diff --git a/.all-contributorsrc b/.all-contributorsrc index 24a4a56e5..8d6f60fa8 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -175,6 +175,15 @@ "contributions": [ "maintenance" ] + }, + { + "login": "szepeviktor", + "name": "Viktor Szépe", + "avatar_url": "https://avatars.githubusercontent.com/u/952007?v=4", + "profile": "https://github.com/szepeviktor", + "contributions": [ + "doc" + ] } ] } diff --git a/docs/introduction.rst b/docs/introduction.rst index 6b6e7313b..8e6d744d1 100644 --- a/docs/introduction.rst +++ b/docs/introduction.rst @@ -33,7 +33,7 @@ Let's have a look at an example of Boston Housing price prediction (using sklear If you are familiar with scikit-learn you will recognise that we define and train a model on the boston housing dataset and obtain the test prediction ``y_pred``. Furthermore, we use a loss function to calculate the loss between the predicted points and the true values -- nothing unexpected there. -Crucially, however, the skpro model does not just return a list of numbers or point predictions here. Instead, ``y_pred`` is a probablistic prediction, i.e. it represents probability distributions for each individual data point. +Crucially, however, the skpro model does not just return a list of numbers or point predictions here. Instead, ``y_pred`` is a probabilistic prediction, i.e. it represents probability distributions for each individual data point. We can, for instance, obtain the standard deviation of the predicted distribution that corresponds with the first (0th) test point (or any other test point distribution) :: >>> y_pred[0].std() @@ -44,7 +44,7 @@ Notably, the interface represents all distributional properties, including the d >>> y_pred[0].pdf(x=42) 0.00192808538756 -Furthermore, it is possible to conveniently access the distributional properties in their vectorized form accross the test sample:: +Furthermore, it is possible to conveniently access the distributional properties in their vectorized form across the test sample:: >>> y_pred.std().shape (152,) diff --git a/docs/source/changelog.rst b/docs/source/changelog.rst index 982c9fbec..f931401ca 100644 --- a/docs/source/changelog.rst +++ b/docs/source/changelog.rst @@ -2,7 +2,7 @@ Changelog ========= -All notable changes to this project beggining with version 0.1.0 will be +All notable changes to this project beginning with version 0.1.0 will be documented in this file. The format is based on `Keep a Changelog `_ and we adhere to `Semantic Versioning `_. The source @@ -197,6 +197,7 @@ Documentation * [DOC] tutorial notebook for survival prediction (:pr:`305`) :user:`fkiraly` * [DOC] visualizations for first intro vignette in intro notebook and minor updates (:pr:`311`) :user:`fkiraly` * [DOC] improve docstrings of metrics (:pr:`317`) :user:`fkiraly` +* [DOC] Fix typos throughout the codebase (:pr:`338`) :user:`szepeviktor` Contributors ~~~~~~~~~~~~ @@ -207,7 +208,8 @@ Contributors :user:`nilesh05apr`, :user:`setoguchi-naoki`, :user:`ShreeshaM07`, -:user:`sukjingitsit` +:user:`sukjingitsit`, +:user:`szepeviktor` [2.2.2] - 2024-04-20 diff --git a/docs/source/conf.py b/docs/source/conf.py index d66004a07..264842ea3 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -267,7 +267,7 @@ def find_source(): nbsphinx_allow_errors = False # False nbsphinx_timeout = 600 # seconds, set to -1 to disable timeout -# add Binder launch buttom at the top +# add Binder launch button at the top current_file = "{{ env.doc2path( env.docname, base=None) }}" # make sure Binder points to latest stable release, not main diff --git a/docs/source/contribute/code_of_conduct.rst b/docs/source/contribute/code_of_conduct.rst index 935c57ad3..e147e607e 100644 --- a/docs/source/contribute/code_of_conduct.rst +++ b/docs/source/contribute/code_of_conduct.rst @@ -5,7 +5,7 @@ Code of conduct =============== The ``skpro`` project believes that everyone should be able to participate -in our community