Release 0.8.0

MANIFEST.in

@@ -4,3 +4,5 @@ recursive-include autosklearn/metalearning/files *.txt
 include autosklearn/util/logging.yaml
 recursive-include autosklearn *.pyx
 include requirements.txt
+recursive-include autosklearn/experimental/askl2_portfolios *.json
+include autosklearn/experimental/askl2_training_data.json

README.md

@@ -4,6 +4,28 @@ auto-sklearn is an automated machine learning toolkit and a drop-in replacement
 
 Find the documentation [here](http://automl.github.io/auto-sklearn/)
 
+## Automated Machine Learning in four lines of code
+
+```python
+import autosklearn.classification
+cls = autosklearn.classification.AutoSklearnClassifier()
+cls.fit(X_train, y_train)
+predictions = cls.predict(X_test)
+```
+
+## Relevant publications
+
+Efficient and Robust Automated Machine Learning  
+Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum and Frank Hutter  
+Advances in Neural Information Processing Systems 28 (2015)  
+http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf
+
+Auto-Sklearn 2.0: The Next Generation  
+Authors: Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer and Frank Hutter  
+To appear
+
+## Status
+
 Status for master branch
 
 [![Build Status](https://travis-ci.org/automl/auto-sklearn.svg?branch=master)](https://travis-ci.org/automl/auto-sklearn)

autosklearn/__version__.py

@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "0.7.1"
+__version__ = "0.8.0"

autosklearn/automl.py

@@ -36,7 +36,7 @@
 from autosklearn.util.hash import hash_array_or_matrix
 from autosklearn.metrics import f1_macro, accuracy, r2
 from autosklearn.constants import MULTILABEL_CLASSIFICATION, MULTICLASS_CLASSIFICATION, \
-    REGRESSION_TASKS, REGRESSION, BINARY_CLASSIFICATION
+    REGRESSION_TASKS, REGRESSION, BINARY_CLASSIFICATION, MULTIOUTPUT_REGRESSION
 
 
 def _model_predict(model, X, batch_size, logger, task):
@@ -936,13 +936,16 @@ def _check_X(self, X):
 
     def _check_y(self, y):
         y = sklearn.utils.check_array(y, ensure_2d=False)
-
         y = np.atleast_1d(y)
-        if y.ndim == 2 and y.shape[1] == 1:
+
+        if y.ndim == 1:
+            return y
+        elif y.ndim == 2 and y.shape[1] == 1:
             warnings.warn("A column-vector y was passed when a 1d array was"
                           " expected. Will change shape via np.ravel().",
                           sklearn.utils.DataConversionWarning, stacklevel=2)
             y = np.ravel(y)
+            return y
 
         return y
 
@@ -1097,6 +1100,9 @@ def predict_proba(self, X, batch_size=None, n_jobs=1):
 class AutoMLRegressor(BaseAutoML):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
+        self._task_mapping = {'continuous-multioutput': MULTIOUTPUT_REGRESSION,
+                              'continuous': REGRESSION,
+                              'multiclass': REGRESSION}
 
     def fit(
         self,
@@ -1110,17 +1116,20 @@ def fit(
         load_models: bool = True,
     ):
         X, y = super()._perform_input_checks(X, y)
-        _n_outputs = 1 if len(y.shape) == 1 else y.shape[1]
-        if _n_outputs > 1:
-            raise NotImplementedError(
-                'Multi-output regression is not implemented.')
+        y_task = type_of_target(y)
+        task = self._task_mapping.get(y_task)
+        if task is None:
+            raise ValueError('Cannot work on data of type %s' % y_task)
+
         if self._metric is None:
             self._metric = r2
+
+        self._n_outputs = 1 if len(y.shape) == 1 else y.shape[1]
         return super().fit(
             X, y,
             X_test=X_test,
             y_test=y_test,
-            task=REGRESSION,
+            task=task,
             feat_type=feat_type,
             dataset_name=dataset_name,
             only_return_configuration_space=only_return_configuration_space,

autosklearn/constants.py

@@ -4,8 +4,9 @@
 MULTICLASS_CLASSIFICATION = 2
 MULTILABEL_CLASSIFICATION = 3
 REGRESSION = 4
+MULTIOUTPUT_REGRESSION = 5
 
-REGRESSION_TASKS = [REGRESSION]
+REGRESSION_TASKS = [REGRESSION, MULTIOUTPUT_REGRESSION]
 CLASSIFICATION_TASKS = [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION,
                         MULTILABEL_CLASSIFICATION]
 
