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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.neighbors import KNeighborsClassifier
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import numpy as np
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def knn_iris():
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"""
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用KNN算法对鸢尾花进行预测
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:return:
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"""
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# 1) 获取数据
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iris = load_iris()
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# 2）划分数据集
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x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=22)
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# 3）特征工程：标准化
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transfer = StandardScaler()
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x_train = transfer.fit_transform(x_train)
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x_test = transfer.transform(x_test)
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# 4) KNN算法预估器
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estimator = KNeighborsClassifier(n_neighbors=3)
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estimator.fit(x_train, y_train)
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# 5）模型估计
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# 方法1：直接比对真实值和预测值
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y_predict = estimator.predict(x_test)
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print("y_predict:\n", y_predict)
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print("直接比对真实值和预测值:\n", y_test ==y_predict)
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#方法2：计算准确值
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score = estimator.score(x_test, y_test)
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print("准确值为：\n", score)
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# 6) 做出预测
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X_new = np.array([[1.1, 5.9, 1.4, 2.2]])
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prediction = estimator.predict(X_new)
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print("预测目标类别是:{}".format(prediction))
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print("预测目标花名是:", iris["target_names"][prediction])
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return None
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if __name__ =="__main__":
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knn_iris()
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