def train(self, sentences, labels, cross_validation = False):
x = []
y = []
for i in range(0, len(sentences)):
sentence = sentences[i]
prev = []
j = 0
for word in sentence:
body = word.lower()
featurespace = self._construct_featurespace(body, prev)
prev.append((body, labels[i][j]))
if len(prev) > self.chain_len:
del(prev[0])
x.append(featurespace.featureset)
j += 1
y.extend(labels[i])
prob = svm.problem(y, x)
if cross_validation:
param = svm.parameter('-c 1 -v 4 -s 4')
svm.train(prob, param)
else:
param = svm.parameter('-c 1 -s 4')
self._svm_model = svm.train(prob, param)
def solve(train_X, train_Y, test_X, test_Y):
best_lambda, test_accuracy = train(train_X, train_Y, train_X, train_Y)
print("Answer for problem 16 is {0}".format(best_lambda))
best_lambda, test_accuracy = train(train_X, train_Y, test_X, test_Y)
print("Answer for problem 17 is {0}".format(best_lambda))
train_x, val_x, train_y, val_y = split_data(train_X, train_Y, 120, 200)
best_lambda, test_accuracy = train(train_x, train_y, val_x, val_y)
print("best lambda is: {0}".format(best_lambda))
model = liblinearutil.train(train_y, train_x, '-s 0 -c 50 -e 0.000001')
label, accuracy, value = liblinearutil.predict(test_Y, test_X, model)
print("Answer for problem 18 is {0}".format((100 - accuracy[0]) / 100))
model = liblinearutil.train(train_Y, train_X, '-s 0 -c 50 -e 0.000001')
label, accuracy, value = liblinearutil.predict(test_Y, test_X, model)
print("Answer for problem 19 is {0}".format((100 - accuracy[0]) / 100))
accuracy = []
for i in range(5):
train_x, val_x, train_y, val_y = split_data(train_X, train_Y, 40 * i, 40 * (i + 1))
best_lambda, test_accuracy = train(train_x, train_y, val_x, val_y)
accuracy.append(test_accuracy)
mean_accuracy = np.mean(accuracy, axis=0)
print("Answer for problem 20 is {0}".format(min(mean_accuracy)))
def train(self, sentences, labels, cross_validation=False):
x = []
y = []
for i in range(0, len(sentences)):
sentence = sentences[i]
prev = []
j = 0
for word in sentence:
body = word.lower()
featurespace = self._construct_featurespace(body, prev)
prev.append((body, labels[i][j]))
if len(prev) > self.chain_len:
del (prev[0])
x.append(featurespace.featureset)
j += 1
y.extend(labels[i])
prob = svm.problem(y, x)
if cross_validation:
param = svm.parameter('-c 1 -v 4 -s 4')
svm.train(prob, param)
else:
param = svm.parameter('-c 1 -s 4')
self._svm_model = svm.train(prob, param)
def _complete_training(self, debug=False):
""" Forward data to external training and extract classifier information
"""
if self.str_label_function is not None:
self.label_function = eval(self.str_label_function)
self.labels = self.label_function()
options = "-c %.42f -e %.42f -s %d -B %d" % \
(self.complexity, self.tolerance, self.alg_num, self.offset)
for i, w in enumerate(self.weight):
options += " -w%d %.42f" % (i, w)
if not self.debug:
options += " -q"
self._log("Liblinear is now quiet!")
import liblinearutil
param = liblinearutil.parameter(options)
problem = liblinearutil.problem(self.labels, self.samples)
model = liblinearutil.train(problem, param)
self.calculate_classification_vector(model)
if self.debug:
print self.print_w
print self.b
def train_liblinear(args):
model_name, gold_dir, dirs = args[0], args[1], args[2:]
vectors, predicates = get_data(gold_dir, dirs)
prob = problem(map(num_to_class, predicates), vectors)
param = parameter('-s 0')
model = train(prob, param)
save_model(model_name, model)
def train_liblinear(args):
model_name, gold_dir, dirs = args[0], args[1], args[2:]
vectors, predicates = get_data(gold_dir, dirs)
prob = problem(map(num_to_class, predicates), vectors)
param = parameter('-s 0')
model = train(prob, param)
save_model(model_name, model)
def tuneParameters(weightString="-w-1 1.0 -w1 1.0 ", trainFile='models/type2_fc_10_11.train',
CList=[0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0], BList=[0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0], v=10):
labels = []
features = []
bestAccuracy = 0
bestParams = None
with open(trainFile) as f:
for line in f:
if line == '':
continue
data = line.rstrip().split()
labels.append(float(data[0]))
data = [float(x.split(':')[1]) for x in data[1:]]
features.append(data)
for C in CList:
for B in BList:
myOptions = weightString + "-s 0 -e 0.000001 -c " + str(C) + " -B " + str(B) + " -v " + str(v)
accuracy = liblinearutil.train(labels, features, myOptions)
print "C: " + str(C) + " B: " + str(B) + " " + str(accuracy)
if accuracy > bestAccuracy:
bestAccuracy = accuracy
bestParams = (C,B)
return bestAccuracy, bestParams[0], bestParams[1]
def _complete_training(self, debug=False):
""" Forward data to external training and extract classifier information
"""
if self.str_label_function is not None:
self.label_function = eval(self.str_label_function)
self.labels = self.label_function()
options = "-c %.42f -e %.42f -s %d -B %d" % \
(self.complexity, self.tolerance, self.alg_num, self.offset)
for i,w in enumerate(self.weight):
options += " -w%d %.42f" % (i, w)
if not self.debug:
options += " -q"
self._log("Liblinear is now quiet!")
import liblinearutil
param = liblinearutil.parameter(options)
problem = liblinearutil.problem(self.labels, self.samples)
model = liblinearutil.train(problem, param)
self.calculate_classification_vector(model)
if self.debug:
print self.print_w
print self.b
def _train(self, folder):
# train classifier on all .txt files in <folder>
entities = dict()
files = filter(lambda x: x.endswith('.txt'), os.listdir(folder))
for f in files:
# assign number to current language
print('LogR adds file ' + f)
for tweet in LogR._read_from_file(os.path.join(folder, f)):
# get features
features = self._extract_features(tweet)
# push out twit into training set
ind = LogR._get_dict_and_update(self.languages, f[:-4])
entities[ind] = entities.get(ind, []) + [features]
target = []
objects = []
for lang in entities.keys():
for obj in entities[lang]:
target.append(lang)
objects.append(obj)
print('LogR started training...')
self.model = liblinearutil.train(target, objects, '-s 0')
print('Finished!')
