style

_doc/examples/plot_kmeans_l1.py

@@ -14,7 +14,6 @@
 from sklearn.cluster import KMeans
 from mlinsights.mlmodel import KMeansL1L2
 
-
 ######################################################################
 # Simple datasets
 # ---------------

_doc/examples/plot_piecewise_linear_regression.py

@@ -26,7 +26,6 @@
 from sklearn.dummy import DummyRegressor
 from mlinsights.mlmodel import PiecewiseRegressor
 
-
 X = npr.normal(size=(1000, 4))
 alpha = [4, -2]
 t = (X[:, 0] + X[:, 3] * 0.5) > 0

_doc/examples/plot_predictable_tsne.py

@@ -28,7 +28,6 @@
 from sklearn.preprocessing import StandardScaler
 from mlinsights.mlmodel import PredictableTSNE
 
-
 digits = datasets.load_digits(n_class=6)
 Xd = digits.data
 yd = digits.target

_doc/examples/plot_quantile_mlpregression.py

@@ -17,7 +17,6 @@
 from sklearn.neural_network import MLPRegressor
 from mlinsights.mlmodel import QuantileMLPRegressor
 
-
 X = numpy.random.random(1000)
 eps1 = (numpy.random.random(900) - 0.5) * 0.1
 eps2 = (numpy.random.random(100)) * 10

_doc/examples/plot_regression_confidence_interval.py

@@ -35,7 +35,6 @@
 from sklearn.tree import DecisionTreeRegressor
 from mlinsights.mlmodel import IntervalRegressor, QuantileLinearRegression
 
-
 N = 200
 X = rand(N, 1) * 2
 eps = randn(N, 1) * 0.2

_doc/examples/plot_search_images_torch.py

@@ -26,7 +26,6 @@
 from mlinsights.plotting import plot_gallery_images
 from torchvision.models.squeezenet import SqueezeNet1_0_Weights
 
-
 model = models.squeezenet1_0(weights=SqueezeNet1_0_Weights.IMAGENET1K_V1)
 model
 

_doc/examples/plot_sklearn_transformed_target.py

@@ -31,7 +31,6 @@
 from mlinsights.mlmodel import TransformedTargetRegressor2
 from mlinsights.mlmodel import TransformedTargetClassifier2
 
-
 rnd = random((1000, 1))
 rndn = randn(1000)
 X = rnd[:, :1] * 10

_doc/examples/plot_traceable_ngrams_tfidf.py

@@ -22,7 +22,6 @@
     TraceableTfidfVectorizer,
 )
 
-
 corpus = numpy.array(
     [
         "This is the first document.",

_doc/examples/plot_visualize_pipeline.py

@@ -34,7 +34,6 @@
 )
 from mlinsights.plotting import pipeline2dot, pipeline2str
 
-
 iris = datasets.load_iris()
 X = iris.data[:, :4]
 df = pandas.DataFrame(X)

_unittests/ut_plotting/test_dot.py

@@ -122,8 +122,7 @@ def test_union_features(self):
         self.assertIn("MinMaxScaler", dot)
 
     def test_onehotencoder_dot(self):
-        data = dedent(
-            """
+        data = dedent("""
             date,value,notrend,trend,weekday,lag1,lag2,lag3,lag4,lag5,lag6,lag7,lag8
             2017-07-10 13:27:04.669830,0.003463591425601385,0.0004596547917981044,0.0030039366338032807,
             ###0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
@@ -151,8 +150,7 @@ def test_onehotencoder_dot(self):
             2017-07-21 13:27:04.669830,0.005866058541412791,0.00217339675927127,0.0036926617821415207,4,0.004773874566436903,
             ###0.004200435956007872,0.0038464710972236286,0.0035533180858140765,0.008716378909294038,0.006336617719481035,
             ###0.006078151848127084,0.004277700876279705
-            """
-        ).replace("\n###", "")
+            """).replace("\n###", "")
         df = pandas.read_csv(StringIO(data))
         cols = ["lag1", "lag2", "lag3", "lag4", "lag5", "lag6", "lag7", "lag8"]
         model = make_pipeline(
@@ -180,9 +178,7 @@ def test_pipeline_tr_small(self):
             7.8,0.76,0.04,2.3,0.092,15.0,54.0,0.997,3.26,0.65,9.8,5,red
             11.2,0.28,0.56,1.9,0.075,17.0,60.0,0.998,3.16,0.58,9.8,6,white
             7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4,5,red
-            """.replace(
-            "            ", ""
-        )
+            """.replace("            ", "")
         X_train = pandas.read_csv(StringIO(buffer)).drop("quality", axis=1)
 
         pipe = Pipeline(
@@ -224,9 +220,7 @@ def test_pipeline_tr(self):
             7.8,0.76,0.04,2.3,0.092,15.0,54.0,0.997,3.26,0.65,9.8,5,red
             11.2,0.28,0.56,1.9,0.075,17.0,60.0,0.998,3.16,0.58,9.8,6,white
             7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4,5,red
-            """.replace(
-            "            ", ""
-        )
+            """.replace("            ", "")
         X_train = pandas.read_csv(StringIO(buffer)).drop("quality", axis=1)
 
         numeric_features = [c for c in X_train if c != "color"]