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‎benchmarks/bench_mnist.py

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Benchmark on the MNIST dataset. The dataset comprises 70,000 samples
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and 784 features. Here, we consider the task of predicting
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10 classes - digits from 0 to 9 from their raw images. By contrast to the
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covertype dataset, the feature space is homogenous.
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covertype dataset, the feature space is homogeneous.
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Example of output :
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[..]

‎benchmarks/bench_random_projections.py

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return delta.seconds + delta.microseconds / mu_second
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def bench_scikit_transformer(X, transfomer):
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def bench_scikit_transformer(X, transformer):
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gc.collect()
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clf = clone(transfomer)
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clf = clone(transformer)
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# start time
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t_start = datetime.now()
@@ -195,7 +195,7 @@ def print_row(clf_type, time_fit, time_transform):
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###########################################################################
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n_nonzeros = int(opts.ratio_nonzeros * opts.n_features)
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print("Dataset statics")
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print("Dataset statistics")
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print("===========================")
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print("n_samples \t= %s" % opts.n_samples)
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print("n_features \t= %s" % opts.n_features)

‎build_tools/azure/posix-docker.yml

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${{ insert }}: ${{ parameters.matrix }}
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steps:
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# Container is detached and sleeping, allowing steps to run commmands
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# Container is detached and sleeping, allowing steps to run commands
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# in the container. The TEST_DIR is mapped allowing the host to access
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# the JUNITXML file
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- script: >

‎build_tools/circle/list_versions.py

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@@ -34,7 +34,7 @@ def human_readable_data_quantity(quantity, multiple=1024):
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def get_file_extension(version):
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if "dev" in version:
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# The 'dev' branch should be explictly handled
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# The 'dev' branch should be explicitly handled
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return "zip"
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current_version = LooseVersion(version)

‎build_tools/shared.sh

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@@ -5,7 +5,7 @@ get_dep() {
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# do not install with none
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echo
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elif [[ "${version%%[^0-9.]*}" ]]; then
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# version number is explicity passed
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# version number is explicitly passed
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echo "$package==$version"
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elif [[ "$version" == "latest" ]]; then
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# use latest

‎doc/common_pitfalls.rst

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@@ -560,7 +560,7 @@ bad performance. Similarly, we want a random forest to be robust w.r.t the
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set of randomly selected features that each tree will be using.
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For these reasons, it is preferable to evaluate the cross-validation
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preformance by letting the estimator use a different RNG on each fold. This
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performance by letting the estimator use a different RNG on each fold. This
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is done by passing a `RandomState` instance (or `None`) to the estimator
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initialization.
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‎doc/conf.py

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"release_highlights"
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] = f"auto_examples/release_highlights/{latest_highlights}"
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# get version from higlight name assuming highlights have the form
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# get version from highlight name assuming highlights have the form
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# plot_release_highlights_0_22_0
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highlight_version = ".".join(latest_highlights.split("_")[-3:-1])
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html_context["release_highlights_version"] = highlight_version

‎doc/developers/advanced_installation.rst

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@@ -377,7 +377,7 @@ isolation from the Python packages installed via the system packager. When
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using an isolated environment, ``pip3`` should be replaced by ``pip`` in the
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above commands.
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When precompiled wheels of the runtime dependencies are not avalaible for your
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When precompiled wheels of the runtime dependencies are not available for your
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architecture (e.g. ARM), you can install the system versions:
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.. prompt:: bash $

‎doc/developers/contributing.rst

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@@ -1004,7 +1004,7 @@ installed in your current Python environment:
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asv run --python=same
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It's particulary useful when you installed scikit-learn in editable mode to
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It's particularly useful when you installed scikit-learn in editable mode to
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avoid creating a new environment each time you run the benchmarks. By default
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the results are not saved when using an existing installation. To save the
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results you must specify a commit hash:

‎doc/developers/maintainer.rst

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@@ -33,7 +33,7 @@ Before a release
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- ``maint_tools/sort_whats_new.py`` can put what's new entries into
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sections. It's not perfect, and requires manual checking of the changes.
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If the whats new list is well curated, it may not be necessary.
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If the what's new list is well curated, it may not be necessary.
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- The ``maint_tools/whats_missing.sh`` script may be used to identify pull
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requests that were merged but likely missing from What's New.
@@ -198,7 +198,7 @@ Making a release
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`Continuous Integration
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<https://en.wikipedia.org/wiki/Continuous_integration>`_. The CD workflow on
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GitHub Actions is also used to automatically create nightly builds and
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publish packages for the developement branch of scikit-learn. See
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publish packages for the development branch of scikit-learn. See
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:ref:`install_nightly_builds`.
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4. Once all the CD jobs have completed successfully in the PR, merge it,

