take into account raghav comments

Author: ngoix

benchmarks/bench_isolation_forest.py

@@ -20,7 +20,6 @@
 np.random.seed(1)
 
 datasets = ['http', 'smtp', 'SA', 'SF', 'shuttle', 'forestcover']
-# datasets = ['http']
 
 for dat in datasets:
     # loading and vectorization
@@ -34,7 +33,7 @@
         dataset = fetch_mldata('shuttle')
         X = dataset.data
         y = dataset.target
-        sh(X, y)
+        X, y = sh(X, y)
         # we remove data with label 4
         # normal data are then those of class 1
         s = (y != 4)
@@ -95,7 +94,7 @@
     scoring = - model.decision_function(X_test)  # the lower, the more normal
 
     # Show score histograms
-    f, ax = plt.subplots(3, sharex=True, sharey=True)
+    fig, ax = plt.subplots(3, sharex=True, sharey=True)
     bins = np.linspace(-0.5, 0.5, 200)
     ax[0].hist(scoring, bins, color='black')
     ax[0].set_title('decision function for %s dataset' % dat)

sklearn/ensemble/iforest.py

@@ -193,7 +193,7 @@ def fit(self, X, y=None, sample_weight=None):
         return self
 
     def predict(self, X):
-        """Outlyingness of observations in X according to the fitted model.
+        """Predict if a particular sample is an outlier or not.
 
         Parameters
         ----------
@@ -206,7 +206,7 @@ def predict(self, X):
         -------
         is_inlier : array, shape = (n_samples, )
             For each observations, tells whether or not (+1 or -1) it should
-            be considered as an outlier according to the fitted model.
+            be considered as an inlier according to the fitted model.
 
         """
         X = check_array(X, accept_sparse='csr')