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 gridSearch(x, y, kernel='rbf'):
kernel_type = {
'linear': '0',
'polynomial': '1',
'rbf': '2',
'self-defined': '4'
}
C = [1, 10, 20, 30]
gamma = [0.2, 0.1, 0.05, 0.01]
degree = [2, 3, 4, 5]
coef0 = [0, 5, 10]
max_acc = 0.0
opt = '-q -s 0 -v 5 '
if (kernel == 'linear'):
best_param = (C[0])
for c in C:
# print('C = {}'.format(c))
acc = svm_train(y, x,
opt + '-t {} -c {}'.format(kernel_type[kernel], c))
if (acc > max_acc):
max_acc = acc
best_param = (c)
elif (kernel == 'polynomial'):
best_param = (C[0], gamma[0], coef0[0], degree[0])
for c in C:
for g in gamma:
for d in degree:
for r in coef0:
# print('C = {}, gamma = {}, degree = {}, coef0 = {}'.format(c, g, d, r))
acc = svm_train(
y, x, opt + '-t {} -c {} -g {} -d {} -r {}'.format(
kernel_type[kernel], c, g, d, r))
if (acc > max_acc):
max_acc = acc
best_param = (c, g, d, r)
elif (kernel == 'rbf'):
best_param = (C[0], gamma[0])
for c in C:
for g in gamma:
# print('C = {}, gamma = {}'.format(c, g))
acc = svm_train(
y, x, '-q -s 0 -v 5 -t {} -c {} -g {}'.format(
kernel_type[kernel], c, g))
if (acc > max_acc):
max_acc = acc
best_param = (c, g)
return best_param
def setUpClass(self):
"""
Set up the unit test by loading the dataset and training a model.
"""
if not _HAS_SKLEARN:
return
if not _HAS_LIBSVM:
return
scikit_data = load_boston()
prob = svmutil.svm_problem(
scikit_data["target"] > scikit_data["target"].mean(),
scikit_data["data"].tolist(),
)
param = svmutil.svm_parameter()
param.svm_type = svmutil.C_SVC
param.kernel_type = svmutil.LINEAR
param.eps = 1
libsvm_model = svmutil.svm_train(prob, param)
libsvm_spec = libsvm_converter.convert(
libsvm_model, scikit_data.feature_names, "target"
).get_spec()
# Save the data and the model
self.scikit_data = scikit_data
self.libsvm_spec = libsvm_spec
def setUpClass(self):
"""
Set up the unit test by loading the dataset and training a model.
"""
if not _HAS_LIBSVM:
# setUpClass is still called even if class is skipped.
return
# Generate some random data.
# This unit test should not rely on scikit learn for test data.
self.x, self.y = [], []
random.seed(42)
for _ in range(50):
self.x.append([random.gauss(200, 30), random.gauss(-100, 22)])
self.y.append(random.choice([1, 2]))
self.y[0] = 1 # Make sure 1 is always the first label it sees
self.y[1] = 2
self.column_names = ["x1", "x2"]
self.prob = svmutil.svm_problem(self.y, self.x)
param = svmutil.svm_parameter()
param.svm_type = svmutil.NU_SVC
param.kernel_type = svmutil.LINEAR
param.eps = 1
param.probability = 1
# Save the data and the model
self.libsvm_model = svmutil.svm_train(self.prob, param)
self.df = pd.DataFrame(self.x, columns=self.column_names)
def train_svm(train_features,
train_labels,
arguments='-s 0 -t 0'): # -c param 10-4, 10+4
print "SVM is being trained"
svm = svm_train(train_labels, train_features, arguments)
print "SVM is trained"
return svm
def test_convert_svmc(self):
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
y[y == 2] = 1
prob = svmutil.svm_problem(y, X.tolist())
param = svmutil.svm_parameter()
param.svm_type = SVC
param.kernel_type = svmutil.RBF
param.eps = 1
param.probability = 1
if noprint:
param.print_func = noprint
libsvm_model = svmutil.svm_train(prob, param)
node = convert(libsvm_model, "LibSvmSvmc",
[('input', FloatTensorType())])
self.assertTrue(node is not None)
dump_data_and_model(X[:5].astype(numpy.float32),
SkAPIClProba2(libsvm_model),
node,
basename="LibSvmSvmc-Dec2")
def test_libsvm_trains_correctly(heart_scale):
y, x = heart_scale
m = svm_train(y[:200], x[:200], '-c 4')
p_label, p_acc, p_val = svm_predict(y[200:], x[200:], m)
assert p_acc == pytest.approx(
[84.28571428571429, 0.6285714285714286, 0.463744141163496])
assert p_label[:6] == [-1.0, 1.0, 1.0, -1.0, 1.0, -1.0]
def test_convert_nusvmc(self):
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
y[y == 2] = 1
prob = svmutil.svm_problem(y, X.tolist())
param = svmutil.svm_parameter()
param.svm_type = NuSVC
param.kernel_type = svmutil.RBF
param.eps = 1
param.probability = 1
if noprint:
param.print_func = noprint
libsvm_model = svmutil.svm_train(prob, param)
node = convert(libsvm_model, "LibSvmNuSvmc",
[('input', FloatTensorType(shape=['None', 'None']))])
self.assertTrue(node is not None)
dump_data_and_model(
X[:5].astype(numpy.float32),
SkAPIClProba2(libsvm_model),
node,
basename="LibSvmNuSvmc-Dec2",
allow_failure=
"StrictVersion(onnxruntime.__version__) <= StrictVersion('0.1.3')")
def train(data, path):
if os.path.exists(path):
print 'Model path exists, do nothing.'
