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test.py
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test.py
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#-*- coding: utf-8 -*-
__author__ = 'k148582'
import pymongo as pm
import pymongo_utill, time , warnings, os, errno, csv
import numpy as np
import numpy.ma as ma
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report, precision_recall_fscore_support
from sklearn.svm import SVC
from sklearn.feature_selection import SelectKBest, chi2
from feature_extraction import WordVectorizer
from sklearn.cross_validation import StratifiedKFold, StratifiedShuffleSplit
import matplotlib.pyplot as plt
from libsvm import svm, svmutil
from sklearn.decomposition import PCA
def self_training(X, y, X_unLabeled, clf, th):
clf.fit(X=X, y=y)
index_unlabeled = ma.arange(0, len(X_unLabeled), 1)
y_unlabeled = np.zeros(len(X_unLabeled))
train_is_failed = False
while True:
probs = clf.predict_proba(X=X_unLabeled[~ma.getmaskarray(index_unlabeled)])
index_greater_equal = np.greater_equal([max(d) for d in probs], [th]*len(probs))
index_labelable = index_unlabeled.data[~ma.getmaskarray(index_unlabeled)][index_greater_equal]
if not len(index_labelable) > 0:
if not len(index_unlabeled.data[ma.getmaskarray(index_unlabeled)]) > 0:
train_is_failed = True
break
index_unlabeled[index_labelable] = ma.masked
if index_unlabeled.all() is ma.masked:
break
y_unlabeled[index_labelable] = [np.argmax(p) for p in probs[index_greater_equal]]
X_labelable = X_unLabeled[index_unlabeled.mask]
y_labelable = y_unlabeled[index_unlabeled.mask]
clf.fit(X=np.append(X, X_labelable, axis=0),
y=np.append(y, y_labelable))
if train_is_failed:
y_unlabeled = []
else:
y_unlabeled = ma.array(data=y_unlabeled, mask=index_unlabeled.mask)
return clf, y_unlabeled
def self_training2(X, y, X_unLabeled, param, th):
model = svmutil.svm_train(svmutil.svm_problem(x=X.tolist(), y=y.tolist()), param)
obj = model.get_objective_value()[0]
itr_num = 0
while True:
predicted_labels = np.array(svmutil.svm_predict(x=X_unLabeled.tolist(),
y=[1]*len(X_unLabeled),
m=model,
options="-q")[0])
model = svmutil.svm_train(svmutil.svm_problem(x=np.append(X, X_unLabeled, axis=0).tolist(),
y=np.append(y, predicted_labels).tolist()), param)
obj_new = model.get_objective_value()[0]
itr_num += 1
if abs(obj_new - obj) < th:
break
else:
obj = obj_new
y_unlabeled = ma.array(data=np.array(svmutil.svm_predict(x=X_unLabeled.tolist(),
y=[1]*len(X_unLabeled),
m=model,
options="-q")[0]),
mask=[True]*len(X_unLabeled))
return model, y_unlabeled, obj_new, itr_num
def get_scores(y_pred, y_true):
scores = precision_recall_fscore_support(y_true=y_true, y_pred=y_pred, labels=[0,1])
average = precision_recall_fscore_support(y_true=y_true,
y_pred=y_pred,
average="macro",
pos_label=None,
labels=[0, 1])
return scores, average
def plot_decision_boundary(clf, X, y, target_names, plt, h=.002):
plt.clf()
x_min, x_max = X[:, 0].min() - 0.01, X[:, 0].max() + 0.01
y_min, y_max = X[:, 1].min() - 0.01, X[:, 1].max() + 0.01
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
for c, i, target_name in zip("rgb", [0, 1], target_names):
plt.scatter(X[y == i, 0], X[y == i, 1], c=c, label=target_name)
plt.legend()
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
return plt
