决策树分类鸢尾花数据demo

code:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import pydotplus

if __name__ == "__main__":
   
	iris_feature_E = "sepal lenght", "sepal width", "petal length", "petal width"
	iris_feature = "the length of sepal", "the width of sepal", "the length of petal", "the width of petal"
	iris_class = "Iris-setosa", "Iris-versicolor", "Iris-virginica"
	
	data = pd.read_csv("iris.data", header=None)
	iris_types = data[4].unique()
	for i, type in enumerate(iris_types):
		data.set_value(data[4] == type, 4, i)
	x, y = np.split(data.values, (4,), axis=1)
	x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state=1)
	print(y_test)

	model = DecisionTreeClassifier(criterion='entropy', max_depth=6)
	model = model.fit(x_train, y_train)
	y_test_hat = model.predict(x_test)
	with open('iris.dot', 'w') as f:
		tree.export_graphviz(model, out_file=f)
	dot_data = tree.export_graphviz(model, out_file=None, feature_names=iris_feature_E, class_names=iris_class,
		filled=True, rounded=True, special_characters=True)
	graph = pydotplus.graph_from_dot_data(dot_data)
	graph.write_pdf('iris.pdf')
	f = open('iris.png', 'wb')
	f.write(graph.create_png())
	f.close()

	# 画图
	# 横纵各采样多少个值
	N, M = 50, 50
	# 第0列的范围
	x1_min, x1_max = x[:, 0].min(), x[:, 0].max()
	# 第1列的范围
	x2_min, x2_max = x[:, 1].min(), x[:, 1].max()
	t1 = np.linspace(x1_min, x1_max, N)
	t2 = np.linspace(x2_min, x2_max, M)
	# 生成网格采样点
	x1, x2 = np.meshgrid(t1, t2)
    # # 无意义，只是为了凑另外两个维度
    # # 打开该注释前，确保注释掉x = x[:, :2]
	x3 = np.ones(x1.size) * np.average(x[:, 2])
	x4 = np.ones(x1.size) * np.average(x[:, 3])
	# 测试点
	x_show = np.stack((x1.flat, x2.flat, x3, x4), axis=1)
	print("x_show_shape:\n", x_show.shape)

	cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
	cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
	# 预测值
	y_show_hat = model.predict(x_show)
	print(y_show_hat.shape)
	print(y_show_hat)
	# 使之与输入的形状相同
	y_show_hat = y_show_hat.reshape(x1.shape)
	print(y_show_hat)
	plt.figure(figsize=(15, 15), facecolor='w')
	# 预测值的显示
	plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light)
	print(y_test)
	print(y_test.ravel())
	# 测试数据
	plt.scatter(x_test[:, 0], x_test[:, 1], c=np.squeeze(y_test), edgecolors='k', s=120, cmap=cm_dark, marker='*')
	# 全部数据
	plt.scatter(x[:, 0], x[:, 1], c=np.squeeze(y), edgecolors='k', s=40, cmap=cm_dark)
	plt.xlabel(iris_feature[0], fontsize=15)
	plt.ylabel(iris_feature[1], fontsize=15)
	plt.xlim(x1_min, x1_max)
	plt.ylim(x2_min, x2_max)
	plt.grid(True)
	plt.title('yuanwei flowers regressiong with DecisionTree', fontsize=17)
	plt.show()

	# 训练集上的预测结果
	y_test = y_test.reshape(-1)
	print(y_test_hat)
	print(y_test)
	# True则预测正确，False则预测错误
	result = (y_test_hat == y_test)
	acc = np.mean(result)
	print('accuracy: %.2f%%' % (100 * acc))

    # 过拟合：错误率
	depth = np.arange(1, 15)
	err_list = []
	for d in depth:
		clf = DecisionTreeClassifier(criterion='entropy', max_depth=d)
		clf = clf.fit(x_train, y_train)
		# 测试数据
		y_test_hat = clf.predict(x_test)
		# True则预测正确，False则预测错误
		result = (y_test_hat == y_test)
		err = 1 - np.mean(result)
		err_list.append(err)
		print(d, 'error ratio: %.2f%%' % (100 * err))
	plt.figure(figsize=(15, 15), facecolor='w')
	plt.plot(depth, err_list, 'ro-', lw=2)
	plt.xlabel('DecisionTree Depth', fontsize=15)
	plt.ylabel('error ratio', fontsize=15)
	plt.title('DecisionTree Depth and Overfit', fontsize=17)
	plt.grid(True)
	plt.show()

