Forgot to add usage.rst

Author: mfeurer

source/usage.rst

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+:orphan:
+
+.. _usage:
+
+Basic Usage
+***********
+
+Connecting to the OpenML server
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The OpenML server can only be accessed by users who have signed up to the OpenML
+platform. If you don't have an account yet,
+`sign up now <http://openml.org/register>`_.
+
+.. code:: python
+
+    >>> from openml.apiconnector import APIConnector
+
+    >>> username = "Your OpenML username"
+    >>> password = "Your OpenML password"
+    >>> connector = APIConnector(username=username, password=password)
+
+The :class:`~openml.apiconnector.APIConnector` will create a cache directory
+and authenticate you at the OpenML server. By this you obtain a session key,
+which is valid for one hour.
+
+You can also configure the OpenML package, e.g. change the cache directory.
+Information about the configuration is in the
+`OpenML client API description <https://github
+.com/openml/OpenML/wiki/Client-API>`_.
+
+Working with datasets
+~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: python
+
+    >>> dataset_id = 31
+    >>> dataset = connector.download_dataset(1)
+
+Attributes of the dataset are stored as member variables:
+
+.. code:: python
+
+    >>> dataset.name
+    u'credit-g'
+    >>> dataset.default_target_attribute
+    u'class'
+
+Data can be loaded in the following ways:
+
+.. code:: python
+
+    >>> pd, categorical = dataset.get_pandas()
+
+returns the dataset as a pandas.DataFrame and a list of booleans,
+indicating which attributes are categorical. Categorical attributes are
+already encoded as integers.
+
+.. code:: python
+
+    >>> X, y, categorical = dataset.get_pandas()
+
+returns the dataset split into X and y, as well as a list indicating which
+attributes are categorical. In case you are working with `scikit-learn
+<http://scikit-learn>`_, you can use this data right away:
+
+.. code:: python
+
+    >>> from sklearn import preprocessing, ensemble
+    >>> enc = preprocessing.OneHotEncoder(categorical_features=categorical)
+    OneHotEncoder(categorical_features=[True, False, True, True, False, True,
+        True, False, True, True, False, True, False, True, True, False, True,
+        False, True, True], dtype=<type 'float'>, n_values='auto',
+        sparse=True)
+    >>> X = enc.transform(X).todense()
+    >>> clf = ensemble.RandomForestClassifier()
+    >>> clf.fit(X, y)
+    RandomForestClassifier(bootstrap=True, compute_importances=None,
+            criterion='gini', max_depth=None, max_features='auto',
+            max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
+            min_samples_split=2, n_estimators=10, n_jobs=1,
+            oob_score=False, random_state=None, verbose=0)
+
+Working with tasks
+~~~~~~~~~~~~~~~~~~
+
+Using the cache
+~~~~~~~~~~~~~~~
+
+Large scale experiments
+~~~~~~~~~~~~~~~~~~~~~~~