MAINT Fix several typos in src and doc files

ChVeen · ChVeen · commit 479e4ef216cf · 2023-04-15T15:23:48.000Z
diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst
@@ -548,8 +548,8 @@ message, the following actions are taken.
     [cd build gh]          CD is run only for GitHub Actions
     [cd build cirrus]      CD is run only for Cirrus CI
     [lint skip]            Azure pipeline skips linting
-    [scipy-dev]            Build & test with our dependencies (numpy, scipy, etc ...) development builds
-    [nogil]                Build & test with the nogil experimental branches of CPython, Cython, NumPy, SciPy...
+    [scipy-dev]            Build & test with our dependencies (numpy, scipy, etc) development builds
+    [nogil]                Build & test with the nogil experimental branches of CPython, Cython, NumPy, SciPy, ...
     [pypy]                 Build & test with PyPy
     [azure parallel]       Run Azure CI jobs in parallel
     [float32]              Run float32 tests by setting `SKLEARN_RUN_FLOAT32_TESTS=1`. See :ref:`environment_variable` for more details
diff --git a/doc/getting_started.rst b/doc/getting_started.rst
@@ -37,8 +37,8 @@ The :term:`fit` method generally accepts 2 inputs:
   represented as rows and features are represented as columns.
 - The target values :term:`y` which are real numbers for regression tasks, or
   integers for classification (or any other discrete set of values). For
-  unsupervized learning tasks, ``y`` does not need to be specified. ``y`` is
-  usually 1d array where the ``i`` th entry corresponds to the target of the
+  unsupervised learning tasks, ``y`` does not need to be specified. ``y`` is
+  usually a 1d array where the ``i`` th entry corresponds to the target of the
   ``i`` th sample (row) of ``X``.
 
 Both ``X`` and ``y`` are usually expected to be numpy arrays or equivalent
diff --git a/doc/modules/cross_decomposition.rst b/doc/modules/cross_decomposition.rst
@@ -28,7 +28,7 @@ PLS draws similarities with `Principal Component Regression
 <https://en.wikipedia.org/wiki/Principal_component_regression>`_ (PCR), where
 the samples are first projected into a lower-dimensional subspace, and the
 targets `y` are predicted using `transformed(X)`. One issue with PCR is that
-the dimensionality reduction is unsupervized, and may lose some important
+the dimensionality reduction is unsupervised, and may lose some important
 variables: PCR would keep the features with the most variance, but it's
 possible that features with a small variances are relevant from predicting
 the target. In a way, PLS allows for the same kind of dimensionality
diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
@@ -316,7 +316,7 @@ def confusion_matrix(
            [0, 0, 1],
            [1, 0, 2]])
 
-    In the binary case, we can extract true positives, etc as follows:
+    In the binary case, we can extract true positives, etc. as follows:
 
     >>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
     >>> (tn, fp, fn, tp)
diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
@@ -379,7 +379,7 @@ def test_no_refit():
             and hasattr(grid_search, "best_params_")
         )
 
-        # Make sure the functions predict/transform etc raise meaningful
+        # Make sure the functions predict/transform etc. raise meaningful
         # error messages
         for fn_name in (
             "predict",
diff --git a/sklearn/neural_network/_multilayer_perceptron.py b/sklearn/neural_network/_multilayer_perceptron.py
@@ -360,7 +360,7 @@ def _backprop(self, X, y, activations, deltas, coef_grads, intercept_grads):
         return loss, coef_grads, intercept_grads
 
     def _initialize(self, y, layer_units, dtype):
-        # set all attributes, allocate weights etc for first call
+        # set all attributes, allocate weights etc. for first call
         # Initialize parameters
         self.n_iter_ = 0
         self.t_ = 0
diff --git a/sklearn/utils/tests/test_class_weight.py b/sklearn/utils/tests/test_class_weight.py
@@ -274,7 +274,7 @@ def test_compute_sample_weight_more_than_32():
     assert_array_almost_equal(weight, np.ones(y.shape[0]))
 
 
-def test_class_weight_does_not_contains_more_classses():
+def test_class_weight_does_not_contains_more_classes():
     """Check that class_weight can contain more labels than in y.
 
     Non-regression test for #22413
diff --git a/sklearn/utils/tests/test_estimator_html_repr.py b/sklearn/utils/tests/test_estimator_html_repr.py
@@ -205,7 +205,7 @@ def test_estimator_html_repr_pipeline():
 
 
 @pytest.mark.parametrize("final_estimator", [None, LinearSVC()])
-def test_stacking_classsifer(final_estimator):
+def test_stacking_classifier(final_estimator):
     estimators = [
         ("mlp", MLPClassifier(alpha=0.001)),
         ("tree", DecisionTreeClassifier()),

Original file line number	Diff line number	Diff line change
`@@ -379,7 +379,7 @@ def test_no_refit():`
`379`	`379`	`and hasattr(grid_search, "best_params_")`
`380`	`380`	`)`
`381`	`381`
`382`		`- # Make sure the functions predict/transform etc raise meaningful`
	`382`	`+ # Make sure the functions predict/transform etc. raise meaningful`
`383`	`383`	`# error messages`
`384`	`384`	`for fn_name in (`
`385`	`385`	`"predict",`