chore: release 5.1.0

conda-recipe/skll/meta.yaml

@@ -1,6 +1,6 @@
 package:
   name: skll
-  version: 5.0.1
+  version: 5.1.0
 
 source:
   path: ../../../skll

doc/contributing.rst

@@ -78,12 +78,12 @@ There are three main entry points into the SKLL codebase:
    `run_experiment <https://skll.readthedocs.io/en/latest/run_experiment.html#using-run-experiment>`__ script. When you run the command
    ``run_experiment <config_file>``, the following happens (at a high level):
 
-   -  the configuration file is handed off to the `run_configuration() <https://github.com/EducationalTestingService/skll/blob/main/skll/experiments/__init__.py#L613>`__ function in ``experiments.py``.
+   -  the configuration file is handed off to the `run_configuration() <https://github.com/EducationalTestingService/skll/blob/main/skll/experiments/__init__.py#L614>`__ function in ``experiments.py``.
 
    -  a `SKLLConfigParser <https://github.com/EducationalTestingService/skll/blob/main/skll/config/__init__.py#L44>`__ object is instantiated from ``config.py`` that parses all of the relevant fields out of the given configuration file.
 
    -  the configuration fields are then passed to the `_classify_featureset() <https://github.com/EducationalTestingService/skll/blob/main/skll/experiments/__init__.py#L65>`__ function in ``experiments.py`` which instantiates the learners (using code from ``learner.py``), the featuresets (using code from ``reader.py`` & ``featureset.py``), and runs the experiments, collects the results, and writes them out to disk.
 
-2. **SKLL API**. Another way to interact with SKLL is via the SKLL API directly in your Python code rather than using configuration files. For example, you could use the `Learner.from_file() <https://github.com/EducationalTestingService/skll/blob/main/skll/learner/__init__.py#L384>`__ or `VotingLearner.from_file() <https://github.com/EducationalTestingService/skll/blob/main/skll/learner/voting.py#L243>`__ methods to load saved models of those types from disk and make predictions on new data. The documentation for the SKLL API can be found `here <https://skll.readthedocs.io/en/latest/api.html>`__.
+2. **SKLL API**. Another way to interact with SKLL is via the SKLL API directly in your Python code rather than using configuration files. For example, you could use the `Learner.from_file() <https://github.com/EducationalTestingService/skll/blob/main/skll/learner/__init__.py#L389>`__ or `VotingLearner.from_file() <https://github.com/EducationalTestingService/skll/blob/main/skll/learner/voting.py#L245>`__ methods to load saved models of those types from disk and make predictions on new data. The documentation for the SKLL API can be found `here <https://skll.readthedocs.io/en/latest/api.html>`__.
 
 3. **Utility scripts**. The scripts listed in the section above under ``utils`` are also entry points into the SKLL code. These scripts are convenient wrappers that use the SKLL API for commonly used tasks, e.g., generating predictions on new data from an already trained model.

doc/getting_started.rst

@@ -6,7 +6,7 @@ SKLL can be installed via ``pip``::
 
     pip install skll
 
-or via `conda <https://conda.io/projects/conda/en/latest/user-guide/install/index.html>`__::
+or via `conda <https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html>`__::
 
     conda install -c conda-forge -c ets skll
 

doc/run_experiment.rst

@@ -77,7 +77,7 @@ SKLL supports the following feature file formats:
 
 arff
 ^^^^
-The same file format used by `Weka <https://www.cs.waikato.ac.nz/ml/weka/>`__
+The same file format used by `Weka <https://ml.cms.waikato.ac.nz//weka/>`__
 with the following added restrictions:
 
 *   Only simple numeric, string, and nomimal values are supported.

doc/tutorial.rst

@@ -31,7 +31,7 @@ Create virtual environment with SKLL
 
 Before we proceed further, we need to install SKLL. The easiest way to do
 this is in a virtual environment. For this tutorial, we will
-use `conda <https://conda.io/projects/conda/en/latest/user-guide/install/index.html>`__
+use `conda <https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html>`__
 for creating our virtual environment as follows::
 
     conda create -n skllenv -c conda-forge -c ets python=3.11 skll

skll/version.py

@@ -10,5 +10,5 @@
 :organization: ETS
 """
 
-__version__ = "5.0.1"
+__version__ = "5.1.0"
 VERSION = tuple(int(x) for x in __version__.split("."))