black formatting

oegedijk · Feb 18, 2023 · 6f59760 · 6f59760
1 parent f703995
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diff --git a/explainerdashboard/cli.py b/explainerdashboard/cli.py
diff --git a/explainerdashboard/dashboard_components/classifier_components.py b/explainerdashboard/dashboard_components/classifier_components.py
diff --git a/explainerdashboard/dashboard_components/composites.py b/explainerdashboard/dashboard_components/composites.py
diff --git a/explainerdashboard/dashboard_components/connectors.py b/explainerdashboard/dashboard_components/connectors.py
diff --git a/explainerdashboard/dashboard_components/decisiontree_components.py b/explainerdashboard/dashboard_components/decisiontree_components.py
diff --git a/explainerdashboard/dashboard_components/overview_components.py b/explainerdashboard/dashboard_components/overview_components.py
diff --git a/explainerdashboard/dashboard_components/regression_components.py b/explainerdashboard/dashboard_components/regression_components.py
diff --git a/explainerdashboard/dashboard_components/shap_components.py b/explainerdashboard/dashboard_components/shap_components.py
diff --git a/explainerdashboard/dashboard_methods.py b/explainerdashboard/dashboard_methods.py
diff --git a/explainerdashboard/datasets.py b/explainerdashboard/datasets.py
@@ -1,17 +1,19 @@
-__all__ = ['titanic_survive', 
-            'titanic_fare', 
-            'titanic_embarked', 
-            'titanic_names',
-            'feature_descriptions',
-            'train_csv',
-            'test_csv']
+__all__ = [
+    "titanic_survive",
+    "titanic_fare",
+    "titanic_embarked",
+    "titanic_names",
+    "feature_descriptions",
+    "train_csv",
+    "test_csv",
+]
 
 import numpy as np
 import pandas as pd
 from pathlib import Path
 
-train_csv = Path(__file__).resolve().parent / 'datasets'/ 'titanic_train.csv'
-test_csv = Path(__file__).resolve().parent / 'datasets'/'titanic_test.csv'
+train_csv = Path(__file__).resolve().parent / "datasets" / "titanic_train.csv"
+test_csv = Path(__file__).resolve().parent / "datasets" / "titanic_test.csv"
 d_train = pd.read_csv(train_csv)
 d_test = pd.read_csv(test_csv)
 
@@ -20,65 +22,96 @@
     "Gender": "Gender of passenger",
     "Deck": "The deck the passenger had their cabin on",
     "PassengerClass": "The class of the ticket: 1st, 2nd or 3rd class",
-    "Fare": "The amount of money people paid for their ticket", 
+    "Fare": "The amount of money people paid for their ticket",
     "Embarked": "the port where the passenger boarded the Titanic. Either Southampton, Cherbourg or Queenstown",
     "Age": "Age of the passenger",
     "No_of_siblings_plus_spouses_on_board": "The sum of the number of siblings plus the number of spouses on board",
-    "No_of_parents_plus_children_on_board" : "The sum of the number of parents plus the number of children on board",
+    "No_of_parents_plus_children_on_board": "The sum of the number of parents plus the number of children on board",
 }
 
+
 def titanic_survive():
-    X_train = d_train.drop(['Survival', 'Name'], axis=1)
+    X_train = d_train.drop(["Survival", "Name"], axis=1)
     X_train.index = d_train.Name
     X_train.index.name = "Passenger"
-    y_train = d_train['Survival']
-    X_test = d_test.drop(['Survival', 'Name'], axis=1)
+    y_train = d_train["Survival"]
+    X_test = d_test.drop(["Survival", "Name"], axis=1)
     X_test.index = d_test.Name
     X_test.index.name = "Passenger"
-    y_test = d_test['Survival']
+    y_test = d_test["Survival"]
     return X_train, y_train, X_test, y_test
 
 
 def titanic_fare():
-    X_train = d_train.drop(['Fare', 'Name'], axis=1)
+    X_train = d_train.drop(["Fare", "Name"], axis=1)
     X_train.index = d_train.Name
     X_train.index.name = "Passenger"
-    y_train = d_train['Fare']
-    X_test = d_test.drop(['Fare', 'Name'], axis=1)
+    y_train = d_train["Fare"]
+    X_test = d_test.drop(["Fare", "Name"], axis=1)
     X_test.index = d_test.Name
     X_test.index.name = "Passenger"
-    y_test = d_test['Fare']
+    y_test = d_test["Fare"]
     return X_train, y_train, X_test, y_test
 
 
 def titanic_embarked():
     d_train2 = d_train.copy()
-    d_train2 = d_train2[d_train2.Embarked_Unknown==0]
-    X_train = d_train2.drop(['Embarked_Cherbourg', 'Embarked_Queenstown', 
-        'Embarked_Southampton', 'Embarked_Unknown', 'Name'], axis=1)
+    d_train2 = d_train2[d_train2.Embarked_Unknown == 0]
+    X_train = d_train2.drop(
+        [
+            "Embarked_Cherbourg",
+            "Embarked_Queenstown",
+            "Embarked_Southampton",
+            "Embarked_Unknown",
+            "Name",
+        ],
+        axis=1,
+    )
     X_train.index = d_train2.Name
     X_train.index.name = "Passenger"
 
-    y_train = pd.Series(np.where(d_train2.Embarked_Queenstown==1, 0, 
-                            np.where(d_train2.Embarked_Southampton==1, 1, 
-                                np.where(d_train2.Embarked_Cherbourg==1, 2, 3))), 
-
-                    name="Embarked")
-    X_test = d_test.drop(['Embarked_Cherbourg', 'Embarked_Queenstown', 
-        'Embarked_Southampton', 'Embarked_Unknown', 'Name'], axis=1)
+    y_train = pd.Series(
+        np.where(
+            d_train2.Embarked_Queenstown == 1,
+            0,
+            np.where(
+                d_train2.Embarked_Southampton == 1,
+                1,
+                np.where(d_train2.Embarked_Cherbourg == 1, 2, 3),
+            ),
+        ),
+        name="Embarked",
+    )
+    X_test = d_test.drop(
+        [
+            "Embarked_Cherbourg",
+            "Embarked_Queenstown",
+            "Embarked_Southampton",
+            "Embarked_Unknown",
+            "Name",
+        ],
+        axis=1,
+    )
     X_test.index = d_test.Name
     X_test.index.name = "Passenger"
-    y_test = pd.Series(np.where(d_test.Embarked_Queenstown==1, 0, 
-                            np.where(d_test.Embarked_Southampton==1, 1, 
-                                np.where(d_test.Embarked_Cherbourg==1, 2, 3))), 
-
-                    name="Embarked")
+    y_test = pd.Series(
+        np.where(
+            d_test.Embarked_Queenstown == 1,
+            0,
+            np.where(
+                d_test.Embarked_Southampton == 1,
+                1,
+                np.where(d_test.Embarked_Cherbourg == 1, 2, 3),
+            ),
+        ),
+        name="Embarked",
+    )
     return X_train, y_train, X_test, y_test
 
 
 def titanic_names(train_only=False, test_only=False):
     if train_only:
-        return d_train['Name'].values.tolist()
+        return d_train["Name"].values.tolist()
     if test_only:
-        return d_test['Name'].values.tolist()
-    return (d_train['Name'].values.tolist(), d_test['Name'].values.tolist())
+        return d_test["Name"].values.tolist()
+    return (d_train["Name"].values.tolist(), d_test["Name"].values.tolist())