def best_C(self,x,y):
"""
training using y=list,x=dict
parameter = string of parameters
searches for best C
"""
prob=lu.problem(y,x)
para=""
para+= "-s 2 -C -B %f -p %f -e %f" % (self.bias,
self.p,
self.eps)
print para
para1=lu.parameter(para)
self.model=lu.train(prob,para1)
best_C, best_rate = lu.train(y, x, para)
return best_C, best_rate
def build(self, images, targets, extra):
shapes, mean_shape, i_stage = extra
n_landmarks = mean_shape.n_points
feature_extractor = self.feature_extractor_builder.build(
images, shapes, targets, (mean_shape, i_stage))
print("Extracting local binary features for each image.\n")
features = [
list(feature_extractor.apply(images[i], shapes[i]))
for i in xrange(len(images))
]
print("Features extracted.\n")
w = np.zeros(shape=(2 * n_landmarks, len(features[0])))
for lmark in xrange(2 * n_landmarks):
print_dynamic(
"Learning linear regression coefficients for landmark coordinate {}/{}.\n"
.format(lmark, 2 * n_landmarks))
linreg = liblinearutil.train(
list(targets[:, lmark]), features,
"-s 12 -p 0 -c {}".format(1 / float(len(features))))
w_list = linreg.get_decfun()[0]
w[lmark][0:len(w_list)] = w_list
return GlobalRegression(feature_extractor, w, mean_shape)
def train(self, data_train, data_dev):
"""Trains Minitagger on the given data."""
start_time = time.time()
assert self.__feature_extractor.is_training # Assert untrained
# Extract features (only labeled instances) and pass them to liblinear.
[label_list, features_list, _] = \
self.__feature_extractor.extract_features(data_train, False, [])
if not self.quiet:
print("{0} labeled instances (out of {1})".format(
len(label_list), data_train.num_instances))
print("{0} label types".format(len(data_train.label_count)))
print("{0} observation types".format(
len(data_train.observation_count)))
print("\"{0}\" feature template".format(
self.__feature_extractor.feature_template))
print("{0} feature types".format(
self.__feature_extractor.num_feature_types()))
problem = liblinearutil.problem(label_list, features_list)
self.__liblinear_model = \
liblinearutil.train(problem, liblinearutil.parameter("-q"))
self.__feature_extractor.is_training = False
if not self.quiet:
num_seconds = int(math.ceil(time.time() - start_time))
print("Training time: {0}".format(
str(datetime.timedelta(seconds=num_seconds))))
if data_dev is not None:
quiet_value = self.quiet
self.quiet = True
_, acc = self.predict(data_dev)
self.quiet = quiet_value
print("Dev accuracy: {0:.3f}%".format(acc))
def _lib_train_liblinear(user_tfidf, num_pos, num_neg, ignore):
param = parameter("-s 0")
sparse_user_tfidf, num_pos, num_neg = _convert_to_sparse_matrix(user_tfidf, num_pos, num_neg, ignore)
labels = ([1] * num_pos) + ([-1] * num_neg)
prob = problem(labels, sparse_user_tfidf)
modellog = train(prob, param)
return modellog
def svm(name):
'''Trains a logistic regression model on the feature extracted data.
Name is the data set name, e.g. who_won_1031.
'''
data = load_dataset(name)
return llb.train(data.Y, data.X, '-s 1')
def train(self, data_train, data_dev):
"""Trains Minitagger on the given data."""
start_time = time.time()
assert self.__feature_extractor.is_training # Assert untrained
# Extract features (only labeled instances) and pass them to liblinear.
[label_list, features_list, _] = \
self.__feature_extractor.extract_features(data_train, False, [])
if not self.quiet:
print("{0} labeled instances (out of {1})".format(
len(label_list), data_train.num_instances))
print("{0} label types".format(len(data_train.label_count)))
print("{0} observation types".format(
len(data_train.observation_count)))
print("\"{0}\" feature template".format(
self.__feature_extractor.feature_template))
print("{0} feature types".format(
self.__feature_extractor.num_feature_types()))
problem = liblinearutil.problem(label_list, features_list)
self.__liblinear_model = \
liblinearutil.train(problem, liblinearutil.parameter("-q"))
self.__feature_extractor.is_training = False
if not self.quiet:
num_seconds = int(math.ceil(time.time() - start_time))
print("Training time: {0}".format(
str(datetime.timedelta(seconds=num_seconds))))
if data_dev is not None:
quiet_value = self.quiet
self.quiet = True
_, acc = self.predict(data_dev)
self.quiet = quiet_value
print("Dev accuracy: {0:.3f}%".format(acc))
def train(self):
sys.stderr.write('creating training problem...')
prob = problem(self.labels, self.contexts)
sys.stderr.write('done\ntraining with option(s) "' + self.parameters +
'"...')
self.model = train(prob, parameter(self.parameters))
sys.stderr.write('done\n')
def LDA_SVM(matrix, test_matrix, n_authors, doc_authors, vocab, stopwords):
# set parameters
num_topics = 20
burn_in = 1000 # 0
alpha = 0.1
beta = 0.1
samples = 8
spacing = 100
num_test_docs = test_matrix.shape[0]
sampler = lda.LDA(num_topics, alpha, beta)
print('Starting!')