def log_self_training(y_pred, y_true, num_itr, wr):
scores, average = get_scores(y_pred=y_pred, y_true=y_true)
wr.writerow({
'num_itr': num_itr,
'num_under30': (y_pred == 0).sum(),
'num_over30': (y_pred == 1).sum(),
'precision_under30': scores[0][0],
'precision_over30': scores[0][1],
'recall_under30': scores[1][0],
'recall_over30': scores[1][1],
'F_score_under30': scores[2][0],
'F_score_over30': scores[2][1],
'support_under30': scores[3][0],
'support_over30': scores[3][1],
'precision_mean': average[0],
'recall_mean': average[1],
'F_score_mean': average[2]})
def self_training3(num_fold, X, y, clf, X_tr, y_tr, X_tes, y_tes, th, target_names, plt, path, num_train):
index_unlabeled = ma.arange(0, len(X), 1)
y_unlabeled = np.zeros(len(X))
train_is_failed = False
num_itr = 0
path = path + "/decision_boundary_fold/" + "train_" + str(num_train) + "/" + "fold_" + str(num_fold) + \
"/threshold_" + str(th)
column_titles = ['num_itr', 'num_under30', 'num_over30',
'precision_under30', 'precision_over30', 'recall_under30', 'recall_over30',
'F_score_under30', 'F_score_over30', 'support_under30', 'support_over30',
'precision_mean', 'recall_mean', 'F_score_mean']
clf.fit(X=X_tr, y=y_tr)
plt = plot_decision_boundary(clf, X_tes, y_tes, target_names, plt)
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
else:
print("\nBE CAREFUL! Directory %s already exists." % path)
plt.savefig(path+'/num_itr_'+str(num_itr)+".pdf", bbox_inches='tight')
labeling_f = open(path+"/labeling.csv", "wt")
semi_svm_f = open(path+"/semi_svm.csv", "wt")
labeling_w = csv.DictWriter(labeling_f, fieldnames=column_titles)
semi_svm_w = csv.DictWriter(semi_svm_f, fieldnames=column_titles)
labeling_w.writerow(dict((n, n) for n in column_titles))
semi_svm_w.writerow(dict((n, n) for n in column_titles))
num_itr += 1
while True:
probs = clf.predict_proba(X=X[~ma.getmaskarray(index_unlabeled)])
index_greater_equal = np.greater_equal([max(d) for d in probs], [th]*len(probs))
index_labelable = index_unlabeled.data[~ma.getmaskarray(index_unlabeled)][index_greater_equal]
if not len(index_labelable) > 0:
if not len(index_unlabeled.data[ma.getmaskarray(index_unlabeled)]) > 0:
train_is_failed = True
break
index_unlabeled[index_labelable] = ma.masked
y_unlabeled[index_labelable] = [np.argmax(p) for p in probs[index_greater_equal]]
X_labelable = X[index_unlabeled.mask]
y_labelable = y_unlabeled[index_unlabeled.mask]
clf.fit(X=np.append(X_tr, X_labelable, axis=0),
y=np.append(y_tr, y_labelable))
log_self_training(y_unlabeled[index_labelable], y[index_labelable], num_itr, labeling_w)
log_self_training(clf.predict(X=X_tes), y_tes, num_itr, semi_svm_w)
plt = plot_decision_boundary(clf, X_tes, y_tes, target_names, plt)
plt.savefig(path+'/num_itr_'+str(num_itr)+".pdf", bbox_inches='tight')
num_itr += 1
if index_unlabeled.all() is ma.masked:
break
if train_is_failed:
y_unlabeled = []
else:
y_unlabeled = ma.array(data=y_unlabeled, mask=index_unlabeled.mask)
labeling_f.close()
return clf, y_unlabeled
if __name__ == '__main__':
"""
star_time = time.time()
user_sequence = pymongo_utill.TwitterUserSequence()
ths = [0.1, 1, 10, 100, 1000]
train_size = [26, 50, 76, 156, 256, 376, 450]
#train_size = [26, 30]
test_size = 480
unlabeled_size = 1000
#train_size = [290, 376, 450, 526]
ths = [0.2, 0.5]
train_size = [20, 10]
unlabeled_size = 30
