机器学习——对样本分类

本次学习从机器学习的典例数据：鸢尾花数据集出发。
Iris dataset可以从sklearn直接引入，也可以从http://archive.ics.uci.edu/ml/datasets/Iris获取。

实验环境

• python3环境
• 使用到的库

from sklearn import datasets
from matplotlib import pyplot as plt
import numpy as np

数据的可视化

1. 从sklearn引入数据集

iris = datasets.load_iris()
features = iris['data']
feature_names = iris['feature_names']
target = iris['target']

2. 画出每个类别的数据集，代码中以sepal length 和sepal width为例

for t, marker, c in zip(range(3), ">ox", "rgb"):
    #我们划出每个类别，各自采用不通颜色标识
    plt.scatter(features[target == t, 0],
                features[target == t, 1],
                marker=marker,
                c=c)
plt.xlabel('sepal length')
plt.ylabel('sepal width')
plt.show()

Figure_1.png

3. 画出每个类别的数据集

def scatter_plot(dim1,dim2):
    for t, marker, c in zip(range(3), ">ox", "rgb"):
        #我们划出每个类别，各自采用不通颜色标识
        plt.scatter(features[target == t, dim1],
                    features[target == t, dim2],
                    marker=marker,
                    c=c)
        dim_desc={0:'sepal length',1:'sepal width',2:'petal length',3:'petal width'}
    plt.xlabel(dim_desc.get(dim1))
    plt.ylabel(dim_desc.get(dim2))
    #plt.show()

#用subplot构建6个子图
count = 0
for j in range(3):
    for k in range(1, 4):
        if(k>j):
            plt.subplot(231+count)
            scatter_plot(j, k)
            count=count+1
plt.show()#注意plt.show()的位置

Figure_1.png

根据可视化的图形，显而易见，山鸢尾花可以由花瓣长度很明显的区分出来。

分类

1. 山鸢尾花区分开来

plength = features[:, 2]
#用numpy操作，来获取setosa的特征,花瓣长度，是一维矩阵

is_setosa = (target == 0) #布尔型一维矩阵
#print(is_setosa)
#print((is_setosa.shape))
#print(plength.shape)

#布尔型索引
setosa_plength = plength[is_setosa]
other_plength = plength[~is_setosa]


max_setosa = setosa_plength.max()
min_non_setosa = other_plength.min()

print('Maximum of setosa:{0}. '.format(max_setosa))
print('Minmum of others:{0}.'.format(min_non_setosa))

2. 筛出另外两个花种

# #筛出非setosa的花种
features = features[~is_setosa]
labels = target[~is_setosa]
# #rint(labels)
#
#
virginica = (labels == 2)
#print(virginica)
#
# #print(virginica)

print(features.shape)
best_acc = -1.0
for fi in range(features.shape[1]):
    thresh = features[:, fi].copy()
    #thresh.sort()
    #print(thresh)
    for t in thresh:
        #print('t is',t)
        pred = (features[:, fi] > t)
        #print(pred)
        #print(pred == virginica)
        acc = (pred == virginica).mean()
        #print('acc=', acc)
        if acc > best_acc:
            best_acc = acc
            best_fi = fi
            best_t = t

print('Best Accuracy:', best_acc)
print('Best Feature Index', fi)
print('Best Threshold', t)
#这里我们首先对每一维度进行排序，然后从该维度中取出任一值作为阈值的一个假设，再计算这个假设的Boolean序列和实际的标签Boolean序列的一致情况，求平均，即得到了准确率。经过所有的循环，最终得到的阈值和所对应的维度。
#最后，我们得到了最佳模型针对第四维花瓣的宽度petal width，我们就可以得到这个决策边界decision boundary。

这里得出的分界是以某个参数的界限值为标准的
得出准确率最高是0.96 但是这里我们的训练数据和测试数据并没有分开来。

3. 交叉验证
   去一法，从训练集中拿出一个样本，并在缺少这个样本的数据上训练一个模型，然后看模型能否对这个样本正确分类：

def learn_model(features,labels):
    best_acc = -1.0
    for fi in range(features.shape[1]):
        thresh = features[:, fi].copy()
        # thresh.sort()
        #print(thresh)
        for t in thresh:
            # print('t is',t)
            pred = (features[:, fi] > t)
            # print(pred)
            # print(pred == virginica)
            acc = (pred == labels).mean()
            # print('acc=', acc)
            if acc > best_acc:
                best_acc = acc
                best_fi = fi
                best_t = t

    print('Best Accuracy:', best_acc)
    print('Best Feature Index', fi)
    print('Best Threshold', t)

    return {'accuracy':best_acc, 'Feature Index':best_fi, 'Threshold':best_t}

def apply_model(features,labels,model):
    t = model['Threshold']
    pred = (features[:, fi] > t)
    acc = (pred == labels).mean()
    return  pred


error = 0.0
for ei in range(len(features)):
#选择除了ei以外的所有位置：
    training = np.ones(len(features), bool)
    training[ei] = False
    testing = ~training
    model = learn_model(features[training],virginica[training])
    predictions = apply_model(features[testing],virginica[testing],model)
    error += np.sum(predictions != virginica[testing])
print(error)