apply linter

examples/plot_classifier.py

@@ -7,6 +7,7 @@
 """
 import numpy as np
 from matplotlib import pyplot as plt
+
 from skltemplate import TemplateClassifier
 
 X = [[0, 0], [1, 1]]
@@ -22,19 +23,23 @@
 X_1 = X_test[y_pred == 1]
 
 
-p0 = plt.scatter(0, 0, c='red', s=100)
-p1 = plt.scatter(1, 1, c='blue', s=100)
+p0 = plt.scatter(0, 0, c="red", s=100)
+p1 = plt.scatter(1, 1, c="blue", s=100)
 
-ax0 = plt.scatter(X_0[:, 0], X_0[:, 1], c='crimson', s=50)
-ax1 = plt.scatter(X_1[:, 0], X_1[:, 1], c='deepskyblue', s=50)
+ax0 = plt.scatter(X_0[:, 0], X_0[:, 1], c="crimson", s=50)
+ax1 = plt.scatter(X_1[:, 0], X_1[:, 1], c="deepskyblue", s=50)
 
-leg = plt.legend([p0, p1, ax0, ax1],
-                 ['Point 0', 'Point 1', 'Class 0', 'Class 1'],
-                 loc='upper left', fancybox=True, scatterpoints=1)
+leg = plt.legend(
+    [p0, p1, ax0, ax1],
+    ["Point 0", "Point 1", "Class 0", "Class 1"],
+    loc="upper left",
+    fancybox=True,
+    scatterpoints=1,
+)
 leg.get_frame().set_alpha(0.5)
 
-plt.xlabel('Feature 1')
-plt.ylabel('Feature 2')
-plt.xlim([-.5, 1.5])
+plt.xlabel("Feature 1")
+plt.ylabel("Feature 2")
+plt.xlim([-0.5, 1.5])
 
 plt.show()

examples/plot_template.py

@@ -7,10 +7,11 @@
 """
 import numpy as np
 from matplotlib import pyplot as plt
+
 from skltemplate import TemplateEstimator
 
 X = np.arange(100).reshape(100, 1)
-y = np.zeros((100, ))
+y = np.zeros((100,))
 estimator = TemplateEstimator()
 estimator.fit(X, y)
 plt.plot(estimator.predict(X))

examples/plot_transformer.py

@@ -7,20 +7,21 @@
 """
 import numpy as np
 from matplotlib import pyplot as plt
+
 from skltemplate import TemplateTransformer
 
 X = np.arange(50, dtype=np.float64).reshape(-1, 1)
 X /= 50
 estimator = TemplateTransformer()
 X_transformed = estimator.fit_transform(X)
 
-plt.plot(X.flatten(), label='Original Data')
-plt.plot(X_transformed.flatten(), label='Transformed Data')
-plt.title('Plots of original and transformed data')
+plt.plot(X.flatten(), label="Original Data")
+plt.plot(X_transformed.flatten(), label="Transformed Data")
+plt.title("Plots of original and transformed data")
 
-plt.legend(loc='best')
+plt.legend(loc="best")
 plt.grid(True)
-plt.xlabel('Index')
-plt.ylabel('Value of Data')
+plt.xlabel("Index")
+plt.ylabel("Value of Data")
 
 plt.show()

skltemplate/__init__.py

@@ -1,8 +1,9 @@
-from ._template import TemplateEstimator
-from ._template import TemplateClassifier
-from ._template import TemplateTransformer
-
+from ._template import TemplateClassifier, TemplateEstimator, TemplateTransformer
 from ._version import __version__
 
-__all__ = ['TemplateEstimator', 'TemplateClassifier', 'TemplateTransformer',
-           '__version__']
+__all__ = [
+    "TemplateEstimator",
+    "TemplateClassifier",
+    "TemplateTransformer",
+    "__version__",
+]

skltemplate/_template.py

@@ -3,13 +3,13 @@
 """
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin
-from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
-from sklearn.utils.multiclass import unique_labels
 from sklearn.metrics import euclidean_distances
+from sklearn.utils.multiclass import unique_labels
+from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
 
 
 class TemplateEstimator(BaseEstimator):
-    """ A template estimator to be used as a reference implementation.
+    """A template estimator to be used as a reference implementation.
 
     For more information regarding how to build your own estimator, read more
     in the :ref:`User Guide <user_guide>`.
@@ -29,7 +29,8 @@ class TemplateEstimator(BaseEstimator):
     >>> estimator.fit(X, y)
     TemplateEstimator()
     """
-    def __init__(self, demo_param='demo_param'):
+
+    def __init__(self, demo_param="demo_param"):
         self.demo_param = demo_param
 
     def fit(self, X, y):
@@ -54,7 +55,7 @@ def fit(self, X, y):
         return self
 
     def predict(self, X):
-        """ A reference implementation of a predicting function.
+        """A reference implementation of a predicting function.
 
         Parameters
         ----------
@@ -67,12 +68,12 @@ def predict(self, X):
             Returns an array of ones.
         """
         X = check_array(X, accept_sparse=True)
-        check_is_fitted(self, 'is_fitted_')
+        check_is_fitted(self, "is_fitted_")
         return np.ones(X.shape[0], dtype=np.int64)
 
 
 class TemplateClassifier(ClassifierMixin, BaseEstimator):
-    """ An example classifier which implements a 1-NN algorithm.
+    """An example classifier which implements a 1-NN algorithm.
 