without fear of harrassment or discrimination. All contributors +in our community without fear of harassment or discrimination. All contributors are expected to show respect and courtesy to other members of the community at all times. @@ -22,4 +22,4 @@ to Dr. Franz Király by email at franz.kiraly@sktime.net. Code of Conduct processes (including how to report incidents). This may change as the project matures. However, ``skpro``'s Code of Conduct will remain - dedicated to promoting a community without harrassment and discrimination. + dedicated to promoting a community without harassment and discrimination. diff --git a/docs/source/developer_guide/dependencies.rst b/docs/source/developer_guide/dependencies.rst index b184802d4..d728edbe7 100644 --- a/docs/source/developer_guide/dependencies.rst +++ b/docs/source/developer_guide/dependencies.rst @@ -18,7 +18,7 @@ Types of dependencies Making it easy to install and use ``skpro`` in a variety of projects is on of ``skpro``'s goals. Therefore, we seeks to minimizing the number of -dependencies needed to provide the proejct's functionality. +dependencies needed to provide the project's functionality. Soft Dependencies ================= diff --git a/docs/source/developer_guide/deprecation.rst b/docs/source/developer_guide/deprecation.rst index fdcf8d62b..606f747c1 100644 --- a/docs/source/developer_guide/deprecation.rst +++ b/docs/source/developer_guide/deprecation.rst @@ -81,7 +81,7 @@ The release manager process happens at every release and is as follows: Special deprecations ==================== -This section outlines the deprecation process for some advaned cases. +This section outlines the deprecation process for some advanced cases. Deprecating tags ---------------- diff --git a/docs/source/developer_guide/reviewer_guide.rst b/docs/source/developer_guide/reviewer_guide.rst index 2636584ed..47a5e1b30 100644 --- a/docs/source/developer_guide/reviewer_guide.rst +++ b/docs/source/developer_guide/reviewer_guide.rst @@ -43,7 +43,7 @@ Code Review .. _reviewer_guide_doc: -Documenation Review +Documentation Review ==================== * Are the docstrings complete and understandable to users? diff --git a/docs/source/installation.rst b/docs/source/installation.rst index cf4a97c46..2c892545a 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -76,17 +76,17 @@ To install the latest development version of ``skpro``, the sequence of steps is as follows: -1. Clone the ``skpro`` `Github repository`_ +1. Clone the ``skpro`` `GitHub repository`_ 2. Create a new virtual environment via ``conda`` and activate it. 3. Use ``pip`` to build ``skpro`` from source and install development dependencies Detail instructions for each step is provided below. -Step 1 - Clone Github repository +Step 1 - Clone GitHub repository -------------------------------- -The ``skpro`` `Github repository`_ should be cloned to a local directory. +The ``skpro`` `GitHub repository`_ should be cloned to a local directory. To install the latest version using the ``git`` command line, use the following steps: @@ -97,7 +97,7 @@ To install the latest version using the ``git`` command line, use the following 4. Make sure you are on the main branch: :code:`git checkout main` 5. Make sure your local version is up-to-date: :code:`git pull` -See Github's `repository clone documentation`_ +See GitHub's `repository clone documentation`_ for additional details. .. hint:: @@ -108,7 +108,7 @@ for additional details. Where ```` is a valid version string that can be found by inspecting the repository's ``git`` tags, by running ``git tag``. - You can also download a specific release version from the Github repository's + You can also download a specific release version from the GitHub repository's zip archive of `releases `_. Step 2 - Create a new virtual environment diff --git a/examples/01_skpro_intro.ipynb b/examples/01_skpro_intro.ipynb index ae2471fc2..b8de395b3 100644 --- a/examples/01_skpro_intro.ipynb +++ b/examples/01_skpro_intro.ipynb @@ -2487,7 +2487,7 @@ " capability:survival\n", " regressor_proba\n", " bool\n", - " whether estimator can use censoring informatio...