I have created a very simple Artificial Neural Network in Python. In the example below I get an accuracy based off of values in a confusion matrix. These are results of the confusion matrix:
array([[3990, 2],
[ 56, 172]])
I am interested in finding the rows where it was marked as false positive(2) and false negative(56).
The following is my code:
#Import the dataset
X = DBF2.iloc[:, 1:2].values
y = DBF2.iloc[:, 2].values
#Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 0] = labelencoder_X_1.fit_transform(X[:, 0])
#Create dummy variables
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
#Remove 2 variables to avoid falling into the dummy variable trap
X = np.delete(X, [0], axis=1)
#Splitting the dataset
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,
random_state = 0)
#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
#Make the ANN
import keras
from keras.models import Sequential
from keras.layers import Dense
#Initialising the ANN
classifier = Sequential()
#Adding the input layer and the first hidden layer
classifier.add(Dense(units=200, kernel_initializer='uniform', activation='relu', input_dim=400))
#Adding a second hidden layer
classifier.add(Dense(units=200, kernel_initializer='uniform',
activation='relu'))
#Adding the output layer
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
#Compiling the ANN
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
#Fitting the ANN to the training set
classifier.fit(X_train, y_train, batch_size=10, epochs=20)
#Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
#Making the confusion matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
#Find accuracy of test set
TruePos = cm[0,0]
FalsePos = cm[0,1]
TrueNeg = cm[1,1]
FalseNeg = cm[1,0]
accuracy = float(TruePos + TrueNeg) / float(TruePos + FalsePos + TrueNeg + FalseNeg)
accuracy = accuracy*100
print "Test Accuracy: " ,accuracy
In order to do that, you can use a mask on ypred and ytest:
X_test[(y_test == 1) & (y_pred[:,0].T == 0)]
X_test[(y_test == 0) & (y_pred[:,0].T == 1)]
Or if you don't care about separating FN from FP:
X_test[(y_test != y_pred[:,0].T).T]