return
print "Loading features."
integerizer = tools.integerization.CIntegerization()
labels = []
samples = []
for sent in io.getsent(data):
for index in range(len(sent)):
f = feature.extractFeatures(sent, index, integerizer)
x = int(sent[index][2])
assert x == 0 or x == 1
if x == 0: x = -1
labels.append(x)
samples.append(f)
print "Training SVM."
problem = svm.svm_problem(labels, samples)
param = svm.svm_parameter()
param.svm_type = svm.C_SVC
param.kernel_type = svm.LINEAR #
param.C = 1
#param.degree=2
param.eps = 1.0
param.probability = 1
param.cache_size = 1000
param.shrinking = 0
model = svmutil.svm_train(problem, param)
print "Saving model."
os.mkdir(path)
svmutil.svm_save_model(os.path.join(path, "scr"), model)
integerizer.write(os.path.join(path, "int"))
def test_convert_svmc_raw(self):
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
y[y == 2] = 1
prob = svmutil.svm_problem(y, X.tolist())
param = svmutil.svm_parameter()
param.svm_type = SVC
param.kernel_type = svmutil.RBF
param.eps = 1
param.probability = 0
if noprint:
param.print_func = noprint
libsvm_model = svmutil.svm_train(prob, param)
# known svm runtime dimension error in ONNX Runtime
node = convert(libsvm_model, "LibSvmSvmcRaw",
[('input', FloatTensorType(shape=['None', 'None']))])
self.assertTrue(node is not None)
dump_data_and_model(
X[:5].astype(numpy.float32),
SkAPICl(libsvm_model),
node,
basename="LibSvmSvmcRaw",
allow_failure=
"StrictVersion(onnxruntime.__version__) < StrictVersion('0.5.0')")
def get_SVM_trained_classifer(self, training_datafile,
classifier_dumpfile):
# read all tweets and labels
tweet_items = common.get_filtered_training_data(training_datafile)
tweets = []
for (words, sentiment) in tweet_items:
words_filtered = [
e.lower() for e in words.split() if (common.is_ascii(e))
]
tweets.append((words_filtered, sentiment))
results = helper.get_SVM_feature_vector_and_labels(
self.feature_list, tweets)
self.feature_vectors = results['feature_vector']
self.labels = results['labels']
problem = svm_problem(self.labels, self.feature_vectors)
# '-q' option suppress console output
param = svm_parameter('-q')
param.kernel_type = LINEAR
# param.show()
classifier = svm_train(problem, param)
svm_save_model(classifier_dumpfile, classifier)
return classifier
def test_convert_svmc_linear_raw_multi(self):
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
y[-5:] = 3
prob = svmutil.svm_problem(y, X.tolist())
param = svmutil.svm_parameter()
param.svm_type = SVC
param.kernel_type = svmutil.LINEAR
param.eps = 1
param.probability = 0
if noprint:
param.print_func = noprint
libsvm_model = svmutil.svm_train(prob, param)
node = convert(libsvm_model, "LibSvmNuSvmcMultiRaw",
[('input', FloatTensorType(shape=['None', 2]))])
self.assertTrue(node is not None)
X2 = numpy.vstack([X[:2], X[60:62], X[110:112],
X[147:149]]) # 5x0, 5x1
dump_data_and_model(
X2.astype(numpy.float32),
SkAPICl(libsvm_model),
node,
basename="LibSvmSvmcRaw-Dec3",
verbose=False,
allow_failure=
"StrictVersion(onnxruntime.__version__) <= StrictVersion('0.1.3')")
def train_impl(self):
labels = []
features = []
for labeled_feature in self.get_labeled_features():
labels.append(labeled_feature.label)
features.append(labeled_feature.feature)
options = self.get_train_options()
self._model = svm_train(labels, features, options)
def train_impl( self ):
labels = []
features = []
for labeled_feature in self.get_labeled_features( ):
labels.append(labeled_feature.label)
features.append(labeled_feature.feature)
options = self.get_train_options()
self._model = svm_train( labels, features, options )
def generate_svm_model(rows, param, kmers=[1,2,3]):
y, x = libsvm_generate_matrix(rows, kmers, dense=True)
prob = svmutil.svm_problem(y, x)
# s = 3 -- epsilon-SVR
param_str = "-s 3 -b 1 -q " # epsilon-SVR, prob estimate true, quiet mode
param_str += " ".join(["-{} {}".format(k,v) for k,v in param.items()])
params = svmutil.svm_parameter(param_str)
model = svmutil.svm_train(prob, params)
return model
def _stop_training(self):
super(LibSVMClassifier, self)._stop_training()
self.normalizer = _LabelNormalizer(self.labels)
labels = self.normalizer.normalize(self.labels.tolist())
features = self.data
# Call svm training method.
prob = libsvmutil.svm_problem(labels, features.tolist())
# Train
self.model = libsvmutil.svm_train(prob, self.parameter)
def finishLearning(self):
"""
Method should be called to indicate that SVM learning is finished.
