In [1]:
import autosklearn.classification
import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics

In [2]:
X, y = sklearn.datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = \
        sklearn.model_selection.train_test_split(X, y, random_state=1)

In [4]:
X_train,y_train


Out[4]:
(array([[ 6.5,  2.8,  4.6,  1.5],
        [ 6.7,  2.5,  5.8,  1.8],
        [ 6.8,  3. ,  5.5,  2.1],
        [ 5.1,  3.5,  1.4,  0.3],
        [ 6. ,  2.2,  5. ,  1.5],
        [ 6.3,  2.9,  5.6,  1.8],
        [ 6.6,  2.9,  4.6,  1.3],
        [ 7.7,  2.6,  6.9,  2.3],
        [ 5.7,  3.8,  1.7,  0.3],
        [ 5. ,  3.6,  1.4,  0.2],
        [ 4.8,  3. ,  1.4,  0.3],
        [ 5.2,  2.7,  3.9,  1.4],
        [ 5.1,  3.4,  1.5,  0.2],
        [ 5.5,  3.5,  1.3,  0.2],
        [ 7.7,  3.8,  6.7,  2.2],
        [ 6.9,  3.1,  5.4,  2.1],
        [ 7.3,  2.9,  6.3,  1.8],
        [ 6.4,  2.8,  5.6,  2.2],
        [ 6.2,  2.8,  4.8,  1.8],
        [ 6. ,  3.4,  4.5,  1.6],
        [ 7.7,  2.8,  6.7,  2. ],
        [ 5.7,  3. ,  4.2,  1.2],
        [ 4.8,  3.4,  1.6,  0.2],
        [ 5.7,  2.5,  5. ,  2. ],
        [ 6.3,  2.7,  4.9,  1.8],
        [ 4.8,  3. ,  1.4,  0.1],
        [ 4.7,  3.2,  1.3,  0.2],
        [ 6.5,  3. ,  5.8,  2.2],
        [ 4.6,  3.4,  1.4,  0.3],
        [ 6.1,  3. ,  4.9,  1.8],
        [ 6.5,  3.2,  5.1,  2. ],
        [ 6.7,  3.1,  4.4,  1.4],
        [ 5.7,  2.8,  4.5,  1.3],
        [ 6.7,  3.3,  5.7,  2.5],
        [ 6. ,  3. ,  4.8,  1.8],
        [ 5.1,  3.8,  1.6,  0.2],
        [ 6. ,  2.2,  4. ,  1. ],
        [ 6.4,  2.9,  4.3,  1.3],
        [ 6.5,  3. ,  5.5,  1.8],
        [ 5. ,  2.3,  3.3,  1. ],
        [ 6.3,  3.3,  6. ,  2.5],
        [ 5.5,  2.5,  4. ,  1.3],
        [ 5.4,  3.7,  1.5,  0.2],
        [ 4.9,  3.1,  1.5,  0.1],
        [ 5.2,  4.1,  1.5,  0.1],
        [ 6.7,  3.3,  5.7,  2.1],
        [ 4.4,  3. ,  1.3,  0.2],
        [ 6. ,  2.7,  5.1,  1.6],
        [ 6.4,  2.7,  5.3,  1.9],
        [ 5.9,  3. ,  5.1,  1.8],
        [ 5.2,  3.5,  1.5,  0.2],
        [ 5.1,  3.3,  1.7,  0.5],
        [ 5.8,  2.7,  4.1,  1. ],
        [ 4.9,  3.1,  1.5,  0.1],
        [ 7.4,  2.8,  6.1,  1.9],
        [ 6.2,  2.9,  4.3,  1.3],
        [ 7.6,  3. ,  6.6,  2.1],
        [ 6.7,  3. ,  5.2,  2.3],
        [ 6.3,  2.3,  4.4,  1.3],
        [ 6.2,  3.4,  5.4,  2.3],
        [ 7.2,  3.6,  6.1,  2.5],
        [ 5.6,  2.9,  3.6,  1.3],
        [ 5.7,  4.4,  1.5,  0.4],
        [ 5.8,  2.7,  3.9,  1.2],
        [ 4.5,  2.3,  1.3,  0.3],
        [ 5.5,  2.4,  3.8,  1.1],
        [ 6.9,  3.1,  4.9,  1.5],
        [ 5. ,  3.4,  1.6,  0.4],
        [ 6.8,  2.8,  4.8,  1.4],
        [ 5. ,  3.5,  1.6,  0.6],
        [ 4.8,  3.4,  1.9,  0.2],
        [ 6.3,  3.4,  5.6,  2.4],
        [ 5.6,  2.8,  4.9,  2. ],
        [ 6.8,  3.2,  5.9,  2.3],
        [ 5. ,  3.3,  1.4,  0.2],
        [ 5.1,  3.7,  1.5,  0.4],
        [ 5.9,  3.2,  4.8,  1.8],
        [ 4.6,  3.1,  1.5,  0.2],
        [ 5.8,  2.7,  5.1,  1.9],
        [ 4.8,  3.1,  1.6,  0.2],
        [ 6.5,  3. ,  5.2,  2. ],
        [ 4.9,  2.5,  4.5,  1.7],
        [ 4.6,  3.2,  1.4,  0.2],
        [ 6.4,  3.2,  5.3,  2.3],
        [ 4.3,  3. ,  1.1,  0.1],
        [ 5.6,  3. ,  4.1,  1.3],
        [ 4.4,  2.9,  1.4,  0.2],
        [ 5.5,  2.4,  3.7,  1. ],
        [ 5. ,  2. ,  3.5,  1. ],
        [ 5.1,  3.5,  1.4,  0.2],
        [ 4.9,  3. ,  1.4,  0.2],
        [ 4.9,  2.4,  3.3,  1. ],
        [ 4.6,  3.6,  1. ,  0.2],
        [ 5.9,  3. ,  4.2,  1.5],
        [ 6.1,  2.9,  4.7,  1.4],
        [ 5. ,  3.4,  1.5,  0.2],
        [ 6.7,  3.1,  4.7,  1.5],
        [ 5.7,  2.9,  4.2,  1.3],
        [ 6.2,  2.2,  4.5,  1.5],
        [ 7. ,  3.2,  4.7,  1.4],
        [ 5.8,  2.7,  5.1,  1.9],
        [ 5.4,  3.4,  1.7,  0.2],
        [ 5. ,  3. ,  1.6,  0.2],
        [ 6.1,  2.6,  5.6,  1.4],
        [ 6.1,  2.8,  4. ,  1.3],
        [ 7.2,  3. ,  5.8,  1.6],
        [ 5.7,  2.6,  3.5,  1. ],
        [ 6.3,  2.8,  5.1,  1.5],
        [ 6.4,  3.1,  5.5,  1.8],
        [ 6.3,  2.5,  4.9,  1.5],
        [ 6.7,  3.1,  5.6,  2.4],
        [ 4.9,  3.1,  1.5,  0.1]]),
 array([1, 2, 2, 0, 2, 2, 1, 2, 0, 0, 0, 1, 0, 0, 2, 2, 2, 2, 2, 1, 2, 1, 0,
        2, 2, 0, 0, 2, 0, 2, 2, 1, 1, 2, 2, 0, 1, 1, 2, 1, 2, 1, 0, 0, 0, 2,
        0, 1, 2, 2, 0, 0, 1, 0, 2, 1, 2, 2, 1, 2, 2, 1, 0, 1, 0, 1, 1, 0, 1,
        0, 0, 2, 2, 2, 0, 0, 1, 0, 2, 0, 2, 2, 0, 2, 0, 1, 0, 1, 1, 0, 0, 1,
        0, 1, 1, 0, 1, 1, 1, 1, 2, 0, 0, 2, 1, 2, 1, 2, 2, 1, 2, 0]))

