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30鱼币
附件那边上传大小必须2m以下，我就只取了csv前面几行数据
代码是这样写的：
#信用卡欺诈检测
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#%matplotlib inline 魔法方法ipython中用
data = pd.read_csv(r"D:\储存\学习\唐宇迪python\01、python数据分析与机器学习实战\课程资料\唐宇迪-机器学习课程资料\机器学习算法配套案例实战\逻辑回归-信用卡欺诈检测\creditcard.csv")
count_classes = pd.value_counts(data['Class'], sort = True).sort_index()
count_classes.plot(kind = 'bar') #建立柱状图
plt.title("Fraud class histogram")
plt.xlabel("Class")
plt.ylabel("Frequency")
from sklearn.preprocessing import StandardScaler
data['normAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Time','Amount'],axis=1)
X = data.ix[:, data.columns != 'Class']
y = data.ix[:, data.columns == 'Class']
number_records_fraud = len(data[data.Class == 1])
fraud_indices = np.array(data[data.Class == 1].index)
normal_indices = data[data.Class == 0].index
random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace = False)
random_normal_indices = np.array(random_normal_indices)
under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])
under_sample_data = data.iloc[under_sample_indices,:]
X_undersample = under_sample_data.ix[:, under_sample_data.columns != 'Class']
y_undersample = under_sample_data.ix[:, under_sample_data.columns == 'Class']
# Showing ratio
print("Percentage of normal transactions: ", len(under_sample_data[under_sample_data.Class == 0])/len(under_sample_data))
print("Percentage of fraud transactions: ", len(under_sample_data[under_sample_data.Class == 1])/len(under_sample_data))
print("Total number of transactions in resampled data: ", len(under_sample_data))
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 0)
print("Number transactions train dataset: ", len(X_train))
print("Number transactions test dataset: ", len(X_test))
print("Total number of transactions: ", len(X_train)+len(X_test))
# 下采样
X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = train_test_split(X_undersample,y_undersample,test_size = 0.3,random_state = 0)
print("")
print("Number transactions train dataset: ", len(X_train_undersample))
print("Number transactions test dataset: ", len(X_test_undersample))
print("Total number of transactions: ", len(X_train_undersample)+len(X_test_undersample))
#Recall = TP/(TP+FN)
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix,recall_score,classification_report
#交叉验证
def printing_Kfold_scores(x_train_data,y_train_data):
fold = KFold(5,shuffle=False)
# fold.get_n_splits(len(y_train_data))
# 定义不同正则化惩罚力度
c_param_range = [0.01,0.1,1,10,100]
#可视化显示
results_table = pd.DataFrame(index = range(len(c_param_range),2), columns = ['C_parameter','Mean recall score'])
results_table['C_parameter'] = c_param_range
# the k-fold will give 2 lists: train_indices = indices[0], test_indices = indices[1]
j = 0
for c_param in c_param_range:
print('-------------------------------------------')
print('C parameter: ', c_param)
print('-------------------------------------------')
print('')
recall_accs = []
for iteration, indices in fold.split(X):
lr = LogisticRegression(C = c_param, penalty = 'l1')
# Use the training data to fit the model. In this case, we use the portion of the fold to train the model
# with indices[0]. We then predict on the portion assigned as the 'test cross validation' with indices[1]
lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())
# Predict values using the test indices in the training data
y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)
# Calculate the recall score and append it to a list for recall scores representing the current c_parameter
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)
# The mean value of those recall scores is the metric we want to save and get hold of.
results_table.ix[j,'Mean recall score'] = np.mean(recall_accs)
j += 1
print('')
print('Mean recall score ', np.mean(recall_accs))
print('')
best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']
# Finally, we can check which C parameter is the best amongst the chosen.
print('*********************************************************************************')
print('Best model to choose from cross validation is with C parameter = ', best_c)
print('*********************************************************************************')
return best_c
best_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)
===========================================================================================
结果就返回错误：
ValueError: Expected 2D array, got 1D array instead:
array=[-1.86375555 3.44264398 -4.46825973 2.80533626 -2.11841248 -2.33228489
-4.2612372 1.70168184 -1.43939588 -6.99990663 6.31620968 -8.670818
0.31602399 -7.41771206 -0.43653747 -3.65280196 -6.29314532 -1.24324829
0.36481048 0.360924 0.66792657 -0.51624236 -0.01221781 0.0706137
0.05850447 0.30488284 0.41801247 0.20885828 -0.34923131].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
是这行有什么问题嘛？辛苦各位大神帮忙看看啦~
lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())
维度不对，期待的是二维矩阵，而且都提示将其reshape下就行，至于哪个维度该是1，你自己跑的应该更明白
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维度不对，期待的是二维矩阵，而且都提示将其reshape下就行，至于哪个维度该是1，你自己跑的应该更明白
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狗仔卡
发表于 2019-12-11 16:23:57
置顶卡
千斤顶
显身卡
发表于 2019-12-11 16:23:58
楼主