theta, phi, likelihood = sampler.train(matrix, burn_in, samples, spacing)
print('likelihood: ', likelihood)
theta_test, likelihood = sampler.classify(test_matrix, phi, burn_in,
samples, spacing)
print('likelihood: ', likelihood)
theta = theta / np.sum(theta, 1)[:, None]
theta_test = theta_test / np.sum(theta_test, 1)[:, None]
svm_model = ll.train(sum(doc_authors, []), theta.tolist(), '-c 4')
p_label, p_acc, p_val = ll.predict(np.random.rand(num_test_docs),
theta_test.tolist(), svm_model)
author_probs = np.zeros((n_test_docs, n_authors))
for doc, author in enumerate(p_label):
author_probs[doc, int(author)] = 1
return author_probs
def train(self, data_train, data_test):
"""
Trains Minitagger on the given train data. If test data is given, it reports the accuracy of the trained model
and the F1_score (macro average of f1_score of each label)
@type data_train: SequenceData
@param data_train: the training data set
@type data_test: SequenceData
@param data_test: the test data set
"""
# keep the training start timestamp
start_time = time.time()
assert (self.__feature_extractor.is_training
), "In order to train, is_training flag should be True"
# Extract features only for labeled instances from data_train
[label_list, features_list,
_] = self.__feature_extractor.extract_features(data_train, False, [])
# print some useful information about the data
if not self.quiet:
print("{0} labeled words (out of {1})".format(
len(label_list), data_train.num_of_words))
print("{0} label types".format(len(data_train.label_count)))
print("{0} word types".format(len(data_train.word_count)))
print("\"{0}\" feature template".format(
self.__feature_extractor.feature_template))
print("{0} feature types".format(
self.__feature_extractor.num_feature_types()))
# define problem to be trained using the parameters received from the feature_extractor
problem = liblinearutil.problem(label_list, features_list)
# train the model (-q stands for quiet = True in the liblinearutil)
self.__liblinear_model = liblinearutil.train(
problem, liblinearutil.parameter("-q"))
# training is done, set is_training to False, so that prediction can be done
self.__feature_extractor.is_training = False
# print some useful information
if not self.quiet:
num_seconds = int(math.ceil(time.time() - start_time))
# how much did the training last
print("Training time: {0}".format(
str(datetime.timedelta(seconds=num_seconds))))
# perform prediction on the data_test and report accuracy
if data_test is not None:
quiet_value = self.quiet
self.quiet = True
pred_labels, acc = self.predict(data_test)
self.quiet = quiet_value
self.__save_prediction_to_file(data_test, pred_labels)
f1score, precision, recall = report_fscore(self.prediction_path +
"/predictions.txt",
wikiner=self.wikiner)
print("Accuracy: ", acc)
# create some files useful for debugging
if self.debug:
self.__debug(data_test, pred_labels)
return f1score, precision, recall
def alternating_train(x, y, lands, c1, c2=1, params='-s 2 -B 1 -q'):
nb_views = lands.shape[2]
L = np.hstack([lands[:, :, v] for v in range(nb_views)])
M = np.hstack([x[:, :, v] for v in range(nb_views)])
l = len(lands)
m = len(x)
r0, mask = missing_lstsq(L, M)
s0 = np.dot(r0, L)
sample = s0.copy()
R = r0.copy()
y_list = y.tolist()
svm = liblin.train(y.tolist(), sample.tolist(),
'-c {} '.format(c1) + params)
it = 0
while True:
it += 1
res = minimize(r_obj_function,
R.flatten(),
args=(M, y, L, svm, mask, c1, c2),
options={'disp': True})
# for i in range(len(x)):
# r_i = minimize(r_obj_function, R[i, :], args=(i, M, y, L, svm, mask, c1, c2), options={'disp': False})
# R[i] = r_i.x
# cost += r_i.fun
print(r_i.fun)
R = res.x.reshape(m, l)
sample = np.dot(R, L)
svm = liblin.train(y.tolist(), sample.tolist(),
'-c {} '.format(c1) + params)
_, p_acc, _ = liblin.predict(y, sample.tolist(), svm, "-q")
print(p_acc)
if it == 1:
break
return svm
def train_liblinear(features, labels, C=1, s=1, folds=10, threads=4):
print('Training model...')
start = time.time()
model = liblinearutil.train(
labels, features,
'-c {0} -s {1} -v {2} -n {3}'.format(C, 2, folds, threads))
end = time.time()
print('Model trained in ' + str(end - start) + ' seconds')
return model
def train_regression(self, x, y):
data = []
for sample in x:
data.append(dict([(self._features.setId(d), sample[d]) for d in sample]))
self._regression = True
param = liblinear.parameter("-c 1 -s 0")
prob = liblinear.problem(y, data)
self._model = liblinear.train(prob, param)
def train(self):
y = list(self.training[0])
x = list(self.training[1])
y.append(-1)
x.append({1:1})
if len(x) <= 1:
message('unable to recommend because you have not read any feed.', 'Error', die=True)
return liblinearutil.train(y, x, '-q')
def train_regression(self, x, y):
data = []
for sample in x:
data.append(
dict([(self._features.setId(d), sample[d]) for d in sample]))
self._regression = True
param = liblinear.parameter('-c 1 -s 0')
prob = liblinear.problem(y, data)
self._model = liblinear.train(prob, param)
def rank_pooling(data):
#mean = time_varying_mean(data)
#non_linear_mean = non_linearity(mean)
normalized_data = normalize(data, axis=1, norm='l2')
total_frames = normalized_data.shape[0]
labels = list(range(1, total_frames + 1))
data = normalized_data.tolist()
model = train(labels, data, '-s 11 -q')
return np.array(model.get_decfun()[0])
def train_log_regr_liblinear(features, responses):
echo('Training with Liblinear')
prob = liblinearutil.problem(responses, features)
# -s 0: L2-regularized logistic regression (primal)
# -B 1: Fit a bias term
# -q: quiet mode
param = liblinearutil.parameter('-s 0 -B 1 -q')
return liblinearutil.train(prob, param)
def train_SVR_liblinear(features, responses):
echo('Training with Liblinear')
prob = liblinearutil.problem(responses, features)
# -s 11: L2-regularized L2-loss support vector regression (primal)
# -B 1: Fit a bias term
# -q: quiet mode
param = liblinearutil.parameter('-s 11 -B 1 -q')
return liblinearutil.train(prob, param)
def train( C, Y_train, X_train, x_lines ):
"""
This function takes in the training labels and features and creates a model and saves that model
:param C : list containing parameter C
:param X_train : training features
:param Y_train : training labels
:return None
"""
# for c in C:
param = '-s 2 -c ' + str(C)
model = lu.train(Y_train, X_train, param)
lu.save_model("model/lmods2_tamper" + str(round(C,2)) + "_" + str(x_lines) + "l.model", model)
def parallel_train_predict(args):
print("A process begins.")