\n", + " whether estimator can use censoring information...\n", " \n", " \n", " 5\n", @@ -2514,7 +2514,7 @@ "1 which machine type(s) is the internal _fit/_pr... \n", "2 whether estimator supports missing values \n", "3 whether estimator supports multioutput regression \n", - "4 whether estimator can use censoring informatio... \n", + "4 whether estimator can use censoring information... \n", "5 which machine type(s) is the internal _fit/_pr... " ] }, @@ -4423,7 +4423,7 @@ "* predicting variance equal to training residual variance - `ResidualDouble` with standard settings\n", " * or other unconditional distribution estimate for residuals\n", "* \"squaring the residual\" two-step prediction - `ResidualDouble`\n", - "* boostrap prediction intervals - `BootstrapRegressor`\n", + "* bootstrap prediction intervals - `BootstrapRegressor`\n", "* MAPIE model agnostic prediction intervals - `MapieRegressor` (from `mapie` package)\n", "* natural gradient boosting aka NGBoost - `NGBoostRegressor` (from `ngboost` package)" ] @@ -5095,7 +5095,7 @@ "1. Read through the [probabilistic regression extension template](https://github.com/sktime/skpro/blob/main/extension_templates/regression.py) - this is a `python` file with `todo` blocks that mark the places in which changes need to be added.\n", "2. Copy the proba regressor extension template to a local folder in your own repository (local/private extension), or to a suitable location in your clone of the `skpro` or affiliated repository (if contributed extension), inside `skpro.regression`; rename the file and update the file docstring appropriately.\n", "3. Address the \"todo\" parts. Usually, this means: changing the name of the class, setting the tag values, specifying hyper-parameters, filling in `__init__`, `_fit`, and at least one of the probabilistic prediction methods, preferably `_predict_proba` (for details see the extension template). You can add private methods as long as they do not override the default public interface. For more details, see the extension template.\n", - "4. To test your estimator manually: import your estimator and run it in the worfklows in Section 1; then use it in the compositors in Section 3.\n", + "4. To test your estimator manually: import your estimator and run it in the workflows in Section 1; then use it in the compositors in Section 3.\n", "5. To test your estimator automatically: call `skpro.utils.check_estimator` on your estimator. You can call this on a class or object instance. Ensure you have specified test parameters in the `get_test_params` method, according to the extension template.\n", "\n", "In case of direct contribution to `skpro` or one of its affiliated packages, additionally:\n", diff --git a/examples/02_skpro_survival.ipynb b/examples/02_skpro_survival.ipynb index 011564c06..582a41581 100644 --- a/examples/02_skpro_survival.ipynb +++ b/examples/02_skpro_survival.ipynb @@ -30,7 +30,7 @@ "source": [ "`skpro` provides a unified interface to time-to-event prediction models, also known as survival prediction models.\n", "\n", - "**Time-to-event prediction** is a form of probabilistic regression where **labels can be \"censored\"**, i.e., of the form \"time is t or later\" instead of exat observations.\n", + "**Time-to-event prediction** is a form of probabilistic regression where **labels can be \"censored\"**, i.e., of the form \"time is t or later\" instead of exact observations.\n", "\n", "**Section 1** provides an overview of the basic **time-to-event prediction workflows** supported by `skpro`.