In [5]:
X_test,y_test


Out[5]:
(array([[ 5.8,  4. ,  1.2,  0.2],
        [ 5.1,  2.5,  3. ,  1.1],
        [ 6.6,  3. ,  4.4,  1.4],
        [ 5.4,  3.9,  1.3,  0.4],
        [ 7.9,  3.8,  6.4,  2. ],
        [ 6.3,  3.3,  4.7,  1.6],
        [ 6.9,  3.1,  5.1,  2.3],
        [ 5.1,  3.8,  1.9,  0.4],
        [ 4.7,  3.2,  1.6,  0.2],
        [ 6.9,  3.2,  5.7,  2.3],
        [ 5.6,  2.7,  4.2,  1.3],
        [ 5.4,  3.9,  1.7,  0.4],
        [ 7.1,  3. ,  5.9,  2.1],
        [ 6.4,  3.2,  4.5,  1.5],
        [ 6. ,  2.9,  4.5,  1.5],
        [ 4.4,  3.2,  1.3,  0.2],
        [ 5.8,  2.6,  4. ,  1.2],
        [ 5.6,  3. ,  4.5,  1.5],
        [ 5.4,  3.4,  1.5,  0.4],
        [ 5. ,  3.2,  1.2,  0.2],
        [ 5.5,  2.6,  4.4,  1.2],
        [ 5.4,  3. ,  4.5,  1.5],
        [ 6.7,  3. ,  5. ,  1.7],
        [ 5. ,  3.5,  1.3,  0.3],
        [ 7.2,  3.2,  6. ,  1.8],
        [ 5.7,  2.8,  4.1,  1.3],
        [ 5.5,  4.2,  1.4,  0.2],
        [ 5.1,  3.8,  1.5,  0.3],
        [ 6.1,  2.8,  4.7,  1.2],
        [ 6.3,  2.5,  5. ,  1.9],
        [ 6.1,  3. ,  4.6,  1.4],
        [ 7.7,  3. ,  6.1,  2.3],
        [ 5.6,  2.5,  3.9,  1.1],
        [ 6.4,  2.8,  5.6,  2.1],
        [ 5.8,  2.8,  5.1,  2.4],
        [ 5.3,  3.7,  1.5,  0.2],
        [ 5.5,  2.3,  4. ,  1.3],
        [ 5.2,  3.4,  1.4,  0.2]]),
 array([0, 1, 1, 0, 2, 1, 2, 0, 0, 2, 1, 0, 2, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1,
        0, 2, 1, 0, 0, 1, 2, 1, 2, 1, 2, 2, 0, 1, 0]))

In [10]:
len(y_test)


Out[10]:
38

In [9]:
len(X_test)


Out[9]:
38

In [7]:
len(X_train)


Out[7]:
112

In [8]:
len(y_train)


Out[8]:
112

In [11]:
automl = autosklearn.classification.AutoSklearnClassifier()
automl.fit(X_train, y_train)

In [13]:
y_hat = automl.predict(X_test)

In [16]:
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_hat))


Accuracy score 0.947368421053

In [ ]: