[DATALAD RUNCMD] Reformat code with black

=== Do not change lines below ===
{
 "chain": [],
 "cmd": "black code/classification_analysis.py",
 "dsid": "4fecd570-70e0-4746-b0c9-273424fc70da",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^

code/classification_analysis.py

@@ -6,31 +6,31 @@ from sklearn.neighbors import KNeighborsClassifier
 from sklearn.metrics import classification_report
 
 parser = argparse.ArgumentParser(description="Analyze iris data")
-parser.add_argument('data', help="Input data (CSV) to process")
-parser.add_argument('output_figure', help="Output figure path")
-parser.add_argument('output_report', help="Output report path")
+parser.add_argument("data", help="Input data (CSV) to process")
+parser.add_argument("output_figure", help="Output figure path")
+parser.add_argument("output_report", help="Output report path")
 args = parser.parse_args()
 
 # prepare the data as a pandas dataframe
 df = pd.read_csv(args.data)
-attributes = ["sepal_length", "sepal_width", "petal_length","petal_width", "class"]
+attributes = ["sepal_length", "sepal_width", "petal_length", "petal_width", "class"]
 df.columns = attributes
 
 # create a pairplot to plot pairwise relationships in the dataset
-plot = sns.pairplot(df, hue='class', palette='muted')
+plot = sns.pairplot(df, hue="class", palette="muted")
 plot.savefig(args.output_figure)
 
 # perform a K-nearest-neighbours classification with scikit-learn
 
 # Step 1: split data in test and training dataset (20:80)
 array = df.values
-X = array[:,0:4]
-Y = array[:,4]
+X = array[:, 0:4]
+Y = array[:, 4]
 test_size = 0.20
 seed = 7
-X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y,
-                                                                    test_size=test_size,
-                                                                    random_state=seed)
+X_train, X_test, Y_train, Y_test = model_selection.train_test_split(
+    X, Y, test_size=test_size, random_state=seed
+)
 
 # Step 2: Fit the model and make predictions on the test dataset
 knn = KNeighborsClassifier()