Auto-Machine-Learning python equivalent code

Question

Is there any way to extract the auto generated machine learning pipeline in a standalone python script from auto-sklearn?

Here is a sample code using auto-sklearn:

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))

It would be nice to have automatic equivalent python code generated somehow.

By comparison, when using TPOT we can obtain the standalone pipeline as follows:

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')

And when inspecting tpot-mnist-pipeline.py the entire ML pipeline can be seen:

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)

The examples above are in relation to an exiting post on automating somewhat shallow machine learning found here.

Answer 1

There is no automated way. You can store the object in pickle format and load later.

with open('automl.pkl', 'wb') as output:
    pickle.dump(automl,output)

You can debug the fit or predict methods and see what is going on.