Question

有没有办法从auto-sklearn中提取独立python脚本中的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代码会很不错。

相比之下，当使用TPOT时，我们可以按如下方式获得独立管道：

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')

当检查tpot-mnist-pipeline.py时，可以看到整个ML管道：

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)

上面的示例与现有的有点浅层机器学习自动化的帖子有关here。

Answer 1

没有自动化方式。您可以将对象存储为pickle格式并稍后加载。

$bitfinex = DB::table('bitfinex')->select('price')->latest()->limit(1);
$bitstamp = DB::table('bitstamp')->select('price')->latest()->limit(1);

$results = $bitfinex->union($bitstamp)->get();

您可以调试拟合或预测方法，看看发生了什么。

自动机器学习python等效代码

1 个答案: