MNT Fix some easy-to-make typos (scikit-learn#15720)

build_tools/azure/install.sh

@@ -11,7 +11,7 @@ make_conda() {
 }
 
 version_ge() {
-    # The two version numbers are seperated with a new line is piped to sort
+    # The two version numbers are separated with a new line is piped to sort
     # -rV. The -V activates for version number sorting and -r sorts in
     # decending order. If the first argument is the top element of the sort, it
     # is greater than or equal to the second argument.

doc/developers/advanced_installation.rst

@@ -374,7 +374,7 @@ Finally, build the package using the standard command::
 
     pip install --verbose --editable .
 
-For the upcomming FreeBSD 12.1 and 11.3 versions, OpenMP will be included in
+For the upcoming FreeBSD 12.1 and 11.3 versions, OpenMP will be included in
 the base system and these steps will not be necessary.
 
 .. _OpenMP: https://en.wikipedia.org/wiki/OpenMP

doc/modules/computing.rst

@@ -529,7 +529,7 @@ Joblib-based parallelism
 ........................
 
 When the underlying implementation uses joblib, the number of workers
-(threads or processes) that are spawned in parallel can be controled via the
+(threads or processes) that are spawned in parallel can be controlled via the
 ``n_jobs`` parameter.
 
 .. note::
@@ -666,7 +666,7 @@ Python runtime
 
 :working_memory:
 
-    the optimal size of temporary arrays used by some algoritms.
+    the optimal size of temporary arrays used by some algorithms.
 
 .. _environment_variable:
 

doc/modules/model_evaluation.rst

@@ -1720,7 +1720,7 @@ relevant), NDCG can be used.
 
 For one sample, given the vector of continuous ground-truth values for each
 target :math:`y \in \mathbb{R}^{M}`, where :math:`M` is the number of outputs, and
-the prediction :math:`\hat{y}`, which induces the ranking funtion :math:`f`, the
+the prediction :math:`\hat{y}`, which induces the ranking function :math:`f`, the
 DCG score is
 
 .. math::

doc/modules/neighbors.rst

@@ -581,7 +581,7 @@ implementation with special data types. The precomputed neighbors
   training point as its own neighbor in the count of `n_neighbors`. However,
   for compatibility reasons with other estimators which use the other
   definition, one extra neighbor will be computed when `mode == 'distance'`.
-  To maximise compatiblity with all estimators, a safe choice is to always
+  To maximise compatibility with all estimators, a safe choice is to always
   include one extra neighbor in a custom nearest neighbors estimator, since
   unnecessary neighbors will be filtered by following estimators.
 

doc/whats_new/v0.20.rst

@@ -709,7 +709,7 @@ Support for Python 3.3 has been officially dropped.
 
 - |Feature| |Fix| :class:`decomposition.SparsePCA` now exposes
   ``normalize_components``. When set to True, the train and test data are
-  centered with the train mean repsectively during the fit phase and the
+  centered with the train mean respectively during the fit phase and the
   transform phase. This fixes the behavior of SparsePCA. When set to False,
   which is the default, the previous abnormal behaviour still holds. The False
   value is for backward compatibility and should not be used. :issue:`11585`

doc/whats_new/v0.21.rst

@@ -295,7 +295,7 @@ Support for Python 3.4 and below has been officially dropped.
 ......................
 
 - |MajorFeature| A new clustering algorithm: :class:`cluster.OPTICS`: an
-  algoritm related to :class:`cluster.DBSCAN`, that has hyperparameters easier
+  algorithm related to :class:`cluster.DBSCAN`, that has hyperparameters easier
   to set and that scales better, by :user:`Shane <espg>`,
   `Adrin Jalali`_, :user:`Erich Schubert <kno10>`, `Hanmin Qin`_, and
   :user:`Assia Benbihi <assiaben>`.

doc/whats_new/v0.22.rst

@@ -799,7 +799,7 @@ Changelog
 - |Fix| :class:`svm.SVC`, :class:`svm.SVR`, :class:`svm.NuSVR` and
   :class:`svm.OneClassSVM` when received values negative or zero
   for parameter ``sample_weight`` in method fit(), generated an
-  invalid model. This behavior occured only in some border scenarios.
+  invalid model. This behavior occurred only in some border scenarios.
   Now in these cases, fit() will fail with an Exception.
   :pr:`14286` by :user:`Alex Shacked <alexshacked>`.
 

examples/inspection/plot_partial_dependence.py

@@ -14,7 +14,7 @@
 :class:`~sklearn.ensemble.HistGradientBoostingRegressor` trained on the
 California housing dataset. The example is taken from [1]_.
 
-The plots show four 1-way and two 1-way partial dependence plots (ommitted for
+The plots show four 1-way and two 1-way partial dependence plots (omitted for
 :class:`~sklearn.neural_network.MLPRegressor` due to computation time). The
 target variables for the one-way PDP are: median income (`MedInc`), average
 occupants per household (`AvgOccup`), median house age (`HouseAge`), and

sklearn/decomposition/_dict_learning.py

@@ -704,7 +704,7 @@ def dict_learning_online(X, n_components=2, alpha=1, n_iter=100,
     inner_stats : tuple of (A, B) ndarrays
         Inner sufficient statistics that are kept by the algorithm.
         Passing them at initialization is useful in online settings, to
-        avoid loosing the history of the evolution.
+        avoid losing the history of the evolution.
         A (n_components, n_components) is the dictionary covariance matrix.
         B (n_features, n_components) is the data approximation matrix
 
@@ -1351,7 +1351,7 @@ class MiniBatchDictionaryLearning(SparseCodingMixin, BaseEstimator):
 
     inner_stats_ : tuple of (A, B) ndarrays
         Internal sufficient statistics that are kept by the algorithm.
-        Keeping them is useful in online settings, to avoid loosing the
+        Keeping them is useful in online settings, to avoid losing the
         history of the evolution, but they shouldn't have any use for the
         end user.
         A (n_components, n_components) is the dictionary covariance matrix.

sklearn/ensemble/_hist_gradient_boosting/binning.py

@@ -32,7 +32,7 @@ def _find_binning_thresholds(data, max_bins, subsample, random_state):
         instead of the quantiles.
     subsample : int or None
         If ``n_samples > subsample``, then ``sub_samples`` samples will be
-        randomly choosen to compute the quantiles. If ``None``, the whole data
+        randomly chosen to compute the quantiles. If ``None``, the whole data
         is used.
     random_state: int or numpy.random.RandomState or None
         Pseudo-random number generator to control the random sub-sampling.
@@ -107,7 +107,7 @@ class _BinMapper(TransformerMixin, BaseEstimator):
         instead of the quantiles.
     subsample : int or None, optional (default=2e5)
         If ``n_samples > subsample``, then ``sub_samples`` samples will be
-        randomly choosen to compute the quantiles. If ``None``, the whole data
+        randomly chosen to compute the quantiles. If ``None``, the whole data
         is used.
     random_state: int or numpy.random.RandomState or None, \
         optional (default=None)
@@ -126,7 +126,7 @@ class _BinMapper(TransformerMixin, BaseEstimator):
         equal to ``n_bins - 1``.
     missing_values_bin_idx_ : uint8
         The index of the bin where missing values are mapped. This is a
-        constant accross all features. This corresponds to the last bin, and
+        constant across all features. This corresponds to the last bin, and
         it is always equal to ``n_bins - 1``. Note that if ``n_bins_missing_``
         is less than ``n_bins - 1`` for a given feature, then there are
         empty (and unused) bins.

sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py

@@ -413,7 +413,7 @@ def test_infinite_values_missing_values():
     # High level test making sure that inf and nan values are properly handled
     # when both are present. This is similar to
     # test_split_on_nan_with_infinite_values() in test_grower.py, though we
-    # cannot check the predicitons for binned values here.
+    # cannot check the predictions for binned values here.
 
     X = np.asarray([-np.inf, 0, 1, np.inf, np.nan]).reshape(-1, 1)
     y_isnan = np.isnan(X.ravel())

sklearn/ensemble/tests/test_gradient_boosting.py

@@ -1311,7 +1311,7 @@ def test_gradient_boosting_with_init(gb, dataset_maker, init_estimator):
     # Check that GradientBoostingRegressor works when init is a sklearn
     # estimator.
     # Check that an error is raised if trying to fit with sample weight but
-    # inital estimator does not support sample weight
+    # initial estimator does not support sample weight
 
     X, y = dataset_maker()
     sample_weight = np.random.RandomState(42).rand(100)

sklearn/externals/_arff.py

@@ -98,7 +98,7 @@
 The above keys must follow the case which were described, i.e., the keys are
 case sensitive. The attribute type ``attribute_type`` must be one of these
 strings (they are not case sensitive): ``NUMERIC``, ``INTEGER``, ``REAL`` or
-``STRING``. For nominal attributes, the ``atribute_type`` must be a list of
+``STRING``. For nominal attributes, the ``attribute_type`` must be a list of
 strings.
 
 In this format, the XOR dataset presented above can be represented as a python

sklearn/metrics/_regression.py

@@ -717,7 +717,7 @@ def mean_tweedie_deviance(y_true, y_pred, sample_weight=None, power=0):
     message = ("Mean Tweedie deviance error with power={} can only be used on "
                .format(power))
     if power < 0:
-        # 'Extreme stable', y_true any realy number, y_pred > 0
+        # 'Extreme stable', y_true any real number, y_pred > 0
         if (y_pred <= 0).any():
             raise ValueError(message + "strictly positive y_pred.")
         dev = 2 * (np.power(np.maximum(y_true, 0), 2 - power)

sklearn/metrics/tests/test_common.py

@@ -115,7 +115,7 @@
     "unnormalized_accuracy_score": partial(accuracy_score, normalize=False),
 
     # `confusion_matrix` returns absolute values and hence behaves unnormalized
-    # . Naming it with an unnormalized_ prefix is neccessary for this module to
+    # . Naming it with an unnormalized_ prefix is necessary for this module to
     # skip sample_weight scaling checks which will fail for unnormalized
     # metrics.
     "unnormalized_confusion_matrix": confusion_matrix,

sklearn/metrics/tests/test_score_objects.py

@@ -649,7 +649,7 @@ def predict(self, X):
 
 
 def test_multimetric_scorer_sanity_check():
-    # scoring dictionary returned is the same as calling each scorer seperately
+    # scoring dictionary returned is the same as calling each scorer separately
     scorers = {'a1': 'accuracy', 'a2': 'accuracy',
                'll1': 'neg_log_loss', 'll2': 'neg_log_loss',
                'ra1': 'roc_auc', 'ra2': 'roc_auc'}
@@ -664,13 +664,13 @@ def test_multimetric_scorer_sanity_check():
 
     result = multi_scorer(clf, X, y)
 
-    seperate_scores = {
+    separate_scores = {
         name: get_scorer(name)(clf, X, y)
         for name in ['accuracy', 'neg_log_loss', 'roc_auc']}
 
     for key, value in result.items():
         score_name = scorers[key]
-        assert_allclose(value, seperate_scores[score_name])
+        assert_allclose(value, separate_scores[score_name])
 
 
 @pytest.mark.parametrize('scorer_name, metric', [

sklearn/model_selection/_search.py

@@ -948,7 +948,7 @@ class GridSearchCV(BaseSearchCV):
         returns the selected ``best_index_`` given ``cv_results_``. In that
         case, the ``best_estimator_`` and ``best_parameters_`` will be set
         according to the returned ``best_index_`` while the ``best_score_``
-        attribute will not be availble.
+        attribute will not be available.
 
         The refitted estimator is made available at the ``best_estimator_``
         attribute and permits using ``predict`` directly on this
@@ -1278,7 +1278,7 @@ class RandomizedSearchCV(BaseSearchCV):
         returns the selected ``best_index_`` given the ``cv_results``. In that
         case, the ``best_estimator_`` and ``best_parameters_`` will be set
         according to the returned ``best_index_`` while the ``best_score_``
-        attribute will not be availble.
+        attribute will not be available.
 
         The refitted estimator is made available at the ``best_estimator_``
         attribute and permits using ``predict`` directly on this