x_train,y_train,x_test,y_test=args
problem = liblinearutil.problem(y_train, x_train)
parameter = liblinearutil.parameter('-s 0 -c 1')
time_start = time.clock()
model = liblinearutil.train(problem, parameter)
print("A process training finished in %f."%(time.clock()-time_start))
time_start = time.clock()
p_label, p_acc, p_val = liblinearutil.predict(y_test, x_test,model,'-b 0')
print("A process predicting finished in %f."%(time.clock()-time_start))
return p_val
def validation(k, data_x, data_y, s, e, C):
accuracies = []
params = get_params(s, e, C)
print('s = {}, e = {}, C = {}'.format(s, e, C))
for fold in range(k):
train_x, test_x = get_k_fold(k, fold, data_x)
train_y, test_y = get_k_fold(k, fold, data_y)
m = liblinearutil.train(train_y, train_x, params)
_, p_acc, __ = liblinearutil.predict(test_y, test_x, m)
accuracies.append(p_acc[0])
return accuracies
def train(self):
y = list(self.training[0])
x = list(self.training[1])
y.append(-1)
x.append({1: 1})
if len(x) <= 1:
message('unable to recommend because you have not read any feed.',
'Error',
die=True)
return liblinearutil.train(y, x, '-q')
def train(c, Y_train, X_train):
"""
This function takes in the training labels and features and creates a model and saves that model
:param C : list containing parameter C
:param X_train : training features
:param Y_train : training labels
:return None
"""
#for c in C:
param = '-s 2 -c ' + str(c)
model = lu.train(Y_train, X_train, param)
lu.save_model("model/lmods2_"+str(round(c,2))+".model", model)
def h_step(features, codes, verbose=True):
N, D = features.shape
models = []
for (y, i) in zip(codes.T, range(codes.shape[1])):
t_start = timeit.default_timer()
models.append(
liblinearutil.train(y.tolist(), features.tolist(),
str('-s 0 -c 4 -q')))
t_end = timeit.default_timer()
if verbose:
print('[H] {:3d}th bit, {:.4f} seconds elapsed'.format(
i, t_end - t_start))
return models
def train(train_data, features, c):
x = []
y = []
for key in train_data:
y.append(train_data[key]['class'])
x.append(features[key])
prob = liblinearutil.problem(y, x)
param = liblinearutil.parameter('-q -c ' + str(c) )
model = liblinearutil.train(prob, param)
return model
def train(self, x, y, biased=False):
data = []
for sample in x:
data.append(dict([(self._features.setId(d), sample[d]) for d in sample]))
labels = [self._labels.setId(C) for C in y]
if self._labels.count() == 2:
labels = [1 if label == 1 else -1 for label in labels]
param = liblinear.parameter("-c 1 -s 2 -q" + (" -B {0}".format(biased) if biased else ""))
else:
param = liblinear.parameter("-c 1 -s 4 -q" + (" -B {0}".format(biased) if biased else ""))
prob = liblinear.problem(labels, data)
self._model = liblinear.train(prob, param)
def unimodalPredDev(gs, feats, nDim):
parts = ['dev']
[cccs, preds] = [{} for i in range(2)]
for s in parts:
cccs[s] = -1.0
warnings.filterwarnings('ignore', category=ConvergenceWarning)
#Liblinear
for comp in v.C:
#Options for liblinear
options = "-s "+str(v.sVal)+" -c "+str(comp)+" -B 1 -q"
#We learn the model on train
model = train(gs['train'][nDim],feats['train'],options)
#We predict on data
for s in parts:
pred = np.array(predict(gs[s][nDim],feats[s],model,"-q"))[0]
#We calculate the correlation and store it
ccc = cccCalc(np.array(pred),gs[s][nDim])
if (ccc > cccs[s]):
preds[s] = pred
cccs[s] = ccc
function = "SVR"
alpha = comp
if (v.fullMode == True):
#We see if we can do better with sklearn
for nbFunc in range(len(v.lFunc)):
for c in v.parFunc[nbFunc]:
func = v.lFunc[nbFunc]
reg = func[0](alpha=c)
#One task prediction
if (func[1] == 0):
reg.fit(feats['train'],gs['train'][nDim])
for s in parts:
p = reg.predict(feats['dev'])
ccc = cccCalc(p,gs[s][nDim])
if (ccc > cccs[s]) :
preds[s] = p
cccs[s] = ccc
function = func[2]
alpha = c
#Multi task prediction
else :
reg.fit(feats['train'],np.transpose(gs['train']))
for s in parts:
p = reg.predict(feats['dev'])[:,nDim]
ccc = cccCalc(p,gs[s][nDim])
if (ccc > cccs[s]) :
preds[s] = p
cccs[s] = ccc
function = func[2]
alpha = c
return cccs, preds, function, alpha
def TOKEN_SVM(matrix, test_matrix, n_authors, doc_authors, vocab, stopwords):
n_docs = matrix.shape[0]
n_test_docs = test_matrix.shape[0]
matrix = matrix / np.sum(matrix, 1)[:, None]
test_matrix = test_matrix / np.sum(test_matrix, 1)[:, None]
svm_model = ll.train(sum(doc_authors, []), matrix.tolist(), '-c 4')
p_label, p_acc, p_val = ll.predict(np.random.rand(n_test_docs),
test_matrix.tolist(), svm_model)
author_probs = np.zeros((n_test_docs, n_authors))
for doc, author in enumerate(p_label):
author_probs[doc, int(author)] = 1
return author_probs
def train(train_X, train_Y, test_X, test_Y):
test_accuracy = []
model_1 = liblinearutil.train(train_Y, train_X, '-s 0 -c 5000 -e 0.000001')
label, accuracy, value = liblinearutil.predict(test_Y, test_X, model_1)
test_accuracy.append((100 - accuracy[0]) / 100)
model_2 = liblinearutil.train(train_Y, train_X, '-s 0 -c 50 -e 0.000001')
label, accuracy, value = liblinearutil.predict(test_Y, test_X, model_2)
test_accuracy.append((100 - accuracy[0]) / 100)
model_3 = liblinearutil.train(train_Y, train_X, '-s 0 -c 0.5 -e 0.000001')
label, accuracy, value = liblinearutil.predict(test_Y, test_X, model_3)
test_accuracy.append((100 - accuracy[0]) / 100)
model_4 = liblinearutil.train(train_Y, train_X, '-s 0 -c 0.005 -e 0.000001')
label, accuracy, value = liblinearutil.predict(test_Y, test_X, model_4)
test_accuracy.append((100 - accuracy[0]) / 100)
model_5 = liblinearutil.train(train_Y, train_X, '-s 0 -c 0.00005 -e 0.000001')
label, accuracy, value = liblinearutil.predict(test_Y, test_X, model_5)
test_accuracy.append((100 - accuracy[0]) / 100)
return lambda_set[test_accuracy.index(min(test_accuracy))], test_accuracy
def train(self, x, y, biased=False):
data = []
