Fix typos and grammatical errors in docs and examples. (#845)

tashay · PGijsbers · commit 547901f79dbb · 2019-10-17T10:41:47.000+02:00
* Fix typos and grammatical errors in docs and examples.

* a-&gt;an because it is followed by a vowel ('o')
diff --git a/doc/contributing.rst b/doc/contributing.rst
@@ -21,20 +21,20 @@ you can use github's assign feature, otherwise you can just leave a comment.
 Scope of the package
 ====================
 
-The scope of the OpenML python package is to provide a python interface to
-the OpenML platform which integrates well with pythons scientific stack, most
+The scope of the OpenML Python package is to provide a Python interface to
+the OpenML platform which integrates well with Python's scientific stack, most
 notably `numpy <http://www.numpy.org/>`_ and `scipy <https://www.scipy.org/>`_.
 To reduce opportunity costs and demonstrate the usage of the package, it also
 implements an interface to the most popular machine learning package written
-in python, `scikit-learn <http://scikit-learn.org/stable/index.html>`_.
+in Python, `scikit-learn <http://scikit-learn.org/stable/index.html>`_.
 Thereby it will automatically be compatible with many machine learning
 libraries written in Python.
 
 We aim to keep the package as light-weight as possible and we will try to
 keep the number of potential installation dependencies as low as possible.
 Therefore, the connection to other machine learning libraries such as
 *pytorch*, *keras* or *tensorflow* should not be done directly inside this
-package, but in a separate package using the OpenML python connector.
+package, but in a separate package using the OpenML Python connector.
 
 .. _issues:
 
@@ -52,7 +52,7 @@ contains longer-term goals.
 How to contribute
 =================
 
-There are many ways to contribute to the development of the OpenML python
+There are many ways to contribute to the development of the OpenML Python
 connector and OpenML in general. We welcome all kinds of contributions,
 especially:
 
diff --git a/doc/usage.rst b/doc/usage.rst
@@ -21,11 +21,11 @@ Installation & Set up
 ~~~~~~~~~~~~~~~~~~~~~~
 
 The OpenML Python package is a connector to `OpenML <https://www.openml.org/>`_.
-It allows to use and share datasets and tasks, run
+It allows you to use and share datasets and tasks, run
 machine learning algorithms on them and then share the results online.
 
 The following tutorial gives a short introduction on how to install and set up
-the OpenML python connector, followed up by a simple example.
+the OpenML Python connector, followed up by a simple example.
 
 * `Introduction <examples/introduction_tutorial.html>`_
 
@@ -52,7 +52,7 @@ Working with tasks
 ~~~~~~~~~~~~~~~~~~
 
 You can think of a task as an experimentation protocol, describing how to apply
-a machine learning model to a dataset in a way that it is comparable with the
+a machine learning model to a dataset in a way that is comparable with the
 results of others (more on how to do that further down). Tasks are containers,
 defining which dataset to use, what kind of task we're solving (regression,
 classification, clustering, etc...) and which column to predict. Furthermore,
@@ -86,7 +86,7 @@ predictions of that run. When a run is uploaded to the server, the server
 automatically calculates several metrics which can be used to compare the
 performance of different flows to each other.
 
-So far, the OpenML python connector works only with estimator objects following
+So far, the OpenML Python connector works only with estimator objects following
 the `scikit-learn estimator API <http://scikit-learn.org/dev/developers/contributing.html#apis-of-scikit-learn-objects>`_.
 Those can be directly run on a task, and a flow will automatically be created or
 downloaded from the server if it already exists.
diff --git a/examples/20_basic/introduction_tutorial.py b/examples/20_basic/introduction_tutorial.py
@@ -61,7 +61,7 @@
 openml.config.start_using_configuration_for_example()
 
 ############################################################################
-# When using the main server, instead make sure your apikey is configured.
+# When using the main server instead, make sure your apikey is configured.
 # This can be done with the following line of code (uncomment it!).
 # Never share your apikey with others.
 
diff --git a/examples/20_basic/simple_suites_tutorial.py b/examples/20_basic/simple_suites_tutorial.py
@@ -50,7 +50,7 @@
 print(tasks)
 
 ####################################################################################################
-# and iterated for benchmarking. For speed reasons we'll only iterate over the first three tasks:
+# and iterated over for benchmarking. For speed reasons we'll only iterate over the first three tasks:
 
 for task_id in tasks[:3]:
     task = openml.tasks.get_task(task_id)
diff --git a/examples/30_extended/create_upload_tutorial.py b/examples/30_extended/create_upload_tutorial.py
@@ -198,11 +198,11 @@
 ############################################################################
 # Dataset is a pandas DataFrame
 # =============================
-# It might happen that your dataset is made of heterogeneous data which can be
-# usually stored as a Pandas DataFrame. DataFrame offers the adavantages to
-# store the type of data for each column as well as the attribute names.
-# Therefore, when providing a Pandas DataFrame, OpenML can infer those
-# information without the need to specifically provide them when calling the
+# It might happen that your dataset is made of heterogeneous data which can usually
+# be stored as a Pandas DataFrame. DataFrames offer the advantage of
+# storing the type of data for each column as well as the attribute names.
+# Therefore, when providing a Pandas DataFrame, OpenML can infer this
+# information without needing to explicitly provide it when calling the
 # function :func:`create_dataset`. In this regard, you only need to pass
 # ``'auto'`` to the ``attributes`` parameter.
 
diff --git a/examples/30_extended/fetch_evaluations_tutorial.py b/examples/30_extended/fetch_evaluations_tutorial.py
@@ -3,7 +3,7 @@
 Fetching Evaluations
 ====================
 
-Evalutions contain a concise summary of the results of all runs made. Each evaluation
+Evaluations contain a concise summary of the results of all runs made. Each evaluation
 provides information on the dataset used, the flow applied, the setup used, the metric
 evaluated, and the result obtained on the metric, for each such run made. These collection
 of results can be used for efficient benchmarking of an algorithm and also allow transparent
diff --git a/examples/30_extended/flow_id_tutorial.py b/examples/30_extended/flow_id_tutorial.py
@@ -24,7 +24,7 @@
 print(flow_id)
 
 ####################################################################################################
-# This piece of code is rather involved. First, it retrieves an
+# This piece of code is rather involved. First, it retrieves a
 # :class:`~openml.extensions.Extension` which is registered and can handle the given model,
 # in our case it is :class:`openml.extensions.sklearn.SklearnExtension`. Second, the extension
 # converts the classifier into an instance of :class:`openml.flow.OpenMLFlow`. Third and finally,
@@ -63,6 +63,6 @@
 print(flow_ids)
 
 ####################################################################################################
-# This also work with the actual model (generalizing the first part of this example):
+# This also works with the actual model (generalizing the first part of this example):
 flow_ids = openml.flows.get_flow_id(model=clf, exact_version=False)
 print(flow_ids)
diff --git a/examples/30_extended/tasks_tutorial.py b/examples/30_extended/tasks_tutorial.py
@@ -175,7 +175,7 @@
 # Let's create a classification task on a dataset. In this example we will do this on the
 # Iris dataset (ID=128 (on test server)). We'll use 10-fold cross-validation (ID=1),
 # and *predictive accuracy* as the predefined measure (this can also be left open).
-# If a task with these parameters exist, we will get an appropriate exception.
+# If a task with these parameters exists, we will get an appropriate exception.
 # If such a task doesn't exist, a task will be created and the corresponding task_id
 # will be returned.
 
