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Here is a sample of my data set:
Pat_ID Flare_Up Demo1 Demo2 Demo3 Demo4 Demo5 Demo6 DisHis1 DisHis1Times DisHis2 ... Dis6Treat Dis7 RespQues1 ResQues1a ResQues1b ResQues1c ResQues2a SmokHis1 SmokHis2 SmokHis3 SmokHis4
0 1 0 1 0.246004 0.391931 0.237792 0 0.443526 0 0.000000 0 ... 1 0 0.12623 0.1032 0.2439 0.0597 0.0 0.411765 0.263620 0.482759 0.1875
1 2 1 1 0.225851 0.268012 0.268481 0 0.286501 0 0.000000 1 ... 1 0 0.60707 0.3808 0.8637 0.4949 0.1 0.117647 0.098418 0.624138 0.0000
2 3 0 0 0.342599 0.476945 0.296468 1 0.159780 1 0.166667 1 ... 0 0 0.77541 0.6318 1.0000 0.6570 0.3 0.035294 0.020211 0.510345 0.0000
[3 rows x 62 columns]
My code to traverse through that data set and print ROC is:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
import itertools
def plot_confusion_matrix(cm, classes, normalize=True, title='Confusion matrix', cmap=plt.cm.Blues):
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
# else:
# print('Confusion matrix, without normalization')
# print(cm)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
def show_data(cm, print_res = 0):
tp = cm[1,1]
fn = cm[1,0]
fp = cm[0,1]
tn = cm[0,0]
if print_res == 1:
print('Precision = {:.3f}'.format(tp/(tp+fp)))
print('Recall (TPR) = {:.3f}'.format(tp/(tp+fn)))
print('Fallout (FPR) = {:.3e}'.format(fp/(fp+tn)))
return tp/(tp+fp), tp/(tp+fn), fp/(fp+tn)
df = pd.read_csv("datasource/DevelopmentData.csv")
print(df.head(3))
y = np.array(df.Class.tolist()) #classes: 1..fraud, 0..no fraud
df = df.drop('Class', 1)
df = df.drop('Time', 1) # optional
df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1,1)) #optionally rescale non-normalized column
X = np.array(df.as_matrix()) # features
A class of 0 means that the transaction was in order, and a class of 1 means that the transaction was fraudulent.
When I run my code, I get this error:
Traceback (most recent call last):
File "finalindex.py", line 54, in <module>
y = np.array(df.Class.tolist()) #classes: 1..fraud, 0..no fraud
File "C:\Users\kulkaa\AppData\Local\Programs\Python\Python37-32\lib\site-packages\pandas\core\generic.py", line 4376, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'Class'
How can I fix that error? Do I need to change column names according to data set?
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[...] The author of this
link (kaggle.com/dstuerzer/optimized-logistic-regression) has used it
and it is working fine with his code.
In the link you mentioned, example, the author's database has a column named "Class" but the database that you have shown does not. As a result, the Class attribute does not exist in your database and therefore cannot be accessed.
Dominik Stuerzer:
Time V1 V2 V3 V4 V5 V6 V7 \
0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599
1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803
2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461
V8 V9 ... V21 V22 V23 V24 \
0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928
1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846
2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281
V25 V26 V27 V28 Amount Class
0 0.128539 -0.189115 0.133558 -0.021053 149.62 0
1 0.167170 0.125895 -0.008983 0.014724 2.69 0
2 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0
[3 rows x 31 columns]
A class of 0 means that the transaction was in order, and a class of 1
means that the transaction was fraudulent. From personal experience we
expect frauds to make up only a tiny fraction of all transactions.
Indeed, in this dataset, for every fraud there are almost 600
non-fraudulent transactions: [...]
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