for sample in x:
data.append(
dict([(self._features.setId(d), sample[d]) for d in sample]))
labels = [self._labels.setId(C) for C in y]
if self._labels.count() == 2:
labels = [1 if label == 1 else -1 for label in labels]
param = liblinear.parameter(
'-c 1 -s 2 -q' + (' -B {0}'.format(biased) if biased else ''))
else:
param = liblinear.parameter(
'-c 1 -s 4 -q' + (' -B {0}'.format(biased) if biased else ''))
prob = liblinear.problem(labels, data)
self._model = liblinear.train(prob, param)
def eval_SVM(X, y, Xhat, yhat):
# create classification problem
problem = liblinearutil.problem(y, X)
# set SVM parameters
svm_param = liblinearutil.parameter('-s 3 -c 10 -q -B 1')
# train SVM
model = liblinearutil.train(problem, svm_param)
# predict and evaluate
p_label, p_acc, p_val = liblinearutil.predict(yhat, Xhat, model, '-q')
# compute accuracy
acc, mse, scc = liblinearutil.evaluations(yhat, p_label)
return acc
def build(self, images, targets, extra):
shapes, mean_shape, i_stage = extra
n_landmarks = mean_shape.n_points
feature_extractor = self.feature_extractor_builder.build(images, shapes, targets, (mean_shape, i_stage))
print("Extracting local binary features for each image.\n")
features = [ list(feature_extractor.apply(images[i], shapes[i])) for i in xrange(len(images)) ]
print("Features extracted.\n")
w = np.zeros(shape=(2*n_landmarks, len(features[0])))
for lmark in xrange(2*n_landmarks):
print_dynamic("Learning linear regression coefficients for landmark coordinate {}/{}.\n".format(lmark, 2*n_landmarks))
linreg = liblinearutil.train(list(targets[:, lmark]), features, "-s 12 -p 0 -c {}".format(1/float(len(features))))
w_list = linreg.get_decfun()[0]
w[lmark][0:len(w_list)] = w_list
return GlobalRegression(feature_extractor, w, mean_shape)
def AT_FA_SVM(matrix, test_matrix, n_authors, doc_authors, vocab, stopwords):
# set parameters
num_topics = 4
burn_in = 1000 # 0
alpha = 0.1
beta = 0.1
samples = 8
spacing = 100
num_test_docs = test_matrix.shape[0]
doc_authors_new, n_authors_new = add_fic_authors(doc_authors, n_authors)
sampler = at.AtSampler(num_topics, n_authors_new, alpha, beta)
print('Starting!')
theta, phi, likelihood = sampler.train(doc_authors_new, matrix, burn_in,
samples, spacing)
print('theta:', theta.shape)
print('phi:', phi.shape)
print('likelihood:', likelihood)
sampler.n_authors = num_test_docs
theta_test = sampler.classify(test_matrix, phi, burn_in, samples, spacing)
print('theta test:', theta_test.shape)
training_matrix = concatenate_fic_authors(doc_authors, num_topics)
num_test_docs = test_matrix.shape[0]
test_matrix = np.concatenate((theta_test, theta_test), axis=1)
training_matrix = training_matrix / np.sum(training_matrix, 1)[:, None]
test_matrix = test_matrix / np.sum(test_matrix, 1)[:, None]
svm_model = ll.train(sum(doc_authors, []), training_matrix.tolist(),
'-c 4')
p_label, p_acc, p_val = ll.predict(np.random.rand(num_test_docs),
test_matrix.tolist(), svm_model)
author_probs = np.zeros((n_test_docs, n_authors))
for doc, author in enumerate(p_label):
author_probs[doc, int(author)] = 1
return author_probs
def learn_embedding(self, G, **kwargs):
print('l2svm G')
print(nx.info(G))
npairs = G.number_of_nodes() * (G.number_of_nodes() - 1)
self.mapping = {}
self.feature_vecs = np.zeros((npairs, len(G.nodes[0]['fingerprint']) * 3))
labels = np.zeros(npairs) - 1
k = 0
nnodes = G.number_of_nodes()
for i in range(nnodes - 1):
fpi = G.nodes[i]['fingerprint']
for j in range(i + 1, nnodes):
fpj = G.nodes[j]['fingerprint']
self.feature_vecs[k] = self._make_feature_vecs(fpi, fpj)
labels[k] = labels[k + 1] = int(G.has_edge(i, j))
self.mapping[(i, j)] = k
k += 1
self.feature_vecs[k] = self._make_feature_vecs(fpj, fpi)
self.mapping[(j, i)] = k
k += 1
assert np.all(labels >= 0)
print('%d training instance' % (len(labels)))
"""
self.svm = LinearSVC(loss='hinge', C=self.C, tol=0.1,
random_state=self.random_seed, verbose=1)
self.svm.fit(self.feature_vecs, labels)
print('Training completed')
print('Accuracy on training set', self.svm.score(self.feature_vecs[:10], labels[:10]))
"""
# -s 3 is l2 regularized l1 loss functino
params = '-s 2 -C' # parameter selection
params = '-s %d -c %f' % (self.s, self.C)
print('SVM param:', params)
self.svm = train(labels, self.feature_vecs, params)
print('SVM instance', self.svm)
test_true_edges = list(G.edges())[:10]
test_neg_edges = list(nx.complement(G).edges())[:10]
yscore = self.get_edge_scores(test_true_edges + test_neg_edges)
ylabel = np.concatenate((np.ones(10), np.zeros(10)))
auc = roc_auc_score(ylabel, yscore)
print('AUC on training set', auc)
self.G = G
def main():
if __name__ == "__main__":
y, x = svm_read_problem(feature_file, return_scipy=True)
# train:test = 7:3
train_X = x[:14000]
train_y = y[:14000]
test_X = x[14000:]
test_y = y[14000:]
prob = problem(train_y, train_X)
param = parameter("-c 1 -s 2")
model = train(prob, param)
p_labs, p_acc, p_vals = predict(test_y, test_X, model)
accuracy, precision, recall = metrics_result(test_y, p_labs)
print
print "accuracy: ", accuracy
print "precision: ", precision
print "recall: ", recall
def train(self,x,y):
"""
training using y=list,x=dict
parameter = string of parameters
"""
prob=lu.problem(y,x)
para=""
para+= "-s %d -c %f -B %f -p %f -e %f" % (self.L,
self.c,
self.bias,
self.p,
self.eps)
if(self.v!=0):
para+=" -v %d" % self.v
if(self.q!=0):
para+= " -q"
print para
para1=lu.parameter(para)
self.model=lu.train(prob,para1)
return True
def run_classifier(train_file, test_file):
count_one=0
y_train, x_train = svm_read_problem(train_file)
counter=0
while counter<len(y_train):
if y_train[counter]==-1:
count_one=count_one+1
counter=counter+1
w1=count_one/float(len(y_train))
#w1=0.95 # Extra credit
#w1=0.95 # Extra credit
param='-s 0 -w1 '+str(w1)+' -w-1 '+str(1-w1)
