In [1]:

    

import pandas as pd
from sklearn import datasets, model_selection, svm, metrics


    

In [2]:

    

# http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html
# http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html
iris = datasets.load_iris()


    

In [3]:

    

print(type(iris))
print(iris.keys())


    

    




<class 'sklearn.datasets.base.Bunch'>
dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])

In [4]:

    

iris_data = pd.DataFrame(data=iris.data, columns=iris.feature_names)
print(iris_data.head())


    

    




   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2

In [5]:

    

iris_label = pd.Series(data=iris.target)
print(iris_label.head())


    

    




0    0
1    0
2    0
3    0
4    0
dtype: int64

In [6]:

    

print(len(iris_data))


    

    




150

In [7]:

    

# http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
data_train, data_test, label_train, label_test = model_selection.train_test_split(iris_data, iris_label)


    

In [8]:

    

print(data_train.head())


    

    




     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
55                 5.7               2.8                4.5               1.3
128                6.4               2.8                5.6               2.1
142                5.8               2.7                5.1               1.9
97                 6.2               2.9                4.3               1.3
130                7.4               2.8                6.1               1.9

In [9]:

    

print(label_train.head())


    

    




55     1
128    2
142    2
97     1
130    2
dtype: int64

In [10]:

    

# default value of test_size = 0.25
print(len(data_train), len(data_test))


    

    




112 38

In [11]:

    

clf = svm.SVC()
clf.fit(data_train, label_train)
pre = clf.predict(data_test)


    

In [12]:

    

print(type(pre))
print(pre)


    

    




<class 'numpy.ndarray'>
[0 0 1 1 0 0 0 0 0 2 1 0 0 0 2 1 1 2 2 2 2 1 0 1 1 1 2 0 2 2 0 2 0 2 0 1 0
 0]

In [13]:

    

# http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
ac_score = metrics.accuracy_score(label_test, pre)


    

In [14]:

    

print(ac_score)


    

    




0.973684210526

In [15]:

    

scores = model_selection.cross_val_score(clf, iris_data, iris_label, cv=3)
print(scores)
print(scores.mean())


    

    




[ 0.98039216  0.96078431  0.97916667]
0.973447712418