@@ -15,10 +16,12 @@
     {BINARY_CLASSIFICATION: 'binary.classification',
      MULTICLASS_CLASSIFICATION: 'multiclass.classification',
      MULTILABEL_CLASSIFICATION: 'multilabel.classification',
-     REGRESSION: 'regression'}
+     REGRESSION: 'regression',
+     MULTIOUTPUT_REGRESSION: 'multioutput.regression'}
 
 STRING_TO_TASK_TYPES = \
     {'binary.classification': BINARY_CLASSIFICATION,
      'multiclass.classification': MULTICLASS_CLASSIFICATION,
      'multilabel.classification': MULTILABEL_CLASSIFICATION,
-     'regression': REGRESSION}
+     'regression': REGRESSION,
+     'multioutput.regression': MULTIOUTPUT_REGRESSION}

autosklearn/data/xy_data_manager.py

@@ -4,7 +4,7 @@
 from scipy import sparse
 
 from autosklearn.constants import STRING_TO_TASK_TYPES, REGRESSION, BINARY_CLASSIFICATION, \
-    MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION
+    MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION
 from autosklearn.data.abstract_data_manager import AbstractDataManager
 
 
@@ -27,6 +27,7 @@ def __init__(self, X, y, X_test, y_test, task, feat_type, dataset_name):
         label_num = {
             REGRESSION: 1,
             BINARY_CLASSIFICATION: 2,
+            MULTIOUTPUT_REGRESSION: y.shape[-1],
             MULTICLASS_CLASSIFICATION: len(np.unique(y)),
             MULTILABEL_CLASSIFICATION: y.shape[-1]
         }

autosklearn/estimators.py

@@ -766,15 +766,15 @@ def fit(self, X, y,
         X : array-like or sparse matrix of shape = [n_samples, n_features]
             The training input samples.
 
-        y : array-like, shape = [n_samples]
+        y : array-like, shape = [n_samples] or [n_samples, n_targets]
             The regression target.
 
         X_test : array-like or sparse matrix of shape = [n_samples, n_features]
             Test data input samples. Will be used to save test predictions for
             all models. This allows to evaluate the performance of Auto-sklearn
             over time.
 
-        y_test : array-like, shape = [n_samples]
+        y_test : array-like, shape = [n_samples] or [n_samples, n_targets]
             The regression target. Will be used to calculate the test error
             of all models. This allows to evaluate the performance of
             Auto-sklearn over time.
@@ -799,7 +799,6 @@ def fit(self, X, y,
         target_type = type_of_target(y)
         if target_type in ['multiclass-multioutput',
                            'multilabel-indicator',
-                           'continuous-multioutput',
                            'unknown',
                            ]:
             raise ValueError("regression with data of type %s is not"

autosklearn/evaluation/abstract_evaluator.py

@@ -15,6 +15,7 @@
     REGRESSION_TASKS,
     MULTILABEL_CLASSIFICATION,
     MULTICLASS_CLASSIFICATION,
+    MULTIOUTPUT_REGRESSION
 )
 from autosklearn.pipeline.implementations.util import (
     convert_multioutput_multiclass_to_multilabel
@@ -204,12 +205,19 @@ def _get_model(self):
                                      random_state=self.seed,
                                      init_params=self._init_params)
         else:
-            dataset_properties = {
-                'task': self.task_type,
-                'sparse': self.datamanager.info['is_sparse'] == 1,
-                'multilabel': self.task_type == MULTILABEL_CLASSIFICATION,
-                'multiclass': self.task_type == MULTICLASS_CLASSIFICATION,
-            }
+            if self.task_type in REGRESSION_TASKS:
+                dataset_properties = {
+                    'task': self.task_type,
+                    'sparse': self.datamanager.info['is_sparse'] == 1,
+                    'multioutput': self.task_type == MULTIOUTPUT_REGRESSION,
+                }
+            else:
+                dataset_properties = {
+                    'task': self.task_type,
+                    'sparse': self.datamanager.info['is_sparse'] == 1,
+                    'multilabel': self.task_type == MULTILABEL_CLASSIFICATION,
+                    'multiclass': self.task_type == MULTICLASS_CLASSIFICATION,
+                }
             model = self.model_class(config=self.configuration,
                                      dataset_properties=dataset_properties,
                                      random_state=self.seed,