test_size = 10
query = [{"$and": [{"age": {"$lt": 30}},
{"age": {"$gte": 13}}], "num": 1200},
{"$and": [{"age": {"$lt": 80}},
{"age": {"$gte": 30}}], "num": 1200}]
user_sequence.make_user_sequence(query=query)
selector = SelectKBest(chi2, k=185)
path = "experiment_11_24"
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
else:
print("\nBE CAREFUL! Directory %s already exists." % path)
svm_f = open(path+"/svm.csv", "wt")
semi_svm_f = open(path+"/semi_svm.csv", "wt")
labeling_f = open(path+"/labeling.csv", "wt")
column_titles = ['fold', 'num_train', 'num_unlabeled', 'num_test',
'precision_under30', 'precision_over30', 'recall_under30', 'recall_over30',
'F_score_under30', 'F_score_over30', 'support_under30', 'support_over30',
'precision_mean', 'recall_mean', 'F_score_mean']
svm_w = csv.DictWriter(svm_f, fieldnames=column_titles)
semi_svm_w = csv.DictWriter(semi_svm_f, fieldnames=column_titles + ['threshold', 'obj_value', 'itr_num'])
labeling_w = csv.DictWriter(labeling_f, fieldnames=column_titles + ['threshold', 'num_labeled'])
svm_w.writerow(dict((n, n) for n in column_titles))
semi_svm_w.writerow(dict((n, n) for n in column_titles + ['threshold', 'obj_value', 'itr_num']))
labeling_w.writerow(dict((n, n) for n in column_titles + ['threshold', 'num_labeled']))
for size in train_size:
ssf = StratifiedShuffleSplit(y=user_sequence.labels, n_iter=5, random_state=10, test_size=test_size)
for i, (index, test_index) in enumerate(ssf):
train_index, unLabeled_index = StratifiedShuffleSplit(y=user_sequence.labels[index],
test_size=unlabeled_size,
train_size=size,
random_state=10)._iter_indices().__next__()
user_sequence.set_word_set(index=train_index)
feature_vectors = user_sequence.transform()
selector.fit(X=feature_vectors[train_index],
y=user_sequence.labels[train_index])
feature_vectors = selector.transform(X=feature_vectors)
svm_model = svmutil.svm_train(svmutil.svm_problem(x=feature_vectors[train_index].tolist(),
y=user_sequence.labels[train_index].tolist()),
svmutil.svm_parameter("-s 0 -t 0 -c 0.1 -q"))
scores, average = get_scores(y_pred=np.array(svmutil.svm_predict(x=feature_vectors[test_index].tolist(),
y=user_sequence.labels[test_index].tolist(),
m=svm_model,
options="-q")[0]),
y_true=user_sequence.labels[test_index])
svm_w.writerow({'fold': i,
'num_train': len(train_index),
'num_unlabeled': len(unLabeled_index),
'num_test': len(test_index),
'precision_under30': scores[0][0],
'precision_over30': scores[0][1],
'recall_under30': scores[1][0],
'recall_over30': scores[1][1],
'F_score_under30': scores[2][0],
'F_score_over30': scores[2][1],
'support_under30': scores[3][0],
'support_over30': scores[3][1],
'precision_mean': average[0],
'recall_mean': average[1],
'F_score_mean': average[2]})
for th in ths:
semi_svm, y_unlabeled, obj_new, itr_num = self_training2(X=feature_vectors[train_index],
y=user_sequence.labels[train_index],
X_unLabeled=feature_vectors[unLabeled_index],
param=svmutil.svm_parameter("-s 0 -t 0 -c 0.1 -q"),
th=th)
if len(y_unlabeled) == 0:
continue
scores, average = get_scores(y_pred=np.array(svmutil.svm_predict(x=feature_vectors[test_index].tolist(),
y=user_sequence.labels[test_index].tolist(),
m=semi_svm,
options="-q")[0]),
y_true=user_sequence.labels[test_index])
semi_svm_w.writerow({'fold': i,
'num_train': len(train_index),
