KNN实现鸢尾花分类

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前言

如下提供了两种训练方式：

1. 常规训练 的话需要 自己去试那个K的值，一般试个 3、5、7、9 就行
2. 网格搜索训练 可以让 机器自己去试这个K的值，训练结束后使用最好的模型预测即可
3. N折交叉验证训练 会让训练量提升N倍，但是会最大化的利用已有数据进行训练和验证，一般来说折数多一些训练结果会变好，但也不宜过多，该方法 常用在数据量较少或者获取训练数据成本较高的情况

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一、安装sklearn

pip install scikit-learn

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二、常规训练

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd


if __name__ == '__main__':

    # 读取数据
    iris = load_iris()
    print(iris)
    # {
    #   'data': array([[5.1, 3.5, 1.4, 0.2], [4.9, 3. , 1.4, 0.2], ... [5.9, 3. , 5.1, 1.8]]),
    #   'target': array([0, 0, ... 2]),
    #   'frame': None,
    #   'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='
    #   'DESCR': '.. _iris_dataset:\n\nIris plants dataset ...\n\n|details-end|',
    #   'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'],
    #   'filename': 'iris.csv',
    #   'data_module': 'sklearn.datasets.data'
    # }

    # 训练集、测试集拆分
    x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=233, test_size=0.2)
    print(x_train)
    # [[4.8 3.  1.4 0.3]
    #  [6.3 3.3 4.7 1.6]
    #       ...
    #  [5.1 3.4 1.5 0.2]]
    print(x_test)
    # [[5.2 2.7 3.9 1.4]
    #  [6.8 3.2 5.9 2.3]
    #       ...
    #  [5.7 2.6 3.5 1. ]]
    print(y_train)
    # [0 1 ... 0]
    print(y_test)
    # [1 2 ... 1]

    # 参数标准化【特征缩放到均值为0标准差为1】
    scaler = StandardScaler()
    x_train = scaler.fit_transform(x_train)
    x_test = scaler.fit_transform(x_test)

    # 【常规训练】初始化KNN并指定K为5
    model = KNeighborsClassifier(n_neighbors=5)

    # 训练
    model.fit(x_train, y_train)

    # 评估
    predict = model.predict(x_test)
    print(predict)
    # [1 2 ... 1]

    # 打印评价指标
    accuracy = model.score(x_test, y_test)
    print(accuracy)
    # 0.9666666666666667

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三、网格搜素训练+N折交叉验证

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd


if __name__ == '__main__':

    # 读取数据
    iris = load_iris()
    print(iris)
    # {
    #   'data': array([[5.1, 3.5, 1.4, 0.2], [4.9, 3. , 1.4, 0.2], ... [5.9, 3. , 5.1, 1.8]]),
    #   'target': array([0, 0, ... 2]),
    #   'frame': None,
    #   'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='
    #   'DESCR': '.. _iris_dataset:\n\nIris plants dataset ...\n\n|details-end|',
    #   'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'],
    #   'filename': 'iris.csv',
    #   'data_module': 'sklearn.datasets.data'
    # }

    # 训练集、测试集拆分
    x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=233, test_size=0.2)
    print(x_train)
    # [[4.8 3.  1.4 0.3]
    #  [6.3 3.3 4.7 1.6]
    #       ...
    #  [5.1 3.4 1.5 0.2]]
    print(x_test)
    # [[5.2 2.7 3.9 1.4]
    #  [6.8 3.2 5.9 2.3]
    #       ...
    #  [5.7 2.6 3.5 1. ]]
    print(y_train)
    # [0 1 ... 0]
    print(y_test)
    # [1 2 ... 1]

    # 参数标准化【特征缩放到均值为0标准差为1】
    scaler = StandardScaler()
    x_train = scaler.fit_transform(x_train)
    x_test = scaler.fit_transform(x_test)

    # # 【常规训练】初始化KNN并指定K为5
    # model = KNeighborsClassifier(n_neighbors=5)

    # 【网格搜索训练+N折交叉验证】初始化KNN随便指定一个K值，将 3, 5, 7, 9 分别进行4折交叉验证训练，总训练次数为 16 次
    model = KNeighborsClassifier(n_neighbors=1)
    model = GridSearchCV(model, param_grid={'n_neighbors': [3, 5, 7, 9]}, cv=4)

    # 训练
    model.fit(x_train, y_train)


    # 评估【使用最优模型评估】
    predict = model.best_estimator_.predict(x_test)
    print(predict)
    # [1 2 ... 1]

    # 打印评价指标
    print(f'best accuracy： {model.best_score_}')
    print(f'results： {model.cv_results_}')

    # accuracy = model.score(x_test, y_test)
    # print(accuracy)
    # # 0.9666666666666667