     For more information regarding how to build your own classifier, read more
     in the :ref:`User Guide <user_guide>`.
@@ -91,7 +92,8 @@ class TemplateClassifier(ClassifierMixin, BaseEstimator):
     classes_ : ndarray, shape (n_classes,)
         The classes seen at :meth:`fit`.
     """
-    def __init__(self, demo_param='demo'):
+
+    def __init__(self, demo_param="demo"):
         self.demo_param = demo_param
 
     def fit(self, X, y):
@@ -120,7 +122,7 @@ def fit(self, X, y):
         return self
 
     def predict(self, X):
-        """ A reference implementation of a prediction for a classifier.
+        """A reference implementation of a prediction for a classifier.
 
         Parameters
         ----------
@@ -134,7 +136,7 @@ def predict(self, X):
             seen during fit.
         """
         # Check is fit had been called
-        check_is_fitted(self, ['X_', 'y_'])
+        check_is_fitted(self, ["X_", "y_"])
 
         # Input validation
         X = check_array(X)
@@ -144,7 +146,7 @@ def predict(self, X):
 
 
 class TemplateTransformer(TransformerMixin, BaseEstimator):
-    """ An example transformer that returns the element-wise square root.
+    """An example transformer that returns the element-wise square root.
 
     For more information regarding how to build your own transformer, read more
     in the :ref:`User Guide <user_guide>`.
@@ -159,7 +161,8 @@ class TemplateTransformer(TransformerMixin, BaseEstimator):
     n_features_ : int
         The number of features of the data passed to :meth:`fit`.
     """
-    def __init__(self, demo_param='demo'):
+
+    def __init__(self, demo_param="demo"):
         self.demo_param = demo_param
 
     def fit(self, X, y=None):
@@ -186,7 +189,7 @@ def fit(self, X, y=None):
         return self
 
     def transform(self, X):
-        """ A reference implementation of a transform function.
+        """A reference implementation of a transform function.
 
         Parameters
         ----------
@@ -200,14 +203,13 @@ def transform(self, X):
             in ``X``.
         """
         # Check is fit had been called
-        check_is_fitted(self, 'n_features_')
+        check_is_fitted(self, "n_features_")
 
         # Input validation
         X = check_array(X, accept_sparse=True)
 
         # Check that the input is of the same shape as the one passed
         # during fit.
         if X.shape[1] != self.n_features_:
-            raise ValueError('Shape of input is different from what was seen'
-                             'in `fit`')
+            raise ValueError("Shape of input is different from what was seenin `fit`")
         return np.sqrt(X)

skltemplate/_version.py

@@ -1 +1 @@
-__version__ = "0.0.4.dev0"
+__version__ = "0.0.4.dev0"

skltemplate/tests/test_common.py

@@ -1,15 +1,11 @@
 import pytest
-
 from sklearn.utils.estimator_checks import check_estimator
 
-from skltemplate import TemplateEstimator
-from skltemplate import TemplateClassifier
-from skltemplate import TemplateTransformer
+from skltemplate import TemplateClassifier, TemplateEstimator, TemplateTransformer
 
 
 @pytest.mark.parametrize(
-    "estimator",
-    [TemplateEstimator(), TemplateTransformer(), TemplateClassifier()]
+    "estimator", [TemplateEstimator(), TemplateTransformer(), TemplateClassifier()]
 )
 def test_all_estimators(estimator):
     return check_estimator(estimator)

skltemplate/tests/test_template.py

@@ -1,25 +1,22 @@
-import pytest
 import numpy as np
-
+import pytest
+from numpy.testing import assert_allclose, assert_array_equal
 from sklearn.datasets import load_iris
-from numpy.testing import assert_array_equal
-from numpy.testing import assert_allclose
 
-from skltemplate import TemplateEstimator
-from skltemplate import TemplateTransformer
-from skltemplate import TemplateClassifier
+from skltemplate import TemplateClassifier, TemplateEstimator, TemplateTransformer
 
 
 @pytest.fixture
 def data():
     return load_iris(return_X_y=True)
 
+
 def test_template_estimator(data):
     est = TemplateEstimator()
-    assert est.demo_param == 'demo_param'
+    assert est.demo_param == "demo_param"
 
     est.fit(*data)
-    assert hasattr(est, 'is_fitted_')
+    assert hasattr(est, "is_fitted_")
 
     X = data[0]
     y_pred = est.predict(X)
@@ -38,7 +35,7 @@ def test_template_transformer_error(data):
 def test_template_transformer(data):
     X, y = data
     trans = TemplateTransformer()
-    assert trans.demo_param == 'demo'
+    assert trans.demo_param == "demo"
 
     trans.fit(X)
     assert trans.n_features_ == X.shape[1]
@@ -53,12 +50,12 @@ def test_template_transformer(data):
 def test_template_classifier(data):
     X, y = data
     clf = TemplateClassifier()
-    assert clf.demo_param == 'demo'
+    assert clf.demo_param == "demo"
 
     clf.fit(X, y)
-    assert hasattr(clf, 'classes_')
-    assert hasattr(clf, 'X_')
-    assert hasattr(clf, 'y_')
+    assert hasattr(clf, "classes_")
+    assert hasattr(clf, "X_")
+    assert hasattr(clf, "y_")
 
     y_pred = clf.predict(X)
     assert y_pred.shape == (X.shape[0],)