‎doc/install.rst

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@@ -158,7 +158,7 @@ Installing on Apple Silicon M1 hardware
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The recently introduced `macos/arm64` platform (sometimes also known as
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`macos/aarch64`) requires the open source community to upgrade the build
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configuation and automation to properly support it.
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configuration and automation to properly support it.
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At the time of writing (January 2021), the only way to get a working
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installation of scikit-learn on this hardware is to install scikit-learn and its
@@ -204,7 +204,7 @@ It can be installed by typing the following command:
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Debian/Ubuntu
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-------------
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The Debian/Ubuntu package is splitted in three different packages called
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The Debian/Ubuntu package is split in three different packages called
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``python3-sklearn`` (python modules), ``python3-sklearn-lib`` (low-level
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implementations and bindings), ``python3-sklearn-doc`` (documentation).
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Only the Python 3 version is available in the Debian Buster (the more recent

‎doc/modules/compose.rst

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@@ -573,7 +573,7 @@ many estimators. This visualization is activated by setting the
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>>> from sklearn import set_config
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>>> set_config(display='diagram') # doctest: +SKIP
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>>> # diplays HTML representation in a jupyter context
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>>> # displays HTML representation in a jupyter context
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>>> column_trans # doctest: +SKIP
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An example of the HTML output can be seen in the

‎doc/modules/cross_decomposition.rst

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@@ -64,7 +64,7 @@ Set :math:`X_1` to :math:`X` and :math:`Y_1` to :math:`Y`. Then, for each
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:math:`C = X_k^T Y_k`.
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:math:`u_k` and :math:`v_k` are called the *weights*.
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By definition, :math:`u_k` and :math:`v_k` are
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choosen so that they maximize the covariance between the projected
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chosen so that they maximize the covariance between the projected
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:math:`X_k` and the projected target, that is :math:`\text{Cov}(X_k u_k,
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Y_k v_k)`.
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- b) Project :math:`X_k` and :math:`Y_k` on the singular vectors to obtain

‎doc/modules/cross_validation.rst

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@@ -974,7 +974,7 @@ test is therefore only able to show when the model reliably outperforms
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random guessing.
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Finally, :func:`~sklearn.model_selection.permutation_test_score` is computed
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using brute force and interally fits ``(n_permutations + 1) * n_cv`` models.
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using brute force and internally fits ``(n_permutations + 1) * n_cv`` models.
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It is therefore only tractable with small datasets for which fitting an
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individual model is very fast.
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‎doc/modules/decomposition.rst

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@@ -829,7 +829,7 @@ and the intensity of the regularization with the :attr:`alpha_W` and :attr:`alph
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(:math:`\alpha_W` and :math:`\alpha_H`) parameters. The priors are scaled by the number
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of samples (:math:`n\_samples`) for `H` and the number of features (:math:`n\_features`)
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for `W` to keep their impact balanced with respect to one another and to the data fit
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term as independant as possible of the size of the training set. Then the priors terms
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term as independent as possible of the size of the training set. Then the priors terms
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are:
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.. math::

‎doc/modules/lda_qda.rst

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@@ -187,7 +187,7 @@ an estimate for the covariance matrix). Setting this parameter to a value
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between these two extrema will estimate a shrunk version of the covariance
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matrix.
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The shrinked Ledoit and Wolf estimator of covariance may not always be the
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The shrunk Ledoit and Wolf estimator of covariance may not always be the
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best choice. For example if the distribution of the data
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is normally distributed, the
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Oracle Shrinkage Approximating estimator :class:`sklearn.covariance.OAS`
@@ -234,7 +234,7 @@ For QDA, the use of the SVD solver relies on the fact that the covariance
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matrix :math:`\Sigma_k` is, by definition, equal to :math:`\frac{1}{n - 1}
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X_k^tX_k = \frac{1}{n - 1} V S^2 V^t` where :math:`V` comes from the SVD of the (centered)
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matrix: :math:`X_k = U S V^t`. It turns out that we can compute the
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log-posterior above without having to explictly compute :math:`\Sigma`:
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log-posterior above without having to explicitly compute :math:`\Sigma`:
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computing :math:`S` and :math:`V` via the SVD of :math:`X` is enough. For
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LDA, two SVDs are computed: the SVD of the centered input matrix :math:`X`
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and the SVD of the class-wise mean vectors.

