#Cp交叉验证，选择最优的k值进行判别分析
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
X = heart.iloc[:, 0:5]
y = heart.loc[:, 'y']
k_range = range(1, 31)
k_scores = []
for k in k_range:
knn = KNeighborsClassifier(n_neighbors=k)
scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')
k_scores.append(scores.mean())
plt.plot(k_range, k_scores)
plt.xlabel('Value of K for KNN')
plt.ylabel('Cross-Validated Accuracy')
#选择最优的k值
k = k_scores.index(max(k_scores)) + 1
print('Optimal k: %d' % k)
#绘制最优k值在图中的位置
plt.plot(k_range, k_scores)
plt.xlabel('Value of K for KNN')
plt.ylabel('Cross-Validated Accuracy')
plt.scatter(k, max(k_scores), color='red')
#显示最优k直在图中等于多少
plt.text(k, max(k_scores), '(%d, %.2f)' % (k, max(k_scores)), ha='center', va='bottom')
plt.show()
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Optimal k: 22
KNN分类器
python复制代码
#使用最优k值建立KNN进行分类
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
# Initialize and fit the KNN classifier
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
# Predict and print accuracy
y_pred = knn.predict(X_test)
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
#绘制决策区域
from matplotlib.colors import ListedColormap
import numpy as np
from sklearn.decomposition import PCA
def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
# Reduce dimensionality to 2D using PCA
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])
# plot the decision surface
x1_min, x1_max = X_pca[:, 0].min() - 1, X_pca[:, 0].max() + 1
x2_min, x2_max = X_pca[:, 1].min() - 1, X_pca[:, 1].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(pca.inverse_transform(np.array([xx1.ravel(), xx2.ravel()]).T))
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X_pca[y == cl, 0], y=X_pca[y == cl, 1],
alpha=0.8, c=[cmap(idx)],
marker=markers[idx], label=cl)
# highlight test samples
if test_idx:
X_test, y_test = X_pca[test_idx, :2], y[test_idx]
plt.scatter(X_test[:, 0], X_test[:, 1],
alpha=1.0, linewidth=1, marker='o',
s=55, label='test set')
# Plot decision regions using PCA-transformed features
X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined, y=y_combined, classifier=knn, test_idx=range(len(y_train), len(y_train) + len(y_test)))
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend(loc='upper left')
plt.show()
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Accuracy: 0.69
朴素贝叶斯分类器
python复制代码
#朴素贝叶斯分类器
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from matplotlib.colors import ListedColormap
# Load the dataset
heart = pd.read_csv(r"heart.csv", sep=',')
# Select features and target
X = heart.iloc[:, 0:5]
y = heart.loc[:, 'y']
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
# Initialize and fit the Gaussian Naive Bayes classifier
gnb = GaussianNB()
gnb.fit(X_train, y_train)
# Predict and print accuracy
y_pred = gnb.predict(X_test)
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
# Define the function to plot decision regions
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.decomposition import PCA
def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
# Reduce dimensionality to 2D using PCA
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])
# plot the decision surface
x1_min, x1_max = X_pca[:, 0].min() - 1, X_pca[:, 0].max() + 1
x2_min, x2_max = X_pca[:, 1].min() - 1, X_pca[:, 1].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(pca.inverse_transform(np.array([xx1.ravel(), xx2.ravel()]).T))
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X_pca[y == cl, 0], y=X_pca[y == cl, 1],
alpha=0.8, c=[cmap(idx)],
marker=markers[idx], label=cl)
# # highlight test samples
# if test_idx:
# X_test, y_test = X_pca[test_idx, :2], y[test_idx]
# plt.scatter(X_test[:, 0], X_test[:, 1],
# alpha=1.0, linewidth=1, marker='o',
# s=55, label='test set')
# Plot decision regions using PCA-transformed features
X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined, y=y_combined, classifier=gnb, test_idx=range(len(y_train), len(y_train) + len(y_test)))
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend(loc='upper left')
plt.show()
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Accuracy: 0.70
SVM分类器
python复制代码
#使用SVM进行分类
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
# Load the dataset
heart = pd.read_csv(r"heart.csv", sep=',')
# Select features and target
X = heart.iloc[:, 0:5]
y = heart.loc[:, 'y']
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
# Initialize and fit the SVM classifier
svm = SVC(kernel='linear', C=1.0, random_state=0)
svm.fit(X_train, y_train)
# Predict and print accuracy
y_pred = svm.predict(X_test)
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
# Import necessary libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
import pydotplus
from IPython.display import Image
# Load the dataset
heart = pd.read_csv(r"heart.csv", sep=',')
# Select features and target
X = heart.iloc[:, 0:5]
y = heart.loc[:, 'y']
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
# Initialize and fit the Decision Tree classifier
tree = DecisionTreeClassifier(max_depth=3, random_state=0)
tree.fit(X_train, y_train)
# Predict and print accuracy
y_pred = tree.predict(X_test)
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
# Export the decision tree to a file
export_graphviz(tree, out_file='tree.dot', feature_names=X.columns)
# Convert the dot file to a png
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())
# Plot decision regions using PCA-transformed features
X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined, y=y_combined, classifier=tree, test_idx=range(len(y_train), len(y_train) + len(y_test)))
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend(loc='upper left')
plt.show()
