学习了决策树和逻辑回归的理论知识，决定亲自上手尝试一下。最终导出决策树的决策过程的图片和pdf。逻辑回归部分参考的是用逻辑回归实现鸢尾花数据集分类,感谢原作者xiaoyangerr

注意：要导出为pdf先必须安装graphviz(这是一个软件)并且安装pydotplus这个包,把它的graphviz加入系统的环境变量path，否则会报错

决策树

from sklearn.datasets import load_iris

from sklearn import tree

from sklearn.model_selection import train_test_split

# 加载数据集

iris = load_iris()

# 引入训练模型

clf = tree.DecisionTreeClassifier()

X = iris.data

y = iris.target

# 分割数据集

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=33)

# 开始训练

clf.fit(X_train,y_train)

# 预测

y_predict=clf.predict(X_test)

from sklearn.metrics import classification_report

#显示预测的准确性

# X : array-like, shape = (n_samples, n_features)

# Test samples.

# y : array-like, shape = (n_samples) or (n_samples, n_outputs)

# True labels for X.

print(clf.score(X_test,y_test))# 输出结果为0.9111111111111111

print(classification_report(y_predict,y_test))

# 输出结果为

'''

precision recall f1-score support

0 1.00 1.00 1.00 11

1 1.00 0.79 0.88 19

2 0.79 1.00 0.88 15

avg / total 0.93 0.91 0.91 45

'''

# 导出为pdf

import pydotplus

dot_data = tree.export_graphviz(clf,out_file=None)

graph = pydotplus.graph_from_dot_data(dot_data)

graph.write_pdf('iris.pdf')

#导出为图片

from IPython.display import Image

dot_data = tree.export_graphviz(clf, out_file=None,

feature_names=iris.feature_names, class_names=iris.target_names,filled=True, rounded=True,special_characters=True)

决策过程

决策过程.png

逻辑回归

函数图像

# 图象

x = np.linspace(-10,10,1000)

y = 1/(1+np.exp(-x))

sns.set()

plt.axhline(0.5,color='r',ls='dotted')

plt.axvline(0,color='r',ls = 'dotted')

plt.yticks([0.0,5,1.0])

plt.title(r'Sigmoid', fontsize = 15)

plt.text(5,0.8,r'$y = \frac{1}{1+e^{-z}}$', fontsize = 18)

plt.plot(x,y)

image.png

数据分析

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import plotly.plotly as py

import plotly.graph_objs as go

from sklearn.decomposition import PCA

from plotly.offline import init_notebook_mode, iplot

init_notebook_mode(connected = True)

data = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',header=None)

data.columns = ['Sepal.Length','Sepal.Width','Petal.Length','Petal.Width','Species']

data.head()

'''

输出为：

Sepal.Length Sepal.Width Petal.Length Petal.Width Species

0 5.1 3.5 1.4 0.2 Iris-setosa

1 4.9 3.0 1.4 0.2 Iris-setosa

2 4.7 3.2 1.3 0.2 Iris-setosa

3 4.6 3.1 1.5 0.2 Iris-setosa

4 5.0 3.6 1.4 0.2 Iris-setosa

'''

labels = data.groupby('Species').size().index

values = data.groupby('Species').size()

trace = go.Pie(labels=labels, values=values)

layout = go.Layout(width=500, height=500)

fig = go.Figure(data=[trace], layout=layout)

iplot(fig)

输出为：

image.png

groups= data.groupby(by="Species")

means,sds = groups.mean(),groups.std()

means.plot(yerr=sds,kind='bar',figsize=(9,5),table=True)

plt.show()

输出为：

image.png

col_map = {'Iris-setosa': 'orange', 'Iris-versicolor': 'green', 'Iris-virginica': 'pink'}

pd.tools.plotting.scatter_matrix(data.loc[:, 'Sepal.Length':'Petal.Width']

, diagonal = 'kde', color = [col_map[lb] for lb in data['Species']], s = 75, figsize = (11, 6))

plt.show()

输出为:

image.png

正式开始处理

from sklearn.datasets import load_iris

import matplotlib.pyplot as plt

iris = load_iris()

print("Iris Dataset contains %s samples in total，%s features."%(iris.data.shape[0], iris.data.shape[1]))# 输出为Iris Dataset contains 150 samples in total，4 features.

'''

iris.data[:5]

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

[4.9, 3. , 1.4, 0.2],

[4.7, 3.2, 1.3, 0.2],

[4.6, 3.1, 1.5, 0.2],

[5. , 3.6, 1.4, 0.2]])

iris.target

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])

'''

from sklearn.model_selection import train_test_split

X = iris.data[:,:2]

Y = iris.target

x_train, x_test, y_train, y_test = train_test_split(X,Y, test_size = 0.3, random_state = 0)

from sklearn.linear_model import LogisticRegression

lr = LogisticRegression(penalty='l2',solver='newton-cg',multi_class='multinomial')

lr.fit(x_train,y_train)

print("Logistic Regression模型训练集的准确率：%.3f" %lr.score(x_train, y_train))# Logistic Regression模型训练集的准确率：0.829

print("Logistic Regression模型测试集的准确率：%.3f" %lr.score(x_test, y_test))# Logistic Regression模型测试集的准确率：0.822

target_names = ['setosa', 'versicolor', 'virginica']

print(metrics.classification_report(y_test, y_hat, target_names = target_names))

'''

输出为：

precision recall f1-score support

setosa 1.00 1.00 1.00 16

versicolor 0.81 0.72 0.76 18

virginica 0.62 0.73 0.67 11

avg / total 0.83 0.82 0.82 45

'''