‎doc/modules/model_evaluation.rst

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A scorer object with a specific choice of ``power`` can be built by::
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>>> from sklearn.metrics import d2_tweedie_score, make_scorer
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>>> d2_tweedie_score_15 = make_scorer(d2_tweedie_score, pwoer=1.5)
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>>> d2_tweedie_score_15 = make_scorer(d2_tweedie_score, power=1.5)
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.. _pinball_loss:
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‎doc/modules/outlier_detection.rst

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so that other objects can be local outliers relative to this cluster, and 2)
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smaller than the maximum number of close by objects that can potentially be
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local outliers.
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In practice, such informations are generally not available, and taking
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In practice, such information are generally not available, and taking
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n_neighbors=20 appears to work well in general.
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When the proportion of outliers is high (i.e. greater than 10 \%, as in the
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example below), n_neighbors should be greater (n_neighbors=35 in the example

‎doc/modules/sgd.rst

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The first two loss functions are lazy, they only update the model
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parameters if an example violates the margin constraint, which makes
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training very efficient and may result in sparser models (i.e. with more zero
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coefficents), even when L2 penalty is used.
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coefficients), even when L2 penalty is used.
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Using ``loss="log"`` or ``loss="modified_huber"`` enables the
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``predict_proba`` method, which gives a vector of probability estimates
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where :math:`L` is a loss function that measures model (mis)fit and
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:math:`R` is a regularization term (aka penalty) that penalizes model
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complexity; :math:`\alpha > 0` is a non-negative hyperparameter that controls
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the regularization stength.
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the regularization strength.
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Different choices for :math:`L` entail different classifiers or regressors:
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‎doc/modules/svm.rst

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(w^T \phi (x_i) + b)` would be :math:`\geq 1` for all samples, which
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indicates a perfect prediction. But problems are usually not always perfectly
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separable with a hyperplane, so we allow some samples to be at a distance :math:`\zeta_i` from
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their correct margin boundary. The penalty term `C` controls the strengh of
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their correct margin boundary. The penalty term `C` controls the strength of
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this penalty, and as a result, acts as an inverse regularization parameter
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(see note below).
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‎doc/roadmap.rst

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< B41A tbody>
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(i.e. rectangular data largely invariant to column and row order;
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predicting targets with simple structure)
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* improve the ease for users to develop and publish external components
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* improve inter-operability with modern data science tools (e.g. Pandas, Dask)
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* improve interoperability with modern data science tools (e.g. Pandas, Dask)
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and infrastructures (e.g. distributed processing)
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Many of the more fine-grained goals can be found under the `API tag

‎doc/themes/scikit-learn-modern/static/css/theme.css

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text-align: center
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}
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/* pygments - highlightning */
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/* pygments - highlighting */
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.highlight .hll { background-color: #ffffcc }
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.highlight { background: #f8f8f8; }

‎doc/tutorial/machine_learning_map/ML_MAPS_README.txt

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(https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html)
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The image is made interactive using an imagemap, and uses the jQuery Map Hilight plugin module
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The image is made interactive using an imagemap, and uses the jQuery Map Highlight plugin module
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by David Lynch (https://davidlynch.org/projects/maphilight/docs/) to highlight
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the different items on the image upon mouseover.
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‎doc/tutorial/machine_learning_map/pyparsing.py

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def __init__( self, quoteChar, escChar=None, escQuote=None, multiline=False, unquoteResults=True, endQuoteChar=None, convertWhitespaceEscapes=True):
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super(QuotedString,self).__init__()
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# remove white space from quote chars - wont work anyway
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# remove white space from quote chars - won't work anyway
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quoteChar = quoteChar.strip()
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if not quoteChar:
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warnings.warn("quoteChar cannot be the empty string",SyntaxWarning,stacklevel=2)

‎doc/whats_new/v0.16.rst

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- Out-of core learning of PCA via :class:`decomposition.IncrementalPCA`.
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- Probability callibration of classifiers using
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- Probability calibration of classifiers using
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:class:`calibration.CalibratedClassifierCV`.
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- :class:`cluster.Birch` clustering method for large-scale datasets.

‎doc/whats_new/v0.20.rst

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be used for novelty detection, i.e. predict on new unseen data. Available
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prediction methods are ``predict``, ``decision_function`` and
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``score_samples``. By default, ``novelty`` is set to ``False``, and only
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the ``fit_predict`` method is avaiable.
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the ``fit_predict`` method is available.
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By :user:`Albert Thomas <albertcthomas>`.
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- |Fix| Fixed a bug in :class:`neighbors.NearestNeighbors` where fitting a

‎doc/whats_new/v0.21.rst

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- Add ``check_fit_idempotent`` to
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:func:`~utils.estimator_checks.check_estimator`, which checks that
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when `fit` is called twice with the same data, the ouput of
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when `fit` is called twice with the same data, the output of
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`predict`, `predict_proba`, `transform`, and `decision_function` does not
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change. :pr:`12328` by :user:`Nicolas Hug <NicolasHug>`
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‎doc/whats_new/v0.23.rst

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:pr:`16006` by :user:`Rushabh Vasani <rushabh-v>`.
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- |API| The `StreamHandler` was removed from `sklearn.logger` to avoid
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double logging of messages in common cases where a hander is attached
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double logging of messages in common cases where a handler is attached
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to the root logger, and to follow the Python logging documentation
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recommendation for libraries to leave the log message handling to
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users and application code. :pr:`16451` by :user:`Christoph Deil <cdeil>`.

