ENH Match the book exactly

Including import order and comments

Author: luispedro

ch02/figure1.py

@@ -5,9 +5,12 @@
 #
 # It is made available under the MIT License
 
-fromsklearn.datasetsimportload_iris
 frommatplotlibimportpyplotasplt
 
+# We load the data with load_iris from sklearn
+fromsklearn.datasetsimportload_iris
+
+# load_iris returns an object with several fields
 data=load_iris()
 features=data.data
 feature_names=data.feature_names

ch02/figure2.py

@@ -10,14 +10,18 @@
 frommatplotlibimportpyplotasplt
 fromsklearn.datasetsimportload_iris
 data=load_iris()
-features=data['data']
-feature_names=data['feature_names']
-species=data['target_names'][data['target']]
+features=data.data
+feature_names=data.feature_names
+target=data.target
+target_names=data.target_names
 
-is_setosa= (species=='setosa')
+# We use NumPy fancy indexing to get an array of strings:
+labels=target_names[target]
+
+is_setosa= (labels=='setosa')
 features=features[~is_setosa]
-species=species[~is_setosa]
-is_virginica= (species=='virginica')
+labels=labels[~is_setosa]
+is_virginica= (labels=='virginica')
 
 # Hand fixed threshold:
 t=1.75

ch02/simple_threshold.py

@@ -12,9 +12,12 @@
 target=data['target']
 target_names=data['target_names']
 labels=target_names[target]
-
 plength=features[:, 2]
+
+# To use numpy operations to get setosa features,
+# we build a boolean array
 is_setosa= (labels=='setosa')
+
 max_setosa=plength[is_setosa].max()
 min_non_setosa=plength[~is_setosa].min()