\n", "\n", @@ -103,7 +103,7 @@ "metadata": {}, "outputs": [], "source": [ - "# simulated toy datset, lung cancer survival times\n", + "# simulated toy dataset, lung cancer survival times\n", "import numpy as np\n", "\n", "# demographics - age and smoker yes/no\n", @@ -567,7 +567,7 @@ } ], "source": [ - "# plotting survival funtions in one figure, smokers in red\n", + "# plotting survival functions in one figure, smokers in red\n", "from matplotlib.pyplot import subplots\n", "\n", "_, ax = subplots()\n", @@ -636,7 +636,7 @@ } ], "source": [ - "# plotting survival funtions in one figure, smokers in red\n", + "# plotting survival functions in one figure, smokers in red\n", "from matplotlib.pyplot import subplots\n", "\n", "_, ax = subplots()\n", @@ -705,7 +705,7 @@ } ], "source": [ - "# plotting survival funtions in one figure, smokers in red\n", + "# plotting survival functions in one figure, smokers in red\n", "from matplotlib.pyplot import subplots\n", "\n", "_, ax = subplots()\n", @@ -954,7 +954,7 @@ "* metrics will compare probabilistic prediction to tabular ground truth and\n", " censoring indicator\n", "* the metric needs to be of a compatible type, e.g., for distribution predictions\n", - "* special survival metrics are avaliable to take into account censoring;\n", + "* special survival metrics are available to take into account censoring;\n", " if a non-survival metric is used, the censoring indicator will be ignored" ] }, @@ -1296,7 +1296,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "the three main reduction stratgies to create survival regressors:\n", + "the three main reduction strategies to create survival regressors:\n", "\n", "1. adding the capability to handle censoring information to a probabilistic supervised regressor\n", "2. the above, combined with any strategy to create a probabilistic regressor from an `sklearn` (non-probabilistic) regressors\n", @@ -1757,7 +1757,7 @@ "1. Read through the [survival regression extension template](https://github.com/sktime/skpro/blob/main/extension_templates/survival.py) - this is a `python` file with `todo` blocks that mark the places in which changes need to be added.\n", "2. Copy the proba regressor extension template to a local folder in your own repository (local/private extension), or to a suitable location in your clone of the `skpro` or affiliated repository (if contributed extension), inside `skpro.survival`; rename the file and update the file docstring appropriately.\n", "3. Address the \"todo\" parts. Usually, this means: changing the name of the class, setting the tag values, specifying hyper-parameters, filling in `__init__`, `_fit`, and at least one of the probabilistic prediction methods, preferably `_predict_proba` (for details see the extension template). You can add private methods as long as they do not override the default public interface. For more details, see the extension template.\n", - "4. To test your estimator manually: import your estimator and run it in the worfklows in Section 1; then use it in the compositors in Section 3.\n", + "4. To test your estimator manually: import your estimator and run it in the workflows in Section 1; then use it in the compositors in Section 3.\n", "5. To test your estimator automatically: call `skpro.utils.check_estimator` on your estimator. You can call this on a class or object instance. Ensure you have specified test parameters in the `get_test_params` method, according to the extension template.\n", "\n", "In case of direct contribution to `skpro` or one of its affiliated packages, additionally:\n", diff --git a/examples/03_skpro_distributions.ipynb b/examples/03_skpro_distributions.ipynb index bfffc87a5..027d3d7f4 100644 --- a/examples/03_skpro_distributions.ipynb +++ b/examples/03_skpro_distributions.ipynb @@ -1196,7 +1196,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "at construction, all (simple) probabilty distributions broadcast parameters:\n", + "at construction, all (simple) probability distributions broadcast parameters:\n", "\n", "* if `index` or `columns` are passed, always broadcasts to 2D\n", "* 1D