#param='-s 0' # Extra Credit
model = train(y_train, x_train, param)
y_test, x_test = svm_read_problem(test_file)
p_labels, p_acc, p_vals = predict(y_test, x_test, model, '-b 1')
accuracy = p_acc[0]
index=0
if model.label[0]==1:
index=0
elif model.label[1]==1:
index=1
counter=0
prob_list=[]
while counter<len(p_vals):
prob_list.append(p_vals[counter][index])
counter=counter+1
output_tup=(p_labels, y_test, prob_list)
return output_tup
def unimodalPredTest(gs, feats, nDim, func, c):
[cccs, preds] = [{} for i in range(2)]
for s in v.aPart:
cccs[s] = -1.0
warnings.filterwarnings('ignore', category=ConvergenceWarning)
if (func == "SVR"):
#Options for liblinear
options = "-s " + str(v.sVal) + " -c " + str(c) + " -B 1 -q"
#We learn the model on train
model = train(gs['train'][nDim], feats['train'], options)
#We predict on data
for s in v.aPart:
pred = np.array(predict(gs[s][nDim], feats[s], model, "-q"))[0]
#We calculate the correlation and store it
ccc = cccCalc(np.array(pred), gs[s][nDim])
if (ccc > cccs[s]):
preds[s] = pred
cccs[s] = ccc
else:
for f in v.lFunc:
if (f[2] == func):
fun = f
reg = fun[0](alpha=c)
if (fun[1] == 0):
reg.fit(feats['train'], gs['train'][nDim])
for s in v.aPart:
p = reg.predict(feats[s])
ccc = cccCalc(p, gs[s][nDim])
if (ccc > cccs[s]):
preds[s] = p
cccs[s] = ccc
else:
reg.fit(feats['train'], np.transpose(gs['train']))
for s in v.aPart:
p = reg.predict(feats[s])[:, nDim]
ccc = cccCalc(p, gs[s][nDim])
if (ccc > cccs[s]):
preds[s] = p
cccs[s] = ccc
return cccs, preds, func, c
def _svm_test_attr_unit(worker_idx, idx_attr_rng, feat_train, feat_test,
label_train, label_test, attr_entry, cache_dir):
idx_list = range(idx_attr_rng[0], idx_attr_rng[1])
c_list = [0.1, 1., 10.]
pred = np.zeros((label_test.shape[0], len(idx_list)), dtype=np.float32)
for i, idx in enumerate(idx_list):
t = time.time()
l_train = label_train[:, idx].astype(np.int)
l_test = label_test[:, idx].astype(np.int)
w1 = l_train.size / l_train.sum() - 1
# w1 = 1.
# if param_C_by_CV:
# c, _ = liblinear.train(l_train, feat_train, '-s 0 -B 1. -C -w1 %f -q' % w1)
# c = max(0.1, c)
# else:
# c = 512.
best_acc = -1.
for c in c_list:
svm_model = liblinear.train(l_train, feat_train,
'-s 0 -B 1. -c %f -w1 %f -q' % (c, w1))
svm_out = liblinear.predict(l_test, feat_test, svm_model,
'-b 1 -q')
acc = svm_out[1][0]
if acc > best_acc:
best_acc = acc
best_c = c
k = svm_model.get_labels().index(1)
prob = np.array(svm_out[2])[:, k]
pred[:, i] = prob
print(
'worker [%d]: "%s(%d)" [%d/%d], acc: %f, c: %f, time cost: %.2f sec'
% (worker_idx, attr_entry[idx]['entry'], idx, i, len(idx_list),
best_acc, best_c, time.time() - t))
io.save_data(pred, os.path.join(cache_dir, '%d.pkl' % worker_idx))
def Train(self):
# Check classifier type
if(self.classifierType == "SVM"):
if(self.packageType == "liblinear"):
from liblinearutil import train
self.cParam = 4# Best cross validation accuracy
self.nFoldsParam = 10
self.classifierModel = train(self.trainTargets, self.trainFeatures, '-c ' + str(self.cParam))
train(self.trainTargets, self.trainFeatures, '-c ' + str(self.cParam) + ' -v ' + str(self.nFoldsParam))
if(self.packageType == "libsvm"):
from svmutil import svm_train
self.cParam = 32# Best cross validation accuracy
self.nFoldsParam = 10
self.classifierModel = train(self.trainTargets, self.trainFeatures, '-c ' + str(self.cParam))
train(self.trainTargets, self.trainFeatures, '-c ' + str(self.cParam) + ' -v ' + str(self.nFoldsParam))
elif(self.classifierType == "DecisionTree"):
if(self.packageType == "nltk"):
import nltk
train_set = []
i = 0;
weights = [];
for fet in self.trainFeatures:
train_set.append((self.trainFeatures[i],self.trainTargets[i]))
weights.append( i * 0.5)
i +=1
self.classifierModel = nltk.DecisionTreeClassifier.train(train_set,entropy_cutoff=.01,depth_cutoff=300,binary=True,verbose=True)
'''
self.classifierModel = AdaBoostClassifier(DecisionTreeClassifier(criterion="entropy", splitter="best", max_depth=1000),
algorithm="SAMME",
n_estimators=200)
'''
'''
self.classifierModel = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1000),
algorithm="SAMME",
n_estimators=200)
'''
#self.classifierModel.fit(train_set)
#sorted(self.classifierModel.labels())
#print(self.classifierModel)
elif(self.packageType == "sklearn"):
import sklearn.tree
self.classifierModel = sklearn.tree.DecisionTreeClassifier(criterion="entropy", splitter="best", max_depth=1000)
# Convert into array not dictionary
trainFeatures = []
for feature in self.trainFeatures:
trainFeatures.append(list(feature.values()))
self.classifierModel.fit(trainFeatures, self.trainTargets)
elif(self.classifierType == "AdaBoost"):
if(self.packageType == "sklearn"):
import sklearn.ensemble
if(self.baseClassifierType == "DecisionTree"):
import sklearn.tree
self.classifierModel = sklearn.ensemble.AdaBoostClassifier(
sklearn.tree.DecisionTreeClassifier(criterion="gini", splitter="best", max_depth=1000),
algorithm="SAMME",
n_estimators=200)
# Convert into array not dictionary
trainFeatures = []
for feature in self.trainFeatures:
trainFeatures.append(list(feature.values()))
self.classifierModel.fit(trainFeatures, self.trainTargets)
else:
print("Only DecisionTree is supported as base classifier")
else:
print("Only sklearn is supported for AdaBoost")
else:
print("Not supported classifier type")
def train_model():
"""训练模型
"""
y, x = svm_read_problem(TRAIN_INPUT_FILE)
m = train(y, x, "-c 4")
save_model(SVM_MODEL_FILE, m)
def svm_test_single_attr():
# config
tar_attr_idx = 1
train_on_val_set = True
reduced_dim = 512
whiten = True
num_attr = 1000
opt = TestAttributeOptions().parse()
# extract feature
feat_data = extract_feature(opt)
feat_train = feat_data['feat_train']
feat_test = feat_data['feat_test']
print('extract feature done!')