'num_unlabeled': len(unLabeled_index),
'num_test': len(test_index),
'precision_under30': scores[0][0],
'precision_over30': scores[0][1],
'recall_under30': scores[1][0],
'recall_over30': scores[1][1],
'F_score_under30': scores[2][0],
'F_score_over30': scores[2][1],
'support_under30': scores[3][0],
'support_over30': scores[3][1],
'precision_mean': average[0],
'recall_mean': average[1],
'F_score_mean': average[2],
'obj_value': obj_new,
'itr_num': itr_num,
'threshold': th})
scores, average = get_scores(y_pred=y_unlabeled.data[y_unlabeled.mask],
y_true=user_sequence.labels[unLabeled_index][y_unlabeled.mask])
labeling_w.writerow({'fold': i,
'num_train': len(train_index),
'num_unlabeled': len(unLabeled_index),
'num_test': len(test_index),
'precision_under30': scores[0][0],
'precision_over30': scores[0][1],
'recall_under30': scores[1][0],
'recall_over30': scores[1][1],
'F_score_under30': scores[2][0],
'F_score_over30': scores[2][1],
'support_under30': scores[3][0],
'support_over30': scores[3][1],
'precision_mean': average[0],
'recall_mean': average[1],
'F_score_mean': average[2],
'threshold': th,
'num_labeled': len(y_unlabeled.data[y_unlabeled.mask])})
svm_f.close()
semi_svm_f.close()
labeling_f.close()
print("--- %s seconds ---" % (time.time() - star_time))
"""
star_time = time.time()
user_sequence = pymongo_utill.TwitterUserSequence()
#ths = [0.7, 0.75, 0.8, 0.85, 0.9, 0.95]
ths = [0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
#train_size = [26, 50, 76, 156, 256, 290, 376]
train_size = [4, 10]
#train_size = [26, 30]
test_size = 240
unlabeled_size = 1200
#train_size = [290, 376, 450, 526]
"""
ths = [0.7, 0.9]
train_size = [20, 30]
unlabeled_size = 30
test_size = 30
"""
query = [{"$and": [{"age": {"$lt": 30}},
{"age": {"$gte": 13}}], "num": 1200},
{"$and": [{"age": {"$lt": 80}},
{"age": {"$gte": 30}}], "num": 1200}]
user_sequence.make_user_sequence(query=query)
selector = SelectKBest(chi2, k=185)
#pca = PCA(n_components=2)
svm = SVC(kernel='linear', probability=True, C=1000)
path = "experiment_12_3"
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
else:
print("\nBE CAREFUL! Directory %s already exists." % path)
svm_f = open(path+"/svm.csv", "wt")
semi_svm_f = open(path+"/semi_svm.csv", "wt")
labeling_f = open(path+"/labeling.csv", "wt")
column_titles = ['fold', 'num_train', 'num_unlabeled', 'num_test',
'precision_under30', 'precision_over30', 'recall_under30', 'recall_over30',
'F_score_under30', 'F_score_over30', 'support_under30', 'support_over30',
'precision_mean', 'recall_mean', 'F_score_mean']
svm_w = csv.DictWriter(svm_f, fieldnames=column_titles)
semi_svm_w = csv.DictWriter(semi_svm_f, fieldnames=column_titles + ['threshold'])
labeling_w = csv.DictWriter(labeling_f, fieldnames=column_titles + ['threshold', 'num_labeled', 'num_under30', 'num_over30'])
svm_w.writerow(dict((n, n) for n in column_titles))
semi_svm_w.writerow(dict((n, n) for n in column_titles + ['threshold']))
labeling_w.writerow(dict((n, n) for n in column_titles + ['threshold', 'num_labeled', 'num_under30', 'num_over30']))
for size in train_size:
ssf = StratifiedShuffleSplit(y=user_sequence.labels, n_iter=5, random_state=10, test_size=test_size)
for i, (index, test_index) in enumerate(ssf):
train_index, unLabeled_index = StratifiedShuffleSplit(y=user_sequence.labels[index],
