Update develop branch to 0.0.2 release

.gitignore

@@ -1,13 +1,13 @@
-
 # Visual Studio
 *.vs/*
 
 # Visual Studio Code
 *.vscode/*
 
 # Python
-*__pycache__/
+*__pycache__*
 *.pyc
+.ipynb_checkpoints*
 
 # Zipped
 *.tar.gz
@@ -24,19 +24,27 @@ results.json
 outputs/
 jobs.txt
 .pylintrc
+*worker_logs*
 
 # Build
 *build/
-*autonet.egg-info
+*autoPyTorch.egg-info
 *.simg
-
-
-# Datasets
-/datasets/
+.DS_Store
+dist/
 
 # Meta GPU
 *meta_logs/
+runs.log
+runs.log.lock
+logs/
 
 # ensemble data
 predictions_for_ensemble.npy
 test_predictions_for_ensemble.npy
+
+# testing
+tests.ipynb
+
+# venv
+env/

LICENSE

@@ -1,14 +1,201 @@
-Copyright 2018 AutoML Freiburg
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README.md

@@ -1,9 +1,9 @@
 # Auto-PyTorch
 
-Copyright (C) 2018  [AutoML Group](http://www.automl.org/)
+Copyright (C) 2019  [AutoML Group Freiburg](http://www.automl.org/)
 
 This a very early pre-alpha version of our upcoming Auto-PyTorch.
-So far, Auto-PyTorch only supports featurized data.
+So far, Auto-PyTorch supports featurized data (classification, regression) and image data (classification).
 
 ## Installation
 
@@ -33,6 +33,8 @@ $ python setup.py install
 
 ## Examples
 
+For a detailed tutorial, please refer to the jupyter notebook in https://github.com/automl/Auto-PyTorch/tree/master/examples/basics.
+
 In a nutshell:
 
 ```py
@@ -95,7 +97,8 @@ autoPyTorch = AutoNetClassification(networks=["resnet", "shapedresnet", "mlpnet"
 # Each hyperparameter belongs to a node in Auto-PyTorch's ML Pipeline.
 # The names of the hyperparameters are prefixed with the name of the node: NodeName:hyperparameter_name.
 # If a hyperparameter belongs to a component: NodeName:component_name:hyperparameter_name.
-autoPyTorch.get_hyperparameter_search_space()
+# Call with the same arguments as fit.
+autoPyTorch.get_hyperparameter_search_space(X_train, y_train, validation_split=0.3)
 
 # You can configure the search space of every hyperparameter of every component:
 from autoPyTorch import HyperparameterSearchSpaceUpdates
@@ -111,7 +114,7 @@ search_space_updates.append(node_name="NetworkSelector",
 autoPyTorch = AutoNetClassification(hyperparameter_search_space_updates=search_space_updates)
 ```
 
-Enable ensemble building:
+Enable ensemble building (for featurized data):
 
 ```py
 from autoPyTorch import AutoNetEnsemble
@@ -129,15 +132,14 @@ autoPyTorch = AutoNetClassification("tiny_cs", log_level='info', max_runtime=300
 ## License
 
 This program is free software: you can redistribute it and/or modify
-it under the terms of the 3-clause BSD license (please see the LICENSE file).
+it under the terms of the Apache license 2.0 (please see the LICENSE file).
 
 This program is distributed in the hope that it will be useful,
 but WITHOUT ANY WARRANTY; without even the implied warranty of
 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 
-You should have received a copy of the 3-clause BSD license
+You should have received a copy of the Apache license 2.0
 along with this program (see LICENSE file).
-If not, see <https://opensource.org/licenses/BSD-3-Clause>.
 
 ## Reference
 
@@ -156,6 +158,9 @@ If not, see <https://opensource.org/licenses/BSD-3-Clause>.
 }
 ```
 
+**Note**: Previously, the name of the project was AutoNet. Since this was too generic, we changed the name to AutoPyTorch. AutoNet 2.0 in the reference mention above is indeed AutoPyTorch.
+
+
 ## Contact
 
 Auto-PyTorch is developed by the [AutoML Group of the University of Freiburg](http://www.automl.org/).

autoPyTorch/__init__.py

@@ -2,6 +2,7 @@
 hpbandster = os.path.abspath(os.path.join(__file__, '..', '..', 'submodules', 'HpBandSter'))
 sys.path.append(hpbandster)
 
-from autoPyTorch.core.autonet_classes import AutoNetClassification, AutoNetMultilabel, AutoNetRegression
+from autoPyTorch.core.autonet_classes import AutoNetClassification, AutoNetMultilabel, AutoNetRegression, AutoNetImageClassification, AutoNetImageClassificationMultipleDatasets
+from autoPyTorch.data_management.data_manager import DataManager
 from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
 from autoPyTorch.core.ensemble import AutoNetEnsemble

autoPyTorch/components/ensembles/abstract_ensemble.py

@@ -2,6 +2,8 @@
 
 
 class AbstractEnsemble(object):
+    """Ensemble interface extracted from auto-sklearn"""
+
     __metaclass__ = ABCMeta
 
     @abstractmethod