iterables are interpreted as row vectors, i.e., of shape (1, n)\n", @@ -1375,7 +1375,7 @@ "\n", "n = Normal(mu=[[1, 2], [2, 3]], sigma=2)\n", "# results in a shape (2, 2) distribution\n", - "# sigma is broadcast ot the shape of mu\n", + "# sigma is broadcast to the shape of mu\n", "# index, columns are RangeIndex, i.e., [0, 1]\n", "n.shape" ] @@ -1463,7 +1463,7 @@ "n = Normal(mu=[1, 2, 3], sigma=2)\n", "# results in a shape (1, 3) distribution\n", "# mu is interpreted as (1, 3) row vector\n", - "# sigma is broadcast ot the shape of mu\n", + "# sigma is broadcast to the shape of mu\n", "# index, columns are RangeIndex, i.e., index=[0] and columns=[0, 1, 2]\n", "n.shape" ] @@ -1537,7 +1537,7 @@ "source": [ "as first-class citizens, all objects in `skpro` are indexed via the `registry` utility `all_objects`.\n", "\n", - "To find probabilisty distirbutions, use `all_objects` with the type `distribution`:" + "To find probabilisty distributions, use `all_objects` with the type `distribution`:" ] }, { @@ -2447,7 +2447,7 @@ ], "source": [ "# dataframe distributions convert to a DataFrame\n", - "# with column MultiIndex (variable, parmaeter)\n", + "# with column MultiIndex (variable, parameter)\n", "d = Normal(mu=[[1, 2], [3, 4], [5, 6]], sigma=2, columns=[\"var1\", \"var2\"])\n", "d.to_df()" ] @@ -2648,10 +2648,10 @@ "* Follow the [\"implementing estimator\" developer guide](https://skpro.readthedocs.io/en/stable/developer_guide/add_estimators.html)\n", "* Use the [probabilistic regressor template](https://github.com/sktime/skpro/blob/main/extension_templates/regression.py) to get started\n", "\n", - "1. Read through the [probability distibution extension template](https://github.com/sktime/skpro/blob/main/extension_templates/distributions.py) - this is a `python` file with `todo` blocks that mark the places in which changes need to be added.\n", + "1. Read through the [probability distribution extension template](https://github.com/sktime/skpro/blob/main/extension_templates/distributions.py) - this is a `python` file with `todo` blocks that mark the places in which changes need to be added.\n", "2. Copy the distribution extension template to a local folder in your own repository (local/private extension), or to a suitable location in your clone of the `skpro` or affiliated repository (if contributed extension), inside `skpro.distributions`; rename the file and update the file docstring appropriately.\n", "3. Address the \"todo\" parts. Usually, this means: changing the name of the class, setting the tag values, specifying hyper-parameters, filling in `__init__`, and as many methods as possible, most importantly `_ppf`, and possibly other common methods such as `_pdf` or `_pmf`, `_cdf`. You can add private methods as long as they do not override the default public interface. For more details, see the extension template.\n", - "4. To test your estimator manually: import your estimator and run it in the worfklows in Section 1; then use it in the compositors in Section 3.\n", + "4. To test your estimator manually: import your estimator and run it in the workflows in Section 1; then use it in the compositors in Section 3.\n", "5. To test your estimator automatically: call `skpro.utils.check_estimator` on your estimator. You can call this on a class or object instance. Ensure you have specified test parameters in the `get_test_params` method, according to the extension template.