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 </span>= [0.01, 0.1, 1, 10, 100<span style="color: #000000;">]
</span><span style="color: #008000;">#</span><span style="color: #008000;">创建一个空的列表用来存储Mean recall score的值</span>
results_table = pd.DataFrame(index=range(len(c_param_range), 2), columns=[<span style="color: #800000;">'</span><span style="color: #800000;">C_parameter</span><span style="color: #800000;">'</span>, <span style="color: #800000;">'</span><span style="color: #800000;">Mean recall score</span><span style="color: #800000;">'</span><span style="color: #000000;">])

results_table[</span><span style="color: #800000;">'</span><span style="color: #800000;">C_parameter</span><span style="color: #800000;">'</span>] =<span style="color: #000000;"> c_param_range


j </span>=<span style="color: #000000;"> 0
</span><span style="color: #0000ff;">for</span> c_param <span style="color: #0000ff;">in</span><span style="color: #000000;"> c_param_range:
    </span><span style="color: #0000ff;">print</span>(<span style="color: #800000;">'</span><span style="color: #800000;">-----------------------</span><span style="color: #800000;">'</span><span style="color: #000000;">)
    </span><span style="color: #0000ff;">print</span>(<span style="color: #800000;">'</span><span style="color: #800000;">C paramter:</span><span style="color: #800000;">'</span><span style="color: #000000;">, c_param)
    </span><span style="color: #0000ff;">print</span>(<span style="color: #800000;">'</span><span style="color: #800000;">-----------------------</span><span style="color: #800000;">'</span><span style="color: #000000;">)
    </span><span style="color: #0000ff;">print</span>(<span style="color: #800000;">''</span><span style="color: #000000;">)

    recall_accs </span>=<span style="color: #000000;"> []
    </span><span style="color: #0000ff;">for</span> iteration, indices <span style="color: #0000ff;">in</span> enumerate(fold, start=1<span style="color: #000000;">):
        lr </span>= LogisticRegression(C = c_param, penalty=<span style="color: #800000;">'</span><span style="color: #800000;">l1</span><span style="color: #800000;">'</span>)  <span style="color: #008000;">#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)
    </span><span style="color: #008000;">#</span><span style="color: #008000;">求得平均的值</span>
    results_table.ix[j, <span style="color: #800000;">'</span><span style="color: #800000;">Mean recall score</span><span style="color: #800000;">'</span>] =<span style="color: #000000;"> np.mean(recall_accs)
    j </span>+= 1
    <span style="color: #0000ff;">print</span>(<span style="color: #800000;">''</span><span style="color: #000000;">)
    </span><span style="color: #0000ff;">print</span>(<span style="color: #800000;">'</span><span style="color: #800000;">Mean recall score</span><span style="color: #800000;">'</span><span style="color: #000000;">, np.mean(recall_accs))
    </span><span style="color: #0000ff;">print</span>(<span style="color: #800000;">''</span><span style="color: #000000;">)
</span><span style="color: #008000;">#</span><span style="color: #008000;"> 数据类型进行转换</span>
results_table[<span style="color: #800000;">'</span><span style="color: #800000;">Mean recall score</span><span style="color: #800000;">'</span>] = results_table[<span style="color: #800000;">'</span><span style="color: #800000;">Mean recall score</span><span style="color: #800000;">'</span>].astype(<span style="color: #800000;">'</span><span style="color: #800000;">float64</span><span style="color: #800000;">'</span><span style="color: #000000;">)
</span><span style="color: #008000;">#</span><span style="color: #008000;"> 求得Mean recall score 对应的最大的C_parameter值</span>
best_c = results_table.loc[results_table[<span style="color: #800000;">'</span><span style="color: #800000;">Mean recall score</span><span style="color: #800000;">'</span>].idxmax()][<span style="color: #800000;">'</span><span style="color: #800000;">C_parameter</span><span style="color: #800000;">'</span><span style="color: #000000;">]
</span><span style="color: #0000ff;">print</span>(<span style="color: #800000;">'</span><span style="color: #800000;">*********************************************************************************</span><span style="color: #800000;">'</span><span style="color: #000000;">)
</span><span style="color: #0000ff;">print</span>(<span style="color: #800000;">'</span><span style="color: #800000;">Best model to choose from cross validation is with C parameter = </span><span style="color: #800000;">'</span><span style="color: #000000;">, best_c)
</span><span style="color: #0000ff;">print</span>(<span style="color: #800000;">'</span><span style="color: #800000;">*********************************************************************************</span><span style="color: #800000;">'</span><span style="color: #000000;">)

</span><span style="color: #0000ff;">return</span> best_c<br><br>#执行程序</pre>

best_c = printing_Kfold_socres(X_train_undersample, y_train_undersample)