def preprocess_ml_data():
data = pd.read_csv(path)
pixel_list = data['pixels'].tolist()
emotions = data['emotion'].tolist()
faces = []
for pixels in pixel_list:
face = string_to_nparray(pixels)
face = face / float(255)
faces.append(face)
save_pck(faces, './ml_data/X')
save_pck(emotions, './ml_data/y')
def support_vector():
svc = svm.SVC(kernel='linear')
svc.fit(X_train, y_train)
save_pck(svc, './ml_model_saves/svm')
# svc = load('./ml_model_saves/svm.pck')
svc_predict = svc.predict(X_val)
print(classification_report(y_val, svc_predict))
cnf_matrix = (confusion_matrix(y_val, svc_predict))
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=EMOTIONS,
normalize=True,
title='Normalized confusion matrix SVM')
plt.show()
def naive_bayes():
gnb = GaussianNB()
gnb.fit(X_train, y_train)
save_pck(gnb, './ml_model_saves/gnb')
# gnb = load('./ml_model_saves/gnb.pck')
gnb_predict = gnb.predict(X_val)
print(classification_report(y_val, gnb_predict))
cnf_matrix = (confusion_matrix(y_val, gnb_predict))
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=EMOTIONS,
normalize=True,
title='Normalized confusion matrix NB')
plt.show()
def linear_disc():
lda = LinearDiscriminantAnalysis()
lda.fit(X_train, y_train)
save_pck(lda, './ml_model_saves/lda')
# lda = load('./ml_model_saves/lda.pck')
lda_predict = lda.predict(X_val)
print(classification_report(y_val, lda_predict))
cnf_matrix = (confusion_matrix(y_val, lda_predict))
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=EMOTIONS,
normalize=True,
title='Normalized confusion matrix LDA')
plt.show()
def random_forests():
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)
save_pck(rfc, './ml_model_saves/rfc')
# rfc = load('./ml_model_saves/rfc.pck')
rfc_predict = rfc.predict(X_val)
print(classification_report(y_val, rfc_predict))
cnf_matrix = (confusion_matrix(y_val, rfc_predict))
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=EMOTIONS,
normalize=True,
title='Normalized confusion matrix RF')
plt.show()
def ada_boost():
adc = AdaBoostClassifier()
adc.fit(X_train, y_train)
save_pck(adc, './ml_model_saves/adc')
# adc = load('./ml_model_saves/adc.pck')
adc_predict = adc.predict(X_val)
print(classification_report(y_val, adc_predict))
cnf_matrix = (confusion_matrix(y_val, adc_predict))
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=EMOTIONS,
normalize=True,
title='Normalized confusion matrix AB')
plt.show()
def preprocess_cnn_data():
data = pd.read_csv(path)
pixel_list = data['pixels'].tolist()
emotions = data['emotion'].tolist()
faces = []
for pixels in pixel_list:
face = string_to_nparray(pixels)
face = face.reshape(48, 48)
face = face / float(255)
faces.append(face)
# # to flip the image (commenting out since no significant increase in accuracy).
# for pixels in pixel_list:
# face = string_to_nparray(pixels)
# face = face.reshape(48, 48)
# face = face / float(255)
# faces.append(cv2.flip(face, 1))
# emotions = emotions + emotions
save_pck(faces, './cnn_data/X')
save_pck(emotions, './cnn_data/y')