)
exported_pipeline = make_pipeline(
KNeighborsClassifier(n_neighbors=3, weights="uniform")
)
exported_pipeline.fit(training_features, training_classes)
results = exported_pipeline.predict(testing_features)
上面的示例与此处找到的关于自动化有点浅的机器学习的现有帖子有关。