# load attribute label
attr_label = io.load_data('datasets/DeepFashion/Fashion_design/' +
opt.fn_label)
attr_entry = io.load_json('datasets/DeepFashion/Fashion_design/' +
opt.fn_entry)
label_train = np.array(
[attr_label[s_id] for s_id in feat_data['id_list_train']])
label_test = np.array(
[attr_label[s_id] for s_id in feat_data['id_list_test']])
label_train = label_train[:, 0:num_attr]
label_test = label_test[:, 0:num_attr]
# label_train = np.random.choice([0,1], size = (feat_train.shape[0], num_attr))
# label_test = np.random.choice([0,1], size = (feat_test.shape[0], num_attr))
# get validation feature and label
id_list_val = io.load_json(
'datasets/DeepFashion/Fashion_design/Split/ca_split.json')['val']
id2idx = {s_id: idx for idx, s_id in enumerate(feat_data['id_list_train'])}
idx_list_val = [id2idx[s_id] for s_id in id_list_val]
feat_val = feat_train[idx_list_val, :]
label_val = label_train[idx_list_val, :]
if train_on_val_set:
feat_train = feat_val
label_train = label_val
print('PCA reduction and whitening...')
t = time.time()
pca = PCA(n_components=reduced_dim, whiten=whiten)
pca.fit(feat_train)
feat_train = pca.transform(feat_train)
feat_test = pca.transform(feat_test)
print('PCA done! (%f sec)' % (time.time() - t))
t = time.time()
print(
'selected attribute: %s(%d)' %
(attr_entry[tar_attr_idx]['entry'], attr_entry[tar_attr_idx]['type']))
label_train = label_train[:, tar_attr_idx].astype(np.int)
label_test = label_test[:, tar_attr_idx].astype(np.int)
# w1 = label_train.size / label_train.sum() - 1
w1 = 1.
print('w1: %f' % w1)
# best_c , _= liblinear.train(label_train, feat_train, '-s 0 -B 1. -C -w1 %f -q' % w1)
for c in [0.1, 1., 10.]:
svm_model = liblinear.train(label_train, feat_train,
'-s 0 -B 1. -c %f -w1 %f -q' % (c, w1))
svm_out = liblinear.predict(label_test, feat_test, svm_model,
'-b 1 -q')
print('c = %f, acc = %f' % (c, svm_out[1][0]))
k = svm_model.get_labels().index(1)
prob = np.array(svm_out[2])[:, k]
print('SVM training time: %f sec' % (time.time() - t))
crit_ap = MeanAP()
crit_ap.add(prob.reshape(-1, 1), label_test.reshape(-1, 1))
ap, _ = crit_ap.compute_mean_ap()
print('AP: %f' % ap)
def train(self):
if os.path.isfile("svm.model") and self.useModel:
self.model = llu.load_model("svm.model")
else:
self.model = llu.train(self.ys, self.xs, self.train_param)
llu.save_model("svm.model", self.model)
def train(word_dict):
get_feature(word_dict, "data/train.dat", "data/train.format")
get_feature(word_dict, "data/test.dat", "data/test.format")
train_y, train_x = linear.svm_read_problem("data/train.format")
model = linear.train(train_y, train_x)
linear.save_model("model.dat", model)
def train(x, y, c, params='-s 2 -B 1 -q'):
return liblin.train(y.tolist(), x.tolist(), '-c {} '.format(c) + params)
def classify(ds_cur = None):
from os import chdir, system
chdir('./liblinear-2.1/python/')
from liblinearutil import problem, parameter, train, predict
chdir('../../')
from pdb import set_trace
from tqdm import tqdm
from pymongo import MongoClient
from json import dumps
from bson.objectid import ObjectId
set_trace()
dont_include = {'_id' : 0}
print 'List of variables:\n'
for key in variable_lookup:
print key[1]
ch1 = raw_input('Input "s" to select custom fields (default selection - all fields):')
if ch1 == 's':
print 'Please input 0 for fields you would like to exclude, any other input would include it.'
for key in variable_lookup:
if key[0] == 'class':
continue
ch2 = raw_input(key[1] + ':')
if ch2 == '0':
dont_include[key[0]] = 0
if ds_cur == None:
conn = MongoClient('mongodb://localhost:27017')
dataset = conn['rmpdb']['dataset_profs_ten_over']
ds_cur = dataset.find(filter = {}, projection = dont_include)
dataset2 = conn['rmpdb']['dataset_profs_five_over_less_ten']
ds_cur2 = dataset2.find(filter = {}, projection = dont_include)
X = [] # Variables
Y = [] # Classes
ids = [] # Keep track of professor IDs
X2 = [] # Variables
Y2 = [] # Classes
ids2 = [] # Keep track of professor IDs
print 'Building training set according to selection..'
for row in tqdm(ds_cur):
x_dict = dict()
for key in row:
if key == 'class':
Y.append(int(row[key]))
elif key == 'prof_id':
ids.append(row[key])
elif isNan(row[key]):
continue
else:
x_dict[int(key)] = float(row[key])
X.append(x_dict)
for row in tqdm(ds_cur2):
x_dict2 = dict()
for key in row:
if key == 'class':
Y2.append(int(row[key]))
elif key == 'prof_id':
ids2.append(row[key])
elif isNan(row[key]):
continue
else:
x_dict2[int(key)] = float(row[key])
X2.append(x_dict2)
ch = raw_input('Include top words for males and females as features? (y/n) [n]: ')
if ch == 'y':
from glob import glob
from json import loads
vec_files = glob('../logs/*.vec')
if not len(vec_files) == 0:
print 'Word vector files found in ../logs: \n'
print vec_files
fch = raw_input('Enter name of file without extension. [../logs/trial0.vec] Enter 0 to skip. ../logs/')
if fch == '0':
male_vector, female_vector = build_vector()
else:
try:
f = open('../logs/' + fch + '.vec', 'r')
male_vector, female_vector = loads(f.read())
except:
f = open('../logs/trial0.vec', 'r')
male_vector, female_vector = loads(f.read())
else:
male_vector, female_vector = build_vector()
print 'Male vectors as (word, count)'
print male_vector
print "============================================="
print 'Female vectors as (word, count)'
print female_vector
print "============================================="
print 'Calculating word features for all professors in dataset. This shall take some time.'