\n", "\n", "In case of direct contribution to `skpro` or one of its affiliated packages, additionally:\n", @@ -2670,7 +2670,7 @@ "\n", "* `skpro` provides a unified interface for probability distributions, with an interface inspired by `scikit-learn` and `pandas`\n", "\n", - "* `skpro` distributions are parametric first class objects. They are symbolic representations of probability distibution, and come with methods such as `pdf`, `cdf`, `sample`, `plot`, etc\n", + "* `skpro` distributions are parametric first class objects. They are symbolic representations of probability distribution, and come with methods such as `pdf`, `cdf`, `sample`, `plot`, etc\n", "\n", "* `skpro` distributions can be scalar-valued, or data frame valued. The latter broadcast parameters and arguments of methods to 2D, and are returned by `skpro` probabilistic regressors or survival regressors `predict_proba`\n", "\n", diff --git a/extension_templates/regression.py b/extension_templates/regression.py index 00151ff5b..5d987c4d3 100644 --- a/extension_templates/regression.py +++ b/extension_templates/regression.py @@ -70,7 +70,7 @@ class ClassName(BaseProbaRegressor): # parameter checks can go after super call def __init__(self, paramname, paramname2="paramname2default"): # estimators should precede parameters - # if estimators have default values, set None and initalize below + # if estimators have default values, set None and initialize below # todo: write any hyper-parameters and components to self self.paramname = paramname diff --git a/extension_templates/survival.py b/extension_templates/survival.py index f270761db..5447c60bb 100644 --- a/extension_templates/survival.py +++ b/extension_templates/survival.py @@ -49,7 +49,7 @@ class ClassName(BaseSurvReg): # parameter checks can go after super call def __init__(self, paramname, paramname2="paramname2default"): # estimators should precede parameters - # if estimators have default values, set None and initalize below + # if estimators have default values, set None and initialize below # todo: write any hyper-parameters and components to self self.paramname = paramname diff --git a/skpro/datatypes/_convert.py b/skpro/datatypes/_convert.py index 27d920e04..f823b47c4 100644 --- a/skpro/datatypes/_convert.py +++ b/skpro/datatypes/_convert.py @@ -4,7 +4,7 @@ Exports ------- convert_to(obj, to_type: str, as_scitype: str, store=None) - converts object "obj" to type "to_type", considerd as "as_scitype" + converts object "obj" to type "to_type", considered as "as_scitype" convert(obj, from_type: str, to_type: str, as_scitype: str, store=None) same as convert_to, without automatic identification of "from_type" @@ -159,7 +159,7 @@ def convert( pass else: raise ValueError( - "bug: unrechable condition error, store_behaviour has unexpected value" + "bug: unreachable condition error, store_behaviour has unexpected value" ) converted_obj = convert_dict[key](obj, store=store) @@ -167,7 +167,7 @@ def convert( return converted_obj -# conversion based on queriable type to specified target +# conversion based on queryable type to specified target def convert_to( obj, to_type: str, diff --git a/skpro/datatypes/_convert_utils/_convert.py b/skpro/datatypes/_convert_utils/_convert.py index 7e6ac8dbe..4bb0066b5 100644 --- a/skpro/datatypes/_convert_utils/_convert.py +++ b/skpro/datatypes/_convert_utils/_convert.py @@ -52,12 +52,12 @@ def _extend_conversions(mtype, anchor_mtype, convert_dict, mtype_universe=None): anchor_mtype : mtype string in convert_dict convert_dict : conversion dictionary with entries of converter signature see docstring of datatypes._convert - mtype_universe : iterable of mtype strings in convert_dict, coercable to list or set + mtype_universe : iterable of mtype strings in convert_dict, coercible to list or set Returns ------- reference to convert_dict - CAVEAT: convert_dict passed to this function gets mutated, this is a referene + CAVEAT: convert_dict passed to this function gets mutated, this is a reference """ keys = convert_dict.keys() scitype = list(keys)[0][2] diff --git a/skpro/datatypes/tests/test_convert_to.py b/skpro/datatypes/tests/test_convert_to.py index 84d6390e3..fb133e026 100644 --- a/skpro/datatypes/tests/test_convert_to.py +++ b/skpro/datatypes/tests/test_convert_to.py @@ -1,4 +1,4 @@ -"""Testing machine type converters for scitypes - covert_to utility.""" +"""Testing machine type converters for scitypes - convert_to