跟我学算法-Logistic回归

虽然Logistic回归叫回归，但是其实它是一个二分类或者多分类问题

这里的话我们使用信用诈骗的数据进行分析

第一步：导入数据，Amount的数值较大，后续将进行(-1,1)的归一化

data = pd.read_csv('creditcard.csv')  #读取数据

#查看前5行数据
print(data.head())

第二步: 对正常和欺诈的数目进行查看，正常样本的数目远大于欺诈样本，这个时候可以使用下采样或者过采样

# 画图查看
count_data = pd.value_counts(data['Class'], sort=True).sort_index()  #统计样本数
count_data.plot(kind='bar')  #画条形图
plt.title("Fraud class histogram")  #标题
plt.xlabel('Classes')
plt.ylabel('Frequency')
plt.show()

第三步：将amount进行归一化形成amountNorm，并且去除time和amount项

#把amount数据标准化到-1， 1
from sklearn.preprocessing import StandardScaler
#reshape 需要转换到的数值范围
data['NormAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1),)
data = data.drop(['Time', 'Amount'], axis=1)   # 去除两列

#进行分组
X = data.ix[:, data.columns != 'Class']
y = data.ix[:, data.columns == 'Class']

第四步，使用随机挑选来生成下采样数据

number_record_fraud = len(data[data.Class==1])
#找出其索引，组成数组
fraud_indices = np.array(data[data.Class == 1].index)
norm_indices = data[data.Class == 0 ].index
#从Class=0中任意挑选500个组成正常的类别
random_norm_indices = np.random.choice(norm_indices, 500, replace=False)
random_norm_indices = np.array(random_norm_indices)

#把正常的类别和欺诈类别进行组合
under_sample_indices = np.concatenate([fraud_indices, random_norm_indices])
#根据重组索引重新取值
under_sample_datas = data.iloc[under_sample_indices,:]
#选择出属性和结果
X_undersample = under_sample_datas.ix[:, under_sample_datas.columns != 'Class']

第5步，交叉验证选择权重，这里采用的加权方法为|L* w|

from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import KFold, cross_val_score
from sklearn.metrics import confusion_matrix,recall_score,classification_report
def printing_Kfold_socres(x_train_data, y_train_data):
    fold = KFold(len(y_train_data), 5, shuffle=False)


    c_param_range = [0.01, 0.1, 1, 10, 100]
    #创建一个空的列表用来存储Mean recall score的值
    results_table = pd.DataFrame(index=range(len(c_param_range), 2), columns=['C_parameter', 'Mean recall score'])

    results_table['C_parameter'] = c_param_range


    j = 0
    for c_param in c_param_range:
        print('-----------------------')
        print('C paramter:', c_param)
        print('-----------------------')
        print('')

        recall_accs = []
        for iteration, indices in enumerate(fold, start=1):
            lr = LogisticRegression(C = c_param, penalty='l1')  #放入参数，权重模式为l1
            print('indices', indices)
            #建立模型并训练
            lr.fit(x_train_data.iloc[indices[0], :], y_train_data.iloc[indices[0], :].values.ravel())
            y_pred_undersample = lr.predict(x_train_data.iloc[indices[1], :].values)
            print(y_pred_undersample)
            #计算回归得分
            recall_acc = recall_score(y_train_data.iloc[indices[1],:].values, y_pred_undersample)
            recall_accs.append(recall_acc)
            print('Iteration', iteration, ': recall score=', recall_acc)

        #求得平均的值
        results_table.ix[j, 'Mean recall score'] = np.mean(recall_accs)
        j += 1
        print('')
        print('Mean recall score', np.mean(recall_accs))
        print('')
    # 数据类型进行转换
    results_table['Mean recall score'] = results_table['Mean recall score'].astype('float64')
    # 求得Mean recall score 对应的最大的C_parameter值
    best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']
    print('*********************************************************************************')
    print('Best model to choose from cross validation is with C parameter = ', best_c)
    print('*********************************************************************************')

    return best_c

#执行程序

best_c = printing_Kfold_socres(X_train_undersample, y_train_undersample)