第6.1.2章机器学习之八字强弱分析

清疚 2022-02-24 02:18 231阅读 0赞

我对韦千里的《呱呱集》中108个命例，进行标注，尝试使用knn算法，计算命例的用神。结果先用最简单的“克泄耗”和“生助”两种，  
计算规则使用李洪城老师的《具体断四柱导读》  
![1][]  
**1 knn算法**

import numpy as np
    import pandas as pd
    from sklearn import metrics
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import MinMaxScaler
    
    def get_datas():
        file_name = r'/Users/dzm/Documents/bazi/201903291933.csv'
        file = pd.read_csv(file_name,usecols=['生','助','克','泄','耗','用神'],encoding='GBK')
        ar = np.array(file)
        return ar[:,:5],ar[:,5]
        
    def predict_1(x_data,y_data):
        scaler = MinMaxScaler()
        X = scaler.fit_transform(x_data)
        x_train,x_test,y_train,y_test = train_test_split(X,y_data,test_size=0.2)
        knn = KNeighborsClassifier(n_neighbors=3)
        knn.fit(x_train,y_train)
        print(knn)
        expected = y_test
        predicted = knn.predict(x_test)
        print(metrics.classification_report(expected, predicted))
        label = list(set(y_data))
        print(metrics.confusion_matrix(expected, predicted, labels=label))
       
    if __name__ == '__main__':
        x_data, y_data = get_datas()
        predict_1(x_data,y_data)

上面的脚本，有三段输出，怎么解读呢？  
第一段输出的，解释参考[sklearn 翻译笔记：KNeighborsClassifier][sklearn _KNeighborsClassifier]

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
               metric_params=None, n_jobs=None, n_neighbors=3, p=2,
               weights='uniform')

下面的输出，须要了解召回率、准确率、调和平均几个概念，参考[  
准确率与召回率][Link 1]、[如何解释召回率与准确率？][Link 2]，召回率指的样本中想匹配的种类占该类总数的比率，召回率又叫查全率，这个就容易理解一些。而准确率时对抽取的样本而言。下图描述了1和2两种分类的查全率和查准率。

precision    recall  f1-score   support
    
               1       0.83      0.45      0.59        11
               2       0.62      0.91      0.74        11
    
       micro avg       0.68      0.68      0.68        22
       macro avg       0.73      0.68      0.66        22
    weighted avg       0.73      0.68      0.66        22

下面的输出，须要了解[基于混淆矩阵的评价指标][Link 3]，查看[ML01 机器学习后利用混淆矩阵Confusion matrix 进行结果分析][ML01 _Confusion matrix]，就容易理解了。通过混淆矩阵，可以看出，我们的结果并不理想

[[ 5  6]
     [ 1 10]]

**2 决策树**  
决策树需要理解信息熵，参考[白话信息熵][Link 4]

import numpy as np
    import pandas as pd
    from sklearn import metrics
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import MinMaxScaler
    import matplotlib.pylab as plt
    from sklearn import tree
    
    def predict_2(x_data,y_data):
        scaler = MinMaxScaler()
        X = scaler.fit_transform(x_data)
        x_train, x_test, y_train, y_test = train_test_split(X, y_data, test_size=0.1)
        clf = tree.DecisionTreeClassifier()
        clf.fit(x_train,y_train)
        print(clf)
        expected = y_test
        predicted = clf.predict(x_test)
        print(metrics.classification_report(expected, predicted))
        label = list(set(y_data))
        print(metrics.confusion_matrix(expected, predicted, labels=label))

输出的结果为

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
                max_features=None, max_leaf_nodes=None,
                min_impurity_decrease=0.0, min_impurity_split=None,
                min_samples_leaf=1, min_samples_split=2,
                min_weight_fraction_leaf=0.0, presort=False, random_state=None,
                splitter='best')
                  precision    recall  f1-score   support
    
               1       0.38      1.00      0.55         3
               2       1.00      0.38      0.55         8
    
       micro avg       0.55      0.55      0.55        11
       macro avg       0.69      0.69      0.55        11
    weighted avg       0.83      0.55      0.55        11
    
    [[3 0]
     [5 3]]

[1]: /images/20220224/7b127a710b5342ba9b74715ea11d1ea0.png
[sklearn _KNeighborsClassifier]: https://blog.csdn.net/d12340555hf/article/details/80075150
[Link 1]: https://blog.csdn.net/zk_j1994/article/details/78478502
[Link 2]: https://www.zhihu.com/question/19645541
[Link 3]: https://blog.csdn.net/Ai_ViVi/article/details/43836641
[ML01 _Confusion matrix]: https://www.cnblogs.com/zhixingheyi/p/8097782.html
[Link 4]: https://blog.csdn.net/Hearthougan/article/details/76192381