utility.""" __author__ = ["fkiraly"] diff --git a/skpro/distributions/base/_base.py b/skpro/distributions/base/_base.py index 5fa03dd89..933985f32 100644 --- a/skpro/distributions/base/_base.py +++ b/skpro/distributions/base/_base.py @@ -67,7 +67,7 @@ def _init_shape_bc(self, index=None, columns=None): default broadcasting and pre-initialization is not desired or applicable, e.g., distribution parameters are not array-like. - If overriden, must set ``self._shape``: this should be an empty tuple + If overridden, must set ``self._shape``: this should be an empty tuple if the distribution is scalar, or a pair of integers otherwise. """ if self.get_tags()["broadcast_init"] == "off": @@ -1572,7 +1572,7 @@ def get_ax(ax, i, j, shape): fig.supxlabel(f"{x_argname}") return fig, ax - # for now, all plots default ot this function + # for now, all plots default to this function # but this could be changed to a dispatch mechanism # e.g., using this line instead # plot_fun_name = f"_plot_{fun}" diff --git a/skpro/distributions/delta.py b/skpro/distributions/delta.py index f0efb4662..002de8883 100644 --- a/skpro/distributions/delta.py +++ b/skpro/distributions/delta.py @@ -10,7 +10,7 @@ class Delta(BaseDistribution): - r"""Delta distribution aka constant distibution aka certain distribution. + r"""Delta distribution aka constant distribution aka certain distribution. This distribution always produces the same value when sampling - ``c``. It it useful to represent a constant value as a distribution, e.g., as a baseline diff --git a/skpro/distributions/empirical.py b/skpro/distributions/empirical.py index 13a4c18d8..8d8ecb3ff 100644 --- a/skpro/distributions/empirical.py +++ b/skpro/distributions/empirical.py @@ -193,7 +193,7 @@ def _apply_per_ix(self, func, params, x=None): def _slice_ix(self, obj, ix): """Slice obj by index ix, applied to MultiIndex levels 1 ... last. - obj is assumed to have MultiIndex, and slicing occurrs on the + obj is assumed to have MultiIndex, and slicing occurs on the last levels, 1 ... last. ix can be a simple index or MultiIndex, diff --git a/skpro/metrics/base.py b/skpro/metrics/base.py index 17f4b6271..18350fa37 100644 --- a/skpro/metrics/base.py +++ b/skpro/metrics/base.py @@ -343,7 +343,7 @@ def _check_alpha(self, alpha): return alpha def _handle_multioutput(self, loss, multioutput): - """Specificies how multivariate outputs should be handled. + """Handle output according to multioutput parameter. Parameters ---------- diff --git a/skpro/regression/cyclic_boosting.py b/skpro/regression/cyclic_boosting.py index ad4bf836a..39c2ce0ad 100644 --- a/skpro/regression/cyclic_boosting.py +++ b/skpro/regression/cyclic_boosting.py @@ -70,7 +70,7 @@ class CyclicBoosting(BaseProbaRegressor): lower : float, default=None lower bound of predictive distribution support. If ``None`` (default), ``upper`` should also be ``None``, and the - predictive distibution will have unbounded support, i.e., the entire reals. + predictive distribution will have unbounded support, i.e., the entire reals. If a float, and ``upper`` is ``None``, prediction will be of semi-bounded support, with support between ``lower`` and infinity. If a float, and ``upper`` is also a float, prediction will be on a bounded @@ -84,7 +84,7 @@ class CyclicBoosting(BaseProbaRegressor): maximal_iterations : int, default=10 maximum number of iterations for the cyclic boosting algorithm dist_type: str, one of ``'normal'`` (default), ``'logistic'`` - inner base distirbution to use for the Johnson QPD, i.e., before + inner base distribution to use for the Johnson QPD, i.e., before arcosh and similar transformations. Available options are ``'normal'`` (default), ``'logistic'``, or ``'sinhlogistic'``. diff --git a/skpro/regression/density.py b/skpro/regression/density.py index 6383db0bf..c4d5f6837 100644 --- a/skpro/regression/density.py +++ b/skpro/regression/density.py @@ -53,7 +53,7 @@ def func(x): class