scikit-learn KNN实现糖尿病预测

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随书代码，阅读笔记。

KNN是一种有监督的机器学习算法，可以解决分类问题，也可以解决回归问题。

算法优点：准确性高，对异常值和噪声有较高的容忍度；

算法缺点：计算量大，内存消耗也比较大。

针对算法计算量大，有一些改进的数据结构，避免重复计算K-D Tree， Ball Tree。

算法变种：根据邻居的距离，分配不同权重。另外一个变种是指定半径。

KNN进行分类

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pdfrom sklearn.datasets.samples_generator import make_blobs
# 生成数据
centers = [[-2, 2], [2, 2], [0, 4]]
X, y = make_blobs(n_samples=60, centers=centers, random_state=0, cluster_std=0.60)# 画出数据
plt.figure(figsize=(16, 10), dpi=144)
c = np.array(centers)
plt.scatter(X[:, 0], X[:, 1], c=y, s=100, cmap='cool');         # 画出样本
plt.scatter(c[:, 0], c[:, 1], s=100, marker='^', c='orange');   # 画出中心点from sklearn.neighbors import KNeighborsClassifier
# 模型训练
k = 5
clf = KNeighborsClassifier(n_neighbors=k)
clf.fit(X, y);# 进行预测
X_sample = [0, 2]
y_sample = clf.predict(X_sample);
neighbors = clf.kneighbors(X_sample, return_distance=False);# 画出示意图
plt.figure(figsize=(16, 10), dpi=144)
plt.scatter(X[:, 0], X[:, 1], c=y, s=100, cmap='cool');    # 样本
plt.scatter(c[:, 0], c[:, 1], s=100, marker='^', c='k');   # 中心点
plt.scatter(X_sample[0], X_sample[1], marker="x", c=y_sample, s=100, cmap='cool')    # 待预测的点for i in neighbors[0]:plt.plot([X[i][0], X_sample[0]], [X[i][1], X_sample[1]], 'k--', linewidth=0.6);    # 预测点与距离最近的 5 个样本的连线

KNN进行回归拟合

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np# 生成训练样本
n_dots = 40
X = 5 * np.random.rand(n_dots, 1)
y = np.cos(X).ravel()# 添加一些噪声
y += 0.2 * np.random.rand(n_dots) - 0.1# 训练模型
from sklearn.neighbors import KNeighborsRegressor
k = 5
knn = KNeighborsRegressor(k)
knn.fit(X, y);# 生成足够密集的点并进行预测
T = np.linspace(0, 5, 500)[:, np.newaxis]
y_pred = knn.predict(T)
knn.score(X, y)#output:0.98579189493611052# 画出拟合曲线
plt.figure(figsize=(16, 10), dpi=144)
plt.scatter(X, y, c='g', label='data', s=100)         # 画出训练样本
plt.plot(T, y_pred, c='k', label='prediction', lw=4)  # 画出拟合曲线
plt.axis('tight')
plt.title("KNeighborsRegressor (k = %i)" % k)
plt.show()

KNN 实现糖尿病预测

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd# 加载数据
data = pd.read_csv('datasets/pima-indians-diabetes/diabetes.csv')
print('dataset shape {}'.format(data.shape))
data.head()data.groupby("Outcome").size()
#Outcome
#0    500 无糖尿病
#1    268 有糖尿病
#dtype: int64X = data.iloc[:, 0:8]
Y = data.iloc[:, 8]
print('shape of X {}; shape of Y {}'.format(X.shape, Y.shape))from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2);from sklearn.neighbors import KNeighborsClassifier, RadiusNeighborsClassifiermodels = []
models.append(("KNN", KNeighborsClassifier(n_neighbors=2)))
models.append(("KNN with weights", KNeighborsClassifier(n_neighbors=2, weights="distance")))
models.append(("Radius Neighbors", RadiusNeighborsClassifier(n_neighbors=2, radius=500.0)))results = []
for name, model in models:model.fit(X_train, Y_train)results.append((name, model.score(X_test, Y_test)))
for i in range(len(results)):print("name: {}; score: {}".format(results[i][0],results[i][1]))#name: KNN; score: 0.681818181818
#name: KNN with weights; score: 0.636363636364
#name: Radius Neighbors; score: 0.62987012987from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score#kfold 训练10次，计算10次的平均准确率
results = []
for name, model in models:kfold = KFold(n_splits=10)cv_result = cross_val_score(model, X, Y, cv=kfold)results.append((name, cv_result))
for i in range(len(results)):print("name: {}; cross val score: {}".format(results[i][0],results[i][1].mean()))#name: KNN; cross val score: 0.714764183185
#name: KNN with weights; cross val score: 0.677050580998
#name: Radius Neighbors; cross val score: 0.6497265892#模型训练
knn = KNeighborsClassifier(n_neighbors=2)
knn.fit(X_train, Y_train)
train_score = knn.score(X_train, Y_train)
test_score = knn.score(X_test, Y_test)
print("train score: {}; test score: {}".format(train_score, test_score))#画出学习曲线
from sklearn.model_selection import ShuffleSplit
from common.utils import plot_learning_curveknn = KNeighborsClassifier(n_neighbors=2)
cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
plt.figure(figsize=(10, 6), dpi=200)
plot_learning_curve(plt, knn, "Learn Curve for KNN Diabetes", X, Y, ylim=(0.0, 1.01), cv=cv);#数据可视化
# 从8个特征中选择2个最重要的特征进行可视化from sklearn.feature_selection import SelectKBestselector = SelectKBest(k=2)
X_new = selector.fit_transform(X, Y)
X_new[0:5]results = []
for name, model in models:kfold = KFold(n_splits=10)cv_result = cross_val_score(model, X_new, Y, cv=kfold)results.append((name, cv_result))
for i in range(len(results)):print("name: {}; cross val score: {}".format(results[i][0],results[i][1].mean()))# 画出数据
plt.figure(figsize=(10, 6), dpi=200)
plt.ylabel("BMI")
plt.xlabel("Glucose")
plt.scatter(X_new[Y==0][:, 0], X_new[Y==0][:, 1], c='r', s=20, marker='o');         # 画出样本
plt.scatter(X_new[Y==1][:, 0], X_new[Y==1][:, 1], c='g', s=20, marker='^');         # 画出样本#2个特征和8个特征得到的结果差不多。分类效果达到了瓶颈