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AgeGender.py
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AgeGender.py
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import os
import pdb
import numpy as np
import glob as glob
from sklearn import svm
from sklearn.svm import LinearSVC
from sklearn import cross_validation
from sklearn import grid_search
from sklearn.preprocessing import StandardScaler
from sklearn.multiclass import OneVsRestClassifier
import cPickle as pickle
import matplotlib.pyplot as plt
def PCAembedding():
# project original data into lower dimensions
pass
def preprocess(X,y,test_size):
print "preprocessing..."
# preprocessing and Cross Validation
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=test_size)
return X_train,X_test,y_train,y_test
def GenderClassifier(data_feature_stack,data_gender_stack,test_size = 0.5,search = False):
genderX_train,genderX_test,genderY_train,genderY_test = preprocess(data_feature_stack,data_gender_stack,test_size)
print "fitting gender Clssfifer..."
"""grid search for best C"""
if search:
clf = svm.SVC(kernel = 'linear' )
parameters = {'kernel':['linear'], 'C':[0.0001,0.001,0.005]} #among 0.0001,0.001, 0.01, 0.1, 1,10,100, 0.001 is the best
cv_clf = grid_search.GridSearchCV(clf, parameters)
cv_clf.fit(genderX_train, genderY_train)
# np.mean(cross_validation.cross_val_score(cv_clf.fit(genderX_train).best_estimator_, genderX_train))
print "cv_clf.best_params_: ",cv_clf.best_params_
clf = cv_clf
else:
clf = svm.SVC(kernel = 'linear',C = 0.001)
clf.fit(genderX_train, genderY_train)
print "predicting gender..."
# gender_test_result = clf.predict(genderX_test)
# gender_train_result = clf.predict(genderX_train)
gender_acc_test = clf.score(genderX_test, genderY_test)
gender_acc_train = clf.score(genderX_train, genderY_train)
pdb.set_trace()
#cross validation
# scores = cross_validation.cross_val_score(clf, data_feature_stack, data_gender_stack, cv=5)
return clf, gender_acc_test,gender_acc_train
def AgeClassifier(data_feature_stack,data_age_stack,test_size = 0.5):
Age_range = np.unique(data_age_stack)
# 923, 1529, 856, 1617, 13836, 6260, 1198
AgeX_train,AgeX_test,AgeY_train,AgeY_test = preprocess(data_feature_stack,data_age_stack,test_size)
print "fitting Age Clssfifer..."
# parameters = (C=1.0, class_weight=None, dual=True, fit_intercept=True,\
# intercept_scaling=1, loss='l2', multi_class='ovr', penalty='l2',\
# random_state=0, tol=0.0001, verbose=0)
clf = OneVsRestClassifier(LinearSVC(C = 0.001)).fit(AgeX_train, AgeY_train)
print "predicting Age..."
Age_test_result = clf.predict(AgeX_test)
Age_train_result = clf.predict(AgeX_train)
# Age_acc_test = clf.score(AgeX_test, AgeY_test)
# Age_acc_train = clf.score(AgeX_train, AgeY_train)
Age_acc_test = np.sum(Age_test_result == AgeY_test)
Age_acc_train = np.sum(Age_train_result == AgeY_train)
temp = Age_test_result-AgeY_test
error = np.sqrt(temp**2)
rmse = np.mean(error)
error2 = np.sqrt(temp[temp!=0]**2)
rmse2 = np.mean(error2)
pdb.set_trace()
return clf, Age_acc_test,Age_acc_train
if __name__ == '__main__':
files = sorted(glob.glob('../data/'+'*_file*'))
dataFromFile = {}
data_face = []
data_feature = []
data_age = []
data_gender = []
for ii in range(len(files)):
dataFromFile[ii] = pickle.load( open( files[ii], "rb" ) )
data = dataFromFile[ii]
data_face.append(np.array(data['face']))
data_feature.append(data['feature'])
data_age.append(data['age'])
data_gender.append(data['sex'])
data_face_stack = np.array(data_face[0])
data_feature_stack = np.array(data_feature[0])
data_age_stack = np.array(data_age[0])
data_gender_stack = np.array(data_gender[0])
for kk in range(len(files)-1):
data_face_stack = np.concatenate((data_face_stack,np.array(data_face[kk+1])), axis=0)
data_feature_stack = np.concatenate((data_feature_stack,np.array(data_feature[kk+1])), axis=0)
data_age_stack = np.concatenate((data_age_stack,np.array(data_age[kk+1])), axis=0)
data_gender_stack = np.concatenate((data_gender_stack,np.array(data_gender[kk+1])), axis=0)
numSample = data_face_stack.shape[0]
"""Gender Clssfifer"""
gender_clf, gender_acc_test,gender_acc_train = GenderClassifier(data_feature_stack,data_gender_stack,test_size =0.6)
"""Age Clssfifer"""
Age_clf, Age_acc_test,Age_acc_train = AgeClassifier(data_feature_stack,data_age_stack,test_size = 0.2)
"""
# random shuffle to get training and testing sets
index_data = range(numSample)
np.random.shuffle(index_data)
index_training = index_data[:1*numSample/4]
index_testing = index_data[3*numSample/4:]
feature_training = data_feature_stack[index_training,:]
sex_training = data_sex_stack[index_training]
feature_testing = data_feature_stack[index_testing,:]
sex_testing = data_sex_stack[index_testing]
"""