From 6562a9b7e06868cc168d68a7656ae46218aca69c Mon Sep 17 00:00:00 2001 From: damien2012eng Date: Sat, 12 Oct 2024 20:30:17 -0400 Subject: [PATCH] chore: release 5.1.0 --- conda-recipe/skll/meta.yaml | 2 +- doc/contributing.rst | 4 ++-- doc/getting_started.rst | 2 +- doc/run_experiment.rst | 2 +- doc/tutorial.rst | 2 +- skll/version.py | 2 +- 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/conda-recipe/skll/meta.yaml b/conda-recipe/skll/meta.yaml index 38ffe588..f571b926 100644 --- a/conda-recipe/skll/meta.yaml +++ b/conda-recipe/skll/meta.yaml @@ -1,6 +1,6 @@ package: name: skll - version: 5.0.1 + version: 5.1.0 source: path: ../../../skll diff --git a/doc/contributing.rst b/doc/contributing.rst index fc981cee..4391bff3 100644 --- a/doc/contributing.rst +++ b/doc/contributing.rst @@ -78,12 +78,12 @@ There are three main entry points into the SKLL codebase: `run_experiment `__ script. When you run the command ``run_experiment ``, the following happens (at a high level): - - the configuration file is handed off to the `run_configuration() `__ function in ``experiments.py``. + - the configuration file is handed off to the `run_configuration() `__ function in ``experiments.py``. - a `SKLLConfigParser `__ 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() `__ 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() `__ or `VotingLearner.from_file() `__ 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 `__. +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() `__ or `VotingLearner.from_file() `__ 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 `__. 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. diff --git a/doc/getting_started.rst b/doc/getting_started.rst index 452b9560..cdd24b24 100644 --- a/doc/getting_started.rst +++ b/doc/getting_started.rst @@ -6,7 +6,7 @@ SKLL can be installed via ``pip``:: pip install skll -or via `conda `__:: +or via `conda `__:: conda install -c conda-forge -c ets skll diff --git a/doc/run_experiment.rst b/doc/run_experiment.rst index f1e299f2..265bc469 100644 --- a/doc/run_experiment.rst +++ b/doc/run_experiment.rst @@ -77,7 +77,7 @@ SKLL supports the following feature file formats: arff ^^^^ -The same file format used by `Weka `__ +The same file format used by `Weka `__ with the following added restrictions: * Only simple numeric, string, and nomimal values are supported. diff --git a/doc/tutorial.rst b/doc/tutorial.rst index f92a3dc5..4f193729 100644 --- a/doc/tutorial.rst +++ b/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 `__ +use `conda `__ for creating our virtual environment as follows:: conda create -n skllenv -c conda-forge -c ets python=3.11 skll diff --git a/skll/version.py b/skll/version.py index fa34ddae..93a4171a 100644 --- a/skll/version.py +++ b/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("."))