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示例来自《Phython机器学习基础教程》

(Introduction to Machine Learning with Python)

[德] Andreas C.Müller [美] Sarah Guido 著 张亮（hysic）译

书上示例代码

import pandas as pd
import mglearn 

然而在copy下来在pycharm里运行时发现了各种错误

01

首先是在进行

import mglearn

时出现的

future warning


\anaconda\lib\site-packages\sklearn\externals\six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).
  "(https://pypi.org/project/six/).", FutureWarning)
\anaconda\lib\site-packages\sklearn\externals\joblib\__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.
  warnings.warn(msg, category=FutureWarning)

反正大意是说“版本将会有变动你这个代码可能在新版本python下搞不好啦”之类的，忽视就行。

02

grr = pd.plotting.scatter_matrix(iris_dataFrame, c=y_train, figsize=(15, 15), marker=‘o’,

hist_kwds={‘bins’: 20}, s=60, alpha=.8, cmap=mglearn.cm3)

貌似2019年以后，pandas中的pd.scatter_matrix()调用不可行，变成了pd.plotting.scatter_matrix()来调用

03

如何显示？？

为啥我什么都搞好了就是没有图呢？

最后发现需要一个plt.show()

plt又是什么？

需要导入一个包

import matplotlib as plt


完整代码

# 文件名 test.py
# 导入包
import pandas as pd
import matplotlib.pyplot as plt
import mglearn
# 随机分割数据集、分为训练集和测试集的函数
from sklearn.model_selection import train_test_split
# sklearn自带的数据集
from sklearn.datasets import load_iris

# 载入数据集
iris_dataset = load_iris()

# 随机分割数据集【因为数据集原本是按照target顺序排列的】
'''
Target:
 [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]
'''

X_train, X_test, y_train, y_test = train_test_split(
    iris_dataset['data'],iris_dataset['target'], random_state=0
)

# 将numpy数组转换成pandas dataFrame类型
iris_dataFrame = pd.DataFrame(X_train, columns=iris_dataset.feature_names)
display(iris_dataFrame)

# 此处可以打印查看一下，记得要 `from IPython.display import display`
# display(iris_dataFrame)

# 调用函数 scatter_matrix，绘制散点图矩阵
grr = pd.plotting.scatter_matrix(iris_dataFrame, c=y_train, figsize=(15, 15), marker='o',
                        hist_kwds={'bins': 20}, s=60, alpha=.8, cmap=mglearn.cm3)
plt.show()


# KNN算法对未知分类的花分类
from sklearn.neighbors import KNeighborsClassifier
# 只考虑一位邻居 ——如果多位邻居，把参数n_neighors改掉就行
knn = KNeighborsClassifier(n_neighbors=1)

# 训练模型
knn.fit(X_train,y_train)

# 尝试预测新的种类

import numpy as np
X_new = np.array([[5, 2.9, 1, 0.2]])
print("X_new.shape: {}".format(X_new.shape))  
'''X_new.shape: (1, 4) '''	
# shape必须符合X_test
# 例如
# shape of data: (150, 4)
# 因此一个元组的shape为(1, 4)


# 尝试调用 knn 对象的 predict 方法来进行预测
prediction = knn.predict(X_new)
print("Prediction: \n{}".format(prediction))
print("Prediction target name :\n {}".format(iris_dataset['target_names'][prediction]))
'''
Prediction: 
[0]
Prediction target name :
 ['setosa']
 
 # 预测值为0 ，对应种类为setosa
 '''


# 评估模型
y_pre = knn.predict(X_test)
corr_rate = np.mean(y_pre == y_test)
print("Test set score : {:.2f}".format(corr_rate))

'''
Test set score : 0.97
'''