‎doc/whats_new/v0.24.rst

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:user:`Joseph Willard <josephwillard>`
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- |Fix| bug in :func:`metrics.hinge_loss` where error occurs when
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``y_true`` is missing some labels that are provided explictly in the
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``y_true`` is missing some labels that are provided explicitly in the
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``labels`` parameter.
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:pr:`17935` by :user:`Cary Goltermann <Ultramann>`.
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‎examples/applications/plot_cyclical_feature_engineering.py

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# %%
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#
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# Lets evaluate our gradient boosting model with the mean absolute error of the
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# relative demand averaged accross our 5 time-based cross-validation splits:
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# relative demand averaged across our 5 time-based cross-validation splits:
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def evaluate(model, X, y, cv):

‎examples/calibration/plot_calibration_multiclass.py

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print(f" * calibrated classifier: {cal_score:.3f}")
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# %%
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# Finally we generate a grid of possibile uncalibrated probabilities over
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# Finally we generate a grid of possible uncalibrated probabilities over
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# the 2-simplex, compute the corresponding calibrated probabilities and
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# plot arrows for each. The arrows are colored according the highest
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# uncalibrated probability. This illustrates the learned calibration map:

‎examples/covariance/plot_mahalanobis_distances.py

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# are Gaussian distributed with mean of 0 but feature 1 has a standard
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# deviation equal to 2 and feature 2 has a standard deviation equal to 1. Next,
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# 25 samples are replaced with Gaussian outlier samples where feature 1 has
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# a standard devation equal to 1 and feature 2 has a standard deviation equal
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# a standard deviation equal to 1 and feature 2 has a standard deviation equal
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# to 7.
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import numpy as np

‎examples/cross_decomposition/plot_pcr_vs_pls.py

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#
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# On the other hand, the PLS regressor manages to capture the effect of the
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# direction with the lowest variance, thanks to its use of target information
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# during the transformation: it can recogize that this direction is actually
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# during the transformation: it can recognize that this direction is actually
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# the most predictive. We note that the first PLS component is negatively
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# correlated with the target, which comes from the fact that the signs of
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# eigenvectors are arbitrary.

‎examples/ensemble/plot_gradient_boosting_early_stopping.py

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model is trained using the training set and evaluated using the validation set.
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When each additional stage of regression tree is added, the validation set is
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used to score the model. This is continued until the scores of the model in
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the last ``n_iter_no_change`` stages do not improve by atleast `tol`. After
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the last ``n_iter_no_change`` stages do not improve by at least `tol`. After
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that the model is considered to have converged and further addition of stages
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is "stopped early".
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
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random_state=0)
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# We specify that if the scores don't improve by atleast 0.01 for the last
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# We specify that if the scores don't improve by at least 0.01 for the last
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# 10 stages, stop fitting additional stages
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gbes = ensemble.GradientBoostingClassifier(n_estimators=n_estimators,
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validation_fraction=0.2,

‎examples/ensemble/plot_gradient_boosting_quantile.py

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# the fact the squared error estimator is very sensitive to large outliers
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# which can cause significant overfitting. This can be seen on the right hand
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# side of the previous plot. The conditional median estimator is biased
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# (underestimation for this asymetric noise) but is also naturally robust to
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# (underestimation for this asymmetric noise) but is also naturally robust to
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# outliers and overfits less.
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#
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# Calibration of the confidence interval

‎examples/inspection/plot_linear_model_coefficient_interpretation.py

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# %%
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# Two regions are populated: when the EXPERIENCE coefficient is
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# positive the AGE one is negative and viceversa.
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# positive the AGE one is negative and vice-versa.
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#
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# To go further we remove one of the 2 features and check what is the impact
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# on the model stability.
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# It is important to keep in mind that the coefficients that have been
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# dropped may still be related to the outcome by themselves: the model
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# chose to suppress them because they bring little or no additional
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# information on top of the other features. Additionnaly, this selection
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# information on top of the other features. Additionally, this selection
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# is unstable for correlated features, and should be interpreted with
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# caution.
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#

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