全文代码如下
需要在github上下载相关数据集,下载整个包,在data中找到ram_prices.csv即可
#决策树
import mglearn
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
cancer = load_breast_cancer()
x_train,x_test,y_train,y_test = train_test_split(cancer.data,cancer.target,stratify=cancer.target,random_state=42)
tree = DecisionTreeClassifier(random_state=0)
tree.fit(x_train,y_train)
print('accuracy on training set:{:.3f}'.format(tree.score(x_train,y_train)))
print('accuracy on test set:{:.3f}'.format(tree.score(x_test,y_test)))
#树的深度为4
tree = DecisionTreeClassifier(max_depth=4,random_state=0)
tree.fit(x_train,y_train)
print('accuracy on training set:{:.3f}'.format(tree.score(x_train,y_train)))
print('accuracy on test set:{:.3f}'.format(tree.score(x_test,y_test)))
from sklearn.tree import export_graphviz
import graphviz
export_graphviz(tree,out_file='tree.dot',class_names=['malignant','benign'],feature_names=cancer.feature_names,impurity=False,filled=True)
with open('tree.dot') as f:
dot_graph = f.read()
graphviz.Source(dot_graph)
print('feature importances:{}'.format(tree.feature_importances_))
def plot_feature_importances_cancer(model):
n_features = cancer.data.shape[1]
plt.barh(range(n_features),model.feature_importances_,align='center')
plt.yticks(np.arange(n_features),cancer.feature_names)
plt.xlabel('feature importance')
plt.ylabel("feature")
plt.show()
from IPython import display
plot_feature_importances_cancer(tree)
tree = mglearn.plots.plot_tree_not_monotone()
display.display(tree)
plt.show()
#计算机内存价格
ram_prices = pd.read_csv("ram_price.csv")
plt.semilogy(ram_prices.date,ram_prices.price)
plt.xlabel('year')
plt.ylabel('price in $/mbyte')
plt.show()
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression
data_train = ram_prices[ram_prices.date < 2000]
data_test = ram_prices[ram_prices.date >= 2000]
x_train = data_train.date[:,np.newaxis]
y_train = np.log(data_train.price)
tree = DecisionTreeRegressor().fit(x_train,y_train)
linear_reg = LinearRegression().fit(x_train,y_train)
x_all = ram_prices.date[:,np.newaxis]
pred_tree = tree.predict(x_all)
pred_lr = linear_reg.predict(x_all)
price_tree = np.exp(pred_tree)
price_lr = np.exp(pred_lr)
plt.semilogy(data_train.date,data_train.price,label='training data')
plt.semilogy(data_test.date,data_test.price,label='test data')
plt.semilogy(ram_prices.date,price_tree,label='tree prediction')
plt.semilogy(ram_prices.date,price_lr,label='linear prediction')
plt.legend()
plt.show()