def KmeansForAgeEst2(db, where, users, n_clusters):
X = []
X_users = []
centers = []
est = []
est_v = []
for at in where:
_users = [users[i] for i in at]
X.append(pymongo_utill.toTimeFreq(db, _users))
X_users.append(_users)
for c, x in enumerate(X):
km = KMeans(init='k-means++', n_clusters=n_clusters, n_init=10)
km.fit(x)
centers.append(km.cluster_centers_)
max_0 = 0
max_1 = 0
est_0_v = ""
est_1_v = ""
for i, u in enumerate(x):
sim = pairwise_kernels(km.cluster_centers_, u, metric="cosine")
if max_0 < sim[0]:
est_0 = X_users[c][i]
max_0 = sim[0]
est_0_v = u
if max_1 < sim[1]:
est_1 = X_users[c][i]
max_1 = sim[1]
est_1_v = u
est.append((est_0, est_1))
est_v.append((est_0_v, est_1_v))
return centers
def ageEstimation():
conn = pymongo_utill.getConnectionToMongoDB()
db = conn['TwitterInsert2']
#feature_vectors, labels, screen_names = pymongo_utill.byTimeFreq(db, sample=225)
screen_names, labels = pymongo_utill.loadUsers(db, sample=1256)
#screen_names, labels = pymongo_utill.loadUsers(db, sample=50)
conn.disconnect()
skf = cross_validation.StratifiedKFold(labels, n_folds=5, shuffle=True, random_state=100)
score = []
precision = [0, 0]
recall = [0, 0]
F_score = [0, 0]
for train, test in skf:
vectorizer = WordVectorizer()
X_w = []
X = []
p_lab = []
"""
screen_names_tr = [screen_names[i] for i in train]
selector = SelectKBest(score_func=chi2, k=16000)
for screen_name in screen_names_tr:
tweets = pymongo_utill.getUsersTweets(db, [screen_name], sample=100)
vectorizer.fit(tweets)
vectorizer.sort_voc()
for screen_name in screen_names:
tweets = pymongo_utill.getUsersTweets(db, [screen_name], sample=100)
X_w.append(vectorizer.transform(tweets)[0])
"""
X_t = pymongo_utill.toTimeFreq(db, screen_names)
#X_w = np.array(X_w)
#selector.fit(X_w[train], labels[train])
#X_w = selector.transform(X_w)
"""
for w, t in zip(X_w, X_t):
X.append(np.append(w,t))
"""
X = np.array(X_t)
svr = SVC(kernel="linear", C=100)
svr.fit(X=X[train], y=labels[train])
score.append(svr.score(X=X[test], y=labels[test]))
p_lab = svr.predict(X[test])
scores = precision_recall_fscore_support(labels[test], p_lab)
precision = [a+b for a, b in zip(precision, scores[0])]
recall = [a+b for a, b in zip(recall, scores[1])]
F_score = [a+b for a, b in zip(F_score, scores[2])]
score = np.array(score)
print('-' * 76)
print("Cross-Validation scores:%s" % score)
print("Mean Score:%s" % np.mean(score))
print("Mean Precision:%s" % [float(precision[0])/5, float(precision[1])/5])
print("Mean recall:%s" % [float(recall[0])/5, float(recall[1])/5])
print("Mean F_score:%s" % [float(F_score[0])/5, float(F_score[1])/5])
print('-' * 76)
def KmeansForAgeEst(db, where, users, n_clusters):
X = []
map = []
cor_k = []
for at in where:
_users = [users[i] for i in at]
X.append(pymongo_utill.toTimeFreq(db, _users))
for i, x in enumerate(X):
km = KMeans(init='k-means++', n_clusters=n_clusters, n_init=10)
km.fit(x)
map = [i]*len(x)
cor_k += [tmp+(i*n_clusters) for tmp in km.predict(x)]
return cor_k, map
def ageEstimationByCluser(file):
conn = pymongo_utill.getConnectionToMongoDB()
db = conn['TwitterInsert2']
screen_names, labels = pymongo_utill.loadUsers(db, sample=1254)