print 'Depending on your cutoff, this can take from 4 - 6 hours. Probably a good idea to get some other stuff done..'
male_words = [tup[0] for tup in male_vector]
female_words = [tup[0] for tup in female_vector]
union_words = list(set(male_words).union(set(female_words)))
final_words = list()
print 'Select words you want to remove by entering "x".'
for word in union_words:
wch = raw_input(word + ':')
if wch == 'x':
continue
else:
final_words.append(word)
from string import punctuation
exclude = set(punctuation)
rmpdb = MongoClient('mongodb://localhost:27017')['rmpdb']
for i in tqdm(range(len(ids))):
prof_id = ids[i]
# male_dict = dict()
# female_dict = dict()
# for tup in male_vector:
# male_dict[tup[0]] = 0
# for tup in female_vector:
# female_dict[tup[0]] = 0
vec_dict = dict()
for word in final_words:
vec_dict[word] = 0
prof_comments = rmpdb['profs'].find_one({'_id' : ObjectId(prof_id)}, {'_id' : 0, 'all comments.rComments' : 1})
for comment in prof_comments['all comments']:
text = comment['rComments']
no_punc_text = ''.join(ch for ch in text if ch not in exclude)
toks = no_punc_text.split()
for tok in toks:
# if tok.lower() in male_dict:
# male_dict[tok.lower()] += 1
# if tok.lower() in female_dict:
# female_dict[tok.lower()] += 1
if tok.lower() in vec_dict:
vec_dict[tok.lower()] += 1
feature_counter = 53 #starts right after variable_lookup['53']
# for j in range(len(male_vector)):
# feature_counter += 1
# tup = male_vector[j]
# if not male_dict[tup[0]] == 0:
# X[i][feature_counter] = male_dict[tup[0]]
# for j in range(len(female_vector)):
# feature_counter += 1
# tup = female_vector[j]
# if not female_dict[tup[0]] == 0:
# X[i][feature_counter] = female_dict[tup[0]]
for j in range(len(final_words)):
feature_counter += 1
word = final_words[j]
if not vec_dict[word] == 0:
X[i][feature_counter] = vec_dict[word]
# if feature_counter == 97:
# break
print "Building test set.."
for i in tqdm(range(len(ids2))):
prof_id = ids2[i]
# male_dict = dict()
# female_dict = dict()
# for tup in male_vector:
# male_dict[tup[0]] = 0
# for tup in female_vector:
# female_dict[tup[0]] = 0
vec_dict = dict()
for word in final_words:
vec_dict[word] = 0
prof_comments = rmpdb['profs'].find_one({'_id' : ObjectId(prof_id)}, {'_id' : 0, 'all comments.rComments' : 1})
for comment in prof_comments['all comments']:
text = comment['rComments']
no_punc_text = ''.join(ch for ch in text if ch not in exclude)
toks = no_punc_text.split()
for tok in toks:
# if tok.lower() in male_dict:
# male_dict[tok.lower()] += 1
# if tok.lower() in female_dict:
# female_dict[tok.lower()] += 1
if tok.lower() in vec_dict:
vec_dict[tok.lower()] += 1
feature_counter = 53 #starts right after variable_lookup['53']
# for j in range(len(male_vector)):
# feature_counter += 1
# tup = male_vector[j]
# if not male_dict[tup[0]] == 0:
# X[i][feature_counter] = male_dict[tup[0]]
# for j in range(len(female_vector)):
# feature_counter += 1
# tup = female_vector[j]
# if not female_dict[tup[0]] == 0:
# X[i][feature_counter] = female_dict[tup[0]]
for j in range(len(final_words)):
feature_counter += 1
word = final_words[j]
if not vec_dict[word] == 0:
X2[i][feature_counter] = vec_dict[word]
print 'Words used:'
print final_words
else:
pass
print 'Writing temp files for AUC calculation..'
build_svm_file(X, Y)
print 'Temp file written..'
print 'Features used:'
fstr = list()
for key in variable_lookup:
if key[0] in dont_include or key[0] == 'class':
continue
else:
fstr.append(key[1])
print dumps(fstr)
print '======================================\n'
prob = problem(Y, X)
param = parameter('-s 6 -v 10')
m = train(prob, param)
print 'Evaluating..\n'
system('liblinear-2.1/train -s 6 -v 10 liblinear-2.1/temp_ds')
model = train(prob, parameter('-s 6 -q'))
#system('rm liblinear-2.1/temp_ds')
print 'Testing model on test set..'
p_Y2, p_acc, p_vals = predict(Y2, X2, model)
contingency_mat = [[0, 0], [0, 0]]
for i in range(len(Y2)):
if (Y2[i] == 0) and (p_Y2[i] == 0):
contingency_mat[0][0] += 1
elif (Y2[i] == 0) and (p_Y2[i] == 1):
contingency_mat[0][1] += 1
elif (Y2[i] == 1) and (p_Y2[i] == 0):
contingency_mat[1][0] += 1
else:
contingency_mat[1][1] += 1
return (model, p_acc, contingency_mat)
def train(y, x, params):
""" Trains on y,x and returns the model """
print "Training on data of size: ", len(y)
m = llu.train(y,x, params)
return m
#coding: utf-8
import liblinearutil
import outputLIBSVMformat
train_label, train_data = liblinearutil.svm_read_problem("./train_libsvmFormat.txt")
#カーネル関数は線型
model = liblinearutil.train(train_label, train_data, "-s 3")
test_label, test_data = liblinearutil.svm_read_problem("./test_libsvmFormat.txt")
p_label, p_acc, p_val = liblinearutil.predict(test_label, test_data, model)
def train(instance_file, model_file, param):
y, x = ll.svm_read_problem(instance_file)
prob = ll.problem(y, x)
m = ll.train(prob, param)
ll.save_model(model_file, m)
print 'done training', model_file
def train(self):
sys.stderr.write('creating training problem...')
prob = problem(self.labels, self.contexts)
sys.stderr.write('done\ntraining with option(s) "'+self.parameters+'"...')
self.model = train(prob, parameter(self.parameters))
sys.stderr.write('done\n')
def simpleLibLinear(X_train,Y_train):
prob = ll.problem(Y_train,X_train)
param = ll.parameter('-c '+str(c))
m = ll.train(prob, param)
return m