In [1]:

    

from sklearn import tree
from sklearn.datasets import load_iris


    

In [2]:

    

iris = load_iris()


    

In [3]:

    

type(iris)


    

    
Out[3]:





sklearn.datasets.base.Bunch

In [4]:

    

isinstance(iris, dict)


    

    
Out[4]:





True

In [5]:

    

iris.keys()


    

    
Out[5]:





dict_keys(['target_names', 'target', 'DESCR', 'feature_names', 'data'])

In [6]:

    

iris.feature_names


    

    
Out[6]:





['sepal length (cm)',
 'sepal width (cm)',
 'petal length (cm)',
 'petal width (cm)']

In [7]:

    

iris.target_names


    

    
Out[7]:





array(['setosa', 'versicolor', 'virginica'], 
      dtype='<U10')

In [8]:

    

iris['target']


    

    
Out[8]:





array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

In [9]:

    

type(iris['target'])


    

    
Out[9]:





numpy.ndarray

In [10]:

    

for i in range(len(iris.target)):
    if i < 5:
        print('Example {}: label {}, features {}'.format(i, iris.target[i], iris.data[i]))


    

    




Example 0: label 0, features [ 5.1  3.5  1.4  0.2]
Example 1: label 0, features [ 4.9  3.   1.4  0.2]
Example 2: label 0, features [ 4.7  3.2  1.3  0.2]
Example 3: label 0, features [ 4.6  3.1  1.5  0.2]
Example 4: label 0, features [ 5.   3.6  1.4  0.2]

In [11]:

    

import numpy as np


    

In [12]:

    

test_idx = [0, 50, 100]  # these are the rows to be removed from the training data

# remove the same rows from the actual data
# Note: without axis=0, returns just a  list, not a list of lists
# ie we want this:
# [[ 4.9,  3. ,  1.4,  0.2],
#       [ 4.7,  3.2,  1.3,  0.2],
#       [ 4.6,  3.1,  1.5,  0.2], …]
# and not this:
# [4.9,  3. ,  1.4,  0.2,  4.7,  3.2,  1.3,  0.2, 4.6,  3.1,  1.5,  0.2, …]
train_data = np.delete(iris.data, test_idx, axis=0)

# np.delete() remove the above 3 indices from array iris.target
# Note: here the axis= arg doesn't matter, as only a 1 interger per item in list
train_target = np.delete(iris.target, test_idx)


    

In [13]:

    

# See how rows have been rm'd
len(iris.target)


    

    
Out[13]:





150

In [14]:

    

len(train_data)  # the three taken out


    

    
Out[14]:





147

In [15]:

    

len(train_target)


    

    
Out[15]:





147

In [16]:

    

test_target = iris.target[test_idx]


    

In [17]:

    

test_target  # only three


    

    
Out[17]:





array([0, 1, 2])

In [18]:

    

test_data = iris.data[test_idx]


    

In [19]:

    

test_data


    

    
Out[19]:





array([[ 5.1,  3.5,  1.4,  0.2],
       [ 7. ,  3.2,  4.7,  1.4],
       [ 6.3,  3.3,  6. ,  2.5]])

In [20]:

    

# Note: on numpy array
l = [1, 4, 5,6,8, 999, 44, 6, 7, 10]
a = np.array(l)

# now we can pull out items with a list of indices/rows
idx = [0, 4, 6]
a[idx]


    

    
Out[20]:





array([ 1,  8, 44])

In [21]:

    

# train model
clf = tree.DecisionTreeClassifier()
clf.fit(train_data, train_target)


    

    
Out[21]:





DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')

In [22]:

    

# make prediction
clf.predict(test_data)


    

    
Out[22]:





array([0, 1, 2])

In [23]:

    

# matches input labels?
clf.predict(test_data) == test_target


    

    
Out[23]:





array([ True,  True,  True], dtype=bool)

In [25]:

    

from sklearn.externals.six import StringIO
import pydotplus # note installed pydotplus for Py3 compatibility


    

In [26]:

    

dot_data = StringIO()

tree.export_graphviz(clf, 
                     out_file=dot_data, 
                     feature_names=iris.feature_names, 
                     class_names=iris.target_names, 
                     filled=True, 
                     rounded=True, 
                     impurity=False)


    

In [27]:

    

graph = pydotplus.graph_from_dot_data(dot_data.getvalue())


    

In [29]:

    

# graphviz installed on mac with `brew install graphviz`
graph.write_pdf('iris.pdf')

# open -a preview ~/ipython/tensorflow/iris.pdf


    

    
Out[29]:





True

In [32]:

    

# now check the rows withheld for testing
# check against rules in graphic tree
test_data[0], test_target[0]  # we know is a setosa


    

    
Out[32]:





(array([ 5.1,  3.5,  1.4,  0.2]), 0)

In [34]:

    

iris.feature_names, iris.target_names


    

    
Out[34]:





(['sepal length (cm)',
  'sepal width (cm)',
  'petal length (cm)',
  'petal width (cm)'],
 array(['setosa', 'versicolor', 'virginica'], 
       dtype='<U10'))

In [36]:

    

test_data[1], test_target[1]  # we know is a versicolor


    

    
Out[36]:





(array([ 7. ,  3.2,  4.7,  1.4]), 1)

In [38]:

    

test_data[2], test_target[2]  # we know is a virginica

# all test to true!


    

    
Out[38]:





(array([ 6.3,  3.3,  6. ,  2.5]), 2)