Euskalduna - 1 year ago 324

Python Question

I am new in machine learning and in scikit-learn.

**My problem:**

(Please, correct any type of missconception)

I have a dataset which is a BIG JSON, I retrieve it and store it in a

`trainList`

I pre-process it in order to be able to work with it.

Once I have done that, I start the classification:

- I use kfold cross validation method in order to obtain the mean

accuracy and I train a classifier. - I make the predicctions and I obtain the accuracy and confusion matrix of that fold.
- After this, I would like to obtain the True Positive(TP), True Negative(TN), False Positive(FP) and False Negative(FN) values. I would use these paramters to obtain the Sensitivity and the specificity and I would them and the total of the TPs to a HTML in order to show a chart with the TPs of each label.

The variables I have for the moment:

`trainList #It is a list with all the data of my dataset in JSON form`

labelList #It is a list with all the labels of my data

Most part of the method:

`#I transform the data from JSON form to a numerical one`

X=vec.fit_transform(trainList)

#I scale the matrix (don't know why but without it, it makes an error)

X=preprocessing.scale(X.toarray())

#I generate a KFold in order to make cross validation

kf = KFold(len(X), n_folds=10, indices=True, shuffle=True, random_state=1)

#I start the cross validation

for train_indices, test_indices in kf:

X_train=[X[ii] for ii in train_indices]

X_test=[X[ii] for ii in test_indices]

y_train=[listaLabels[ii] for ii in train_indices]

y_test=[listaLabels[ii] for ii in test_indices]

#I train the classifier

trained=qda.fit(X_train,y_train)

#I make the predictions

predicted=qda.predict(X_test)

#I obtain the accuracy of this fold

ac=accuracy_score(predicted,y_test)

#I obtain the confusion matrix

cm=confusion_matrix(y_test, predicted)

#I should calculate the TP,TN, FP and FN

#I don't know how to continue

Answer Source

If you have two lists that have the predicted and actual values; as it appears you do you can pass them to a function that will calculate TP, FP, TN, FN with something like this:

```
def perf_measure(y_actual, y_hat):
TP = 0
FP = 0
TN = 0
FN = 0
for i in range(len(y_hat)):
if y_actual[i]==y_hat[i]==1:
TP += 1
for i in range(len(y_hat)):
if y_actual[i]==1 and y_actual!=y_hat[i]:
FP += 1
for i in range(len(y_hat)):
if y_actual[i]==y_hat[i]==0:
TN += 1
for i in range(len(y_hat)):
if y_actual[i]==0 and y_actual!=y_hat[i]:
FN += 1
return(TP, FP, TN, FN)
```

From here you I think you will be able to calculate rates of interest to you, and other performance measure like specificity and sensitivity.