black code formatting

Author: bbengfort

docs/api/anscombe.py

@@ -1,4 +1,4 @@
-# Creates the anscombe visualization. 
+# Creates the anscombe visualization.
 
 import yellowbrick as yb
 import matplotlib.pyplot as plt

docs/api/features/manifold.py

@@ -81,7 +81,8 @@ def dataset_example(dataset="occupancy", manifold="all", path="images/", **kwarg
 
 
 def select_features_example(
-    algorithm="isomap", path="images/occupancy_select_k_best_isomap_manifold.png",
+    algorithm="isomap",
+    path="images/occupancy_select_k_best_isomap_manifold.png",
     **kwargs
 ):
     _, ax = plt.subplots(figsize=(9, 6))
@@ -133,9 +134,7 @@ def plot_manifold_embedding(self, algorithm="lle", path="images"):
         _, ax = plt.subplots(figsize=(9, 6))
         path = self._make_path(path, "s_curve_{}_manifold.png".format(algorithm))
 
-        oz = Manifold(
-            ax=ax, manifold=algorithm, colors="nipy_spectral"
-        )
+        oz = Manifold(ax=ax, manifold=algorithm, colors="nipy_spectral")
 
         oz.fit(self.X, self.y)
         oz.poof(outpath=path)
@@ -154,7 +153,6 @@ def plot_all_manifolds(self, path="images"):
     dataset_example("concrete", "tsne", path="images/concrete_tsne_manifold.png")
     dataset_example("occupancy", "tsne", path="images/occupancy_tsne_manifold.png")
     dataset_example(
-        "concrete", "isomap", path="images/concrete_isomap_manifold.png",
-        n_neighbors=10
+        "concrete", "isomap", path="images/concrete_isomap_manifold.png", n_neighbors=10
     )
     select_features_example(algorithm="isomap", n_neighbors=10)

docs/api/features/pcoords_benchmark.py

@@ -7,7 +7,6 @@
 
 
 def plot_speedup(trials=5, factors=np.arange(1, 11)):
-
     def pcoords_time(X, y, fast=True):
         _, ax = plt.subplots()
         oz = ParallelCoordinates(fast=fast, ax=ax)
@@ -16,9 +15,9 @@ def pcoords_time(X, y, fast=True):
         oz.fit_transform(X, y)
         delta = time.time() - start
 
-        plt.cla()        # clear current axis
-        plt.clf()        # clear current figure
-        plt.close("all") # close all existing plots
+        plt.cla()  # clear current axis
+        plt.clf()  # clear current figure
+        plt.close("all")  # close all existing plots
 
         return delta
 
@@ -49,19 +48,19 @@ def pcoords_speedup(X, y):
     variance = np.array(variance)
 
     series = pd.Series(speedups, index=factors)
-    _, ax = plt.subplots(figsize=(9,6))
-    series.plot(ax=ax, marker='o', label="speedup factor", color='b')
+    _, ax = plt.subplots(figsize=(9, 6))
+    series.plot(ax=ax, marker="o", label="speedup factor", color="b")
 
     # Plot one standard deviation above and below the mean
     ax.fill_between(
-        factors, speedups - variance, speedups + variance, alpha=0.25,
-        color='b',
+        factors, speedups - variance, speedups + variance, alpha=0.25, color="b"
     )
 
     ax.set_ylabel("speedup factor")
     ax.set_xlabel("dataset size (number of repeats in Iris dataset)")
     ax.set_title("Speed Improvement of Fast Parallel Coordinates")
     plt.savefig("images/fast_parallel_coordinates_speedup_benchmark.png")
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     plot_speedup()

docs/api/model_selection/validation_curve.py

@@ -26,16 +26,25 @@
 ## Helper Methods
 ##########################################################################
 
-def validation_curve_sklearn_example(path="images/validation_curve_sklearn_example.png"):
+
+def validation_curve_sklearn_example(
+    path="images/validation_curve_sklearn_example.png"
+):
     digits = load_digits()
     X, y = digits.data, digits.target
 
     _, ax = plt.subplots()
 
     param_range = np.logspace(-6, -1, 5)
     oz = ValidationCurve(
-        SVC(), ax=ax, param_name="gamma", param_range=param_range,
-        logx=True, cv=10, scoring="accuracy", n_jobs=4
+        SVC(),
+        ax=ax,
+        param_name="gamma",
+        param_range=param_range,
+        logx=True,
+        cv=10,
+        scoring="accuracy",
+        n_jobs=4,
     )
     oz.fit(X, y)
     oz.poof(outpath=path)
@@ -52,8 +61,14 @@ def validation_curve_classifier_svc(path="images/validation_curve_classifier_svc
     print("warning: generating the SVC validation curve can take a very long time!")
 
     oz = ValidationCurve(
-        SVC(), ax=ax, param_name="gamma", param_range=param_range,
-        logx=True, cv=cv, scoring="f1_weighted", n_jobs=8,
+        SVC(),
+        ax=ax,
+        param_name="gamma",
+        param_range=param_range,
+        logx=True,
+        cv=cv,
+        scoring="f1_weighted",
+        n_jobs=8,
     )
     oz.fit(X, y)
     oz.poof(outpath=path)
@@ -70,8 +85,13 @@ def validation_curve_classifier_knn(path="images/validation_curve_classifier_knn
     print("warning: generating the KNN validation curve can take a very long time!")
 
     oz = ValidationCurve(
-        KNeighborsClassifier(), ax=ax, param_name="n_neighbors",
-        param_range=param_range, cv=cv, scoring="f1_weighted", n_jobs=8,
+        KNeighborsClassifier(),
+        ax=ax,
+        param_name="n_neighbors",
+        param_range=param_range,
+        cv=cv,
+        scoring="f1_weighted",
+        n_jobs=8,
     )
     oz.fit(X, y)
     oz.poof(outpath=path)
@@ -81,7 +101,7 @@ def validation_curve_classifier_knn(path="images/validation_curve_classifier_knn
 ## Main Method
 ##########################################################################
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # validation_curve_sklearn_example()
     validation_curve_classifier_svc()
     validation_curve_classifier_knn()

docs/api/text/umap_vis.py

@@ -32,7 +32,7 @@ def umap(docs, target, outpath, **kwargs):
     visualizer.poof(outpath=outpath)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
     # Load and vectorize the corpus
     corpus = load_hobbies()