生成的图文件：

鸢尾花的数据特征一共有四种：花萼长度、花萼宽度，花瓣长度，花瓣宽度。然后再使用决策树两两特征进行分类：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import pydotplus

if __name__ == "__main__":
   
	iris_feature_E = "sepal lenght", "sepal width", "petal length", "petal width"
	iris_feature = "the length of sepal", "the width of sepal", "the length of petal", "the width of petal"
	iris_class = "Iris-setosa", "Iris-versicolor", "Iris-virginica"
	
	data = pd.read_csv("iris.data", header=None)
	iris_types = data[4].unique()
	for i, type in enumerate(iris_types):
		data.set_value(data[4] == type, 4, i)
	x_train, y = np.split(data.values, (4,), axis=1)

	feature_pairs = [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]
	plt.figure(figsize=(15, 15), facecolor='w')
	for i, pair in enumerate(feature_pairs):
		# 准备数据
		x = x_train[:, pair]
		# 决策树进行学习
		clf = DecisionTreeClassifier(criterion='entropy', min_samples_leaf=3)
		dt_clf = clf.fit(x, y)
		# 开始画图
		N, M = 500, 500
		# 第0列的范围
		x1_min, x1_max = x[:, 0].min(), x[:, 0].max()   
    	# 第1列的范围
		x2_min, x2_max = x[:, 1].min(), x[:, 1].max()   
		t1 = np.linspace(x1_min, x1_max, N)
		t2 = np.linspace(x2_min, x2_max, M)
    	# 生成网格采样点
		x1, x2 = np.meshgrid(t1, t2)           
    	# 测试点         
		x_test = np.stack((x1.flat, x2.flat), axis=1)
		# 在训练集上预测结果
		y_hat = dt_clf.predict(x)
		y = y.reshape(-1)
		# 统计预测正确的个数
		c = np.count_nonzero(y_hat == y)
		print("y_hat:\n", y_hat)
		print("y:\n", y)
		'''
		set1 = set(y_hat)
		set2 = set(y)
		print(list(set1 & set2))
		if y_hat.any() != y.any():
			print('predict:%.3f   real:%.3f' %(y_hat.all(), y.all()))
		'''
		# 打印相关信息
		print('features:\t', iris_feature[pair[0]], ' + ', iris_feature[pair[1]])
		print('the number of true prediction:', c)
		print('acc:%.2f%%' %(100 * float(c) / float(len(y))))

		# 画图显示
		cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
		cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
		# 预测值
		y_test_hat = dt_clf.predict(x_test)
		# reshape到和输入的x1相同格式
		y_test_hat = y_test_hat.reshape(x1.shape)
		plt.subplot(2, 3, i+1)
		plt.pcolormesh(x1, x2, y_test_hat, cmap=cm_light)
		plt.scatter(x[:, 0], x[:, 1], c=y, edgecolors='k', cmap=cm_dark)
		plt.xlabel(iris_feature[pair[0]], fontsize=14)
		plt.ylabel(iris_feature[pair[1]], fontsize=14)
		plt.xlim(x1_min, x1_max)
		plt.ylim(x2_min, x2_max)
		plt.grid()
	plt.suptitle('the result of yuanwei flowers in each two features with dcisiontree', fontsize=20)
	plt.tight_layout(2)
	plt.subplots_adjust(top=0.92)
	plt.show()

显然第二种组合效果还可以的。

接着我们使用随机森林算法来分类看看效果：

只需要在上面的代码中修改：

# 决策树进行学习
clf = DecisionTreeRegressor(n_estimators=200, criterion='entropy', max_depth=6)

为：

# 决策树进行学习
clf = RandomForestClassifier(n_estimators=200, criterion='entropy', max_depth=6)

效果：

看得出来随机森林的分类要比决策树好，随机森林因为是根据多个决策树弱分类器联合成一个强分类器，所以其边界出呈现很多的锯齿，分类的准确度也提高很多,150个数据，最后只有一个分错。