DensityAdapter(BaseEstimator, metaclass=abc.ABCMeta): """ - Abtract base class for density adapter + Abstract base class for density adapter that transform an input into an density cdf/pdf interface """ diff --git a/skpro/regression/tests/test_cyclic_boosting.py b/skpro/regression/tests/test_cyclic_boosting.py index 3731850c3..ae01071e2 100644 --- a/skpro/regression/tests/test_cyclic_boosting.py +++ b/skpro/regression/tests/test_cyclic_boosting.py @@ -33,8 +33,8 @@ def test_cyclic_boosting_simple_use(): not run_test_for_class(CyclicBoosting), reason="run test only if softdeps are present and incrementally (if requested)", ) -def test_cyclic_boosting_with_manual_paramaters(): - """Test use of cyclic boosting regressor with_manual_paramaters.""" +def test_cyclic_boosting_with_manual_parameters(): + """Test use of cyclic boosting regressor with_manual_parameters.""" from cyclic_boosting import flags from sklearn.datasets import load_diabetes from sklearn.model_selection import train_test_split diff --git a/skpro/survival/ensemble/_survforest_sksurv.py b/skpro/survival/ensemble/_survforest_sksurv.py index 2afcc602d..de7a860e7 100644 --- a/skpro/survival/ensemble/_survforest_sksurv.py +++ b/skpro/survival/ensemble/_survforest_sksurv.py @@ -81,7 +81,7 @@ class SurvivalForestSkSurv(_SksurvAdapter, BaseSurvReg): bootstrap : boolean, optional, default: True Whether bootstrap samples are used when building trees. If False, the - whole datset is used to build each tree. + whole dataset is used to build each tree. oob_score : bool, default: False Whether to use out-of-bag samples to estimate @@ -293,7 +293,7 @@ class SurvivalForestXtraSkSurv(_SksurvAdapter, BaseSurvReg): bootstrap : boolean, optional, default: True Whether bootstrap samples are used when building trees. If False, the - whole datset is used to build each tree. + whole dataset is used to build each tree. oob_score : bool, default: False Whether to use out-of-bag samples to estimate diff --git a/skpro/tests/scenarios/scenarios_getter.py b/skpro/tests/scenarios/scenarios_getter.py index a867fafdb..0ac490ca0 100644 --- a/skpro/tests/scenarios/scenarios_getter.py +++ b/skpro/tests/scenarios/scenarios_getter.py @@ -27,7 +27,7 @@ def retrieve_scenarios(obj, filter_tags=None): If string(s), must be in registry.BASE_CLASS_REGISTER (first col) for instance 'classifier', 'regressor', 'transformer', 'forecaster' filter_tags: dict of (str or list of str), default=None - subsets the returned objectss as follows: + subsets the returned objects as follows: each key/value pair is statement in "and"/conjunction key is tag name to sub-set on value str or list of string are tag values @@ -85,7 +85,7 @@ def _check_tag_cond(obj, filter_tags=None): ---------- obj: object inheriting from sktime BaseObject filter_tags: dict of (str or list of str), default=None - subsets the returned objectss as follows: + subsets the returned objects as follows: each key/value pair is statement in "and"/conjunction key is tag name to sub-set on value str or list of string are tag values diff --git a/skpro/utils/_maint/tests/test_show_versions.py b/skpro/utils/_maint/tests/test_show_versions.py index 2e7944d1c..928017df1 100644 --- a/skpro/utils/_maint/tests/test_show_versions.py +++ b/skpro/utils/_maint/tests/test_show_versions.py @@ -9,7 +9,7 @@ def test_show_versions_runs(): - """Test that show_versions runs without exeptions.""" + """Test that show_versions runs without exceptions.""" # only prints, should return None assert show_versions() is None diff --git a/skpro/utils/numpy.py b/skpro/utils/numpy.py index 2d39189d3..c16ecdaec 100644 --- a/skpro/utils/numpy.py +++ b/skpro/utils/numpy.py @@ -15,7 +15,7 @@ def flatten_to_1D_if_colvector(y): ------- y_flat : numpy array 1D flattened array if y was 2D column vector, or 1D already - otherwise, returne y unchanged + otherwise, return y unchanged """ if len(y.shape) == 2 and y.shape[1] == 1: y_flat = y.flatten()