test_size=unlabeled_size,
train_size=size,
random_state=10)._iter_indices().__next__()
user_sequence.set_word_set(index=train_index)
feature_vectors = user_sequence.transform()
feature_vectors = selector.fit(X=feature_vectors[train_index],
y=user_sequence.labels[train_index]).transform(X=feature_vectors)
#feature_vectors = pca.fit(X=feature_vectors[train_index]).transform(X=feature_vectors)
svm.fit(X=feature_vectors[train_index],
y=user_sequence.labels[train_index])
scores, average = get_scores(y_pred=svm.predict(X=feature_vectors[test_index]),
y_true=user_sequence.labels[test_index])
svm_w.writerow({'fold': i,
'num_train': len(train_index),
'num_unlabeled': len(unLabeled_index),
'num_test': len(test_index),
'precision_under30': scores[0][0],
'precision_over30': scores[0][1],
'recall_under30': scores[1][0],
'recall_over30': scores[1][1],
'F_score_under30': scores[2][0],
'F_score_over30': scores[2][1],
'support_under30': scores[3][0],
'support_over30': scores[3][1],
'precision_mean': average[0],
'recall_mean': average[1],
'F_score_mean': average[2]})
for th in ths:
semi_svm, y_unlabeled = self_training3(X=feature_vectors[unLabeled_index],
y=user_sequence.labels[unLabeled_index],
X_tr=feature_vectors[train_index],
y_tr=user_sequence.labels[train_index],
X_tes=feature_vectors[test_index],
y_tes=user_sequence.labels[test_index],
plt=plt,
path=path,
target_names=['under_30', 'over_30'],
clf=SVC(kernel='linear', probability=True, C=1000),
th=th,
num_fold=i,
num_train=size)
"""
semi_svm, y_unlabeled = self_training(X=feature_vectors[train_index],
y=user_sequence.labels[train_index],
X_unLabeled=feature_vectors[unLabeled_index],
clf=SVC(kernel='linear', probability=True, C=1000),
th=th)
"""
if len(y_unlabeled) == 0:
continue
scores, average = get_scores(y_pred=semi_svm.predict(X=feature_vectors[test_index]),
y_true=user_sequence.labels[test_index])
semi_svm_w.writerow({'fold': i,
'num_train': len(train_index),
'num_unlabeled': len(unLabeled_index),
'num_test': len(test_index),
'precision_under30': scores[0][0],
'precision_over30': scores[0][1],
'recall_under30': scores[1][0],
'recall_over30': scores[1][1],
'F_score_under30': scores[2][0],
'F_score_over30': scores[2][1],
'support_under30': scores[3][0],
'support_over30': scores[3][1],
'precision_mean': average[0],
'recall_mean': average[1],
'F_score_mean': average[2],
'threshold': th})
scores, average = get_scores(y_pred=y_unlabeled.data[y_unlabeled.mask],
y_true=user_sequence.labels[unLabeled_index][y_unlabeled.mask])
labeling_w.writerow({'fold': i,
'num_train': len(train_index),
'num_unlabeled': len(unLabeled_index),
'num_test': len(test_index),
'precision_under30': scores[0][0],
'precision_over30': scores[0][1],
'recall_under30': scores[1][0],
'recall_over30': scores[1][1],
'F_score_under30': scores[2][0],
'F_score_over30': scores[2][1],
'support_under30': scores[3][0],
'support_over30': scores[3][1],
'precision_mean': average[0],
'recall_mean': average[1],
'F_score_mean': average[2],
'threshold': th,
'num_under30': (y_unlabeled.data[y_unlabeled.mask] == 0).sum(),
'num_over30': (y_unlabeled.data[y_unlabeled.mask] == 1).sum(),
'num_labeled': len(y_unlabeled.data[y_unlabeled.mask])})
svm_f.close()
semi_svm_f.close()
labeling_f.close()
print("--- %s seconds ---" % (time.time() - star_time))