#screen_names, labels = pymongo_utill.loadUsers(db, sample=50)
skf = cross_validation.StratifiedKFold(labels, n_folds=5, shuffle=True, random_state=100)
score = []
precision = [0, 0]
recall = [0, 0]
F_score = [0, 0]
error_svm = []
error_proposed_msd = []
error_both = []
for train, test in skf:
screen_names_tr = [screen_names[i] for i in train]
vectorizer = WordVectorizer()
selector = SelectKBest(score_func=chi2, k=16000)
for screen_name in screen_names_tr:
tweets = pymongo_utill.getUsersTweets(db, [screen_name], sample=100)
vectorizer.fit(tweets)
vectorizer.sort_voc()
X_w = []
for screen_name in screen_names:
tweets = pymongo_utill.getUsersTweets(db, [screen_name], sample=100)
X_w.append(vectorizer.transform(tweets)[0])
X_w = np.array(X_w)
X_w_t = selector.fit_transform(X_w[train], labels[train])
X_w_ts = selector.transform(X_w[test])
#X_w = selector.fit_transform(X_w, labels)
X_t = pymongo_utill.toTimeFreq(db, screen_names)
where = []
for threshold in [0, 1]:
where.append(np.argwhere(labels[train] == threshold))
n_clusters = 3
centers = clustering.KmeansForAgeEst2(db, where, screen_names_tr, n_clusters)
svr = SVC(probability=True, kernel="linear", C=100)
svr.fit(X_w_t, labels[train])
"""
for w, t in zip(X_t, X_w):
X.append(np.append(w,t))
"""
X = []
for w, t in zip(X_w_ts, X_t[test]):
X.append((w, t))
X = np.array(X)
right = 0
indetable = 0
screen_names_ts = [screen_names[i] for i in test]
p_lab = []
centers = [c for center in centers
for c in center]
for i, ts in enumerate(X):
w, t = ts
V_sim = pairwise_kernels(centers, t, metric="chi2")
V_sim = [sim/sum(V_sim) for sim in V_sim]
prd_pro0 = svr.predict_proba(w)[0][0]
prd_pro1 = svr.predict_proba(w)[0][1]
if max(V_sim[:n_clusters]) * prd_pro0 > max(V_sim[n_clusters:]) * prd_pro1:
predic = 0
elif max(V_sim[:n_clusters]) * prd_pro0 < max(V_sim[n_clusters:]) * prd_pro1:
predic = 1
else:
indetable += 1
p_lab.append(predic)
if predic == labels[test][i]:
right += 1
if prd_pro0 > prd_pro1 and labels[test][i] == 1:
error_svm.append(screen_names_ts[i])
if prd_pro0 < prd_pro1 and labels[test][i] == 0:
error_svm.append(screen_names_ts[i])
else:
if prd_pro0 < prd_pro1 and labels[test][i] == 1:
error_proposed_msd.append(screen_names_ts[i])
elif prd_pro0 > prd_pro1 and labels[test][i] == 0:
error_proposed_msd.append(screen_names_ts[i])
else:
error_both.append(screen_names_ts[i])
scores = precision_recall_fscore_support(labels[test], p_lab)
precision = [a+b for a, b in zip(precision, scores[0])]
recall = [a+b for a, b in zip(recall, scores[1])]
F_score = [a+b for a, b in zip(F_score, scores[2])]
score.append(float(right)/len(X))
for name in error_svm:
file.write("error_svm:"+name+'\n')
for name in error_proposed_msd:
file.write("error_propsed_msd:"+name+"\n")
for name in error_both:
file.write("error_both:"+name+"\n")
score = np.array(score)
print('-' * 76)
print("Cross-Validation scores:%s" % score)
print("Mean Score:%s" % np.mean(score))
print("Mean Precision:%s" % [float(precision[0])/5, float(precision[1])/5])
print("Mean recall:%s" % [float(recall[0])/5, float(recall[1])/5])
print("Mean F_score:%s" % [float(F_score[0])/5, float(F_score[1])/5])
print('-' * 76)