When this happens we take all the learned values and feed them into
a new SVM Model to train it for later inference.
"""
if self.__svmModel==None:
#model = svm_train(y, x [, 'training_options'])
if HMAX.DEBUG:
print "creating svmModel..."
self.__svmModel = svmutil.svm_train(self.classes, self.learned, "-q -b 1")
if HMAX.DEBUG:
print "svmModel successfully trained"
def finishLearning(self):
"""
Method should be called to indicate that SVM learning is finished.
When this happens we take all the learned values and feed them into
a new SVM Model to train it for later inference.
"""
if self.__svmModel == None:
#model = svm_train(y, x [, 'training_options'])
if HMAX.DEBUG:
print "creating svmModel..."
self.__svmModel = svmutil.svm_train(self.classes, self.learned,
"-q -b 1")
if HMAX.DEBUG:
print "svmModel successfully trained"
def train_and_predict(x, y, kernel, option=''):
kernel_type = {
'linear': '0',
'polynomial': '1',
'rbf': '2',
'sigmoid': '3',
'self-defined': '4'
}
x_train, x_test = x
y_train, y_test = y
param = '-q -t ' + kernel_type[
kernel] + option # -q: suppress the output in libsvm
m = svm_train(y_train, x_train, param)
pred, pred_acc, pred_val = svm_predict(y_test, x_test, m)
def train(self, input_data_path, params="-t 0 -c 4 -b 1", is_eval=True):
with commons.PhaseLogger("LIBSVM.train.read_problem"):
Y, X = svmutil.svm_read_problem(input_data_path + "/Train.txt")
prob = svmutil.svm_problem(Y, X)
#Y, X = svmutil.svm_read_problem(input_data_path + "\\Train.txt")
self._params = svmutil.svm_parameter(params)
with commons.PhaseLogger("LIBSVM.train.svm_train"):
self._model = svmutil.svm_train(prob, self._params)
self._init = True
if is_eval is True:
p_labels, p_acc, p_vals = svmutil.svm_predict(Y, X, self._model)
acc, mse, _ = p_acc
logging.info("[%s]: train with Acc[%.4f] Mse[%.4f]" %
(self._get_class_name(), acc, mse))
def train(self, labeled_featuresets):
"""
Trains the classifier on the specified training set. Multiple calls to
this method invalidates the previous ones.
The labeled_featuresets parameter must have the format [([feature], label)]
"""
self.__features_ids = {}
self.__last_feature_id = 0
self.__labels_ids = {}
self.__labels = []
y, x = self.__adapt_labeled_featuresets(labeled_featuresets)
prob = svm_problem(y, x)
param = svm_parameter('-c 1000 -q')
self.__model = svm_train(prob, param)
def train(self, labeled_featuresets):
"""
Trains the classifier on the specified training set. Multiple calls to
this method invalidates the previous ones.
The labeled_featuresets parameter must have the format [([feature], label)]
"""
self.__features_ids = {}
self.__last_feature_id = 0
self.__labels_ids = {}
self.__labels = []
y, x = self.__adapt_labeled_featuresets(labeled_featuresets)
prob = svm_problem(y, x)
param = svm_parameter('-c 1000 -q')
self.__model = svm_train(prob, param)
def predict(tr_data_arr, tr_label_arr, pred_data_arr, pred_label_arr):
data_arr = data_format(tr_data_arr)
prob = svm_problem(tr_label_arr, data_arr)
# 以下参数c和g通过交叉验证得到
param = svm_parameter('-c 2048.0 -g 0.001953125')
svm_model = svm_train(prob, param)
pred_data_arr = data_format(pred_data_arr)
pred_data_len = len(pred_label_arr)
wrong = 0
for idx, data in enumerate(pred_data_arr):
p_label, p_acc, p_val = svm_predict(
[pred_label_arr[idx]], [data], svm_model)
if int(p_label[0]) != int(pred_label_arr[idx]):
wrong += 1
accuracy = (pred_data_len - wrong) * 100.0 / pred_data_len
return pred_data_len, wrong, accuracy
def setUpClass(self):
"""
Set up the unit test by loading the dataset and training a model.
"""
if not _HAS_SKLEARN:
return
if not _HAS_LIBSVM:
return
scikit_data = load_boston()
prob = svmutil.svm_problem(scikit_data["target"],
scikit_data["data"].tolist())
param = svmutil.svm_parameter()
param.svm_type = svmutil.NU_SVR
param.kernel_type = svmutil.LINEAR
param.eps = 1
self.libsvm_model = svmutil.svm_train(prob, param)
def process(training_file, test_file, check, draw):
# Load training data.
with open(training_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# Convert data to lists for libsvm.
class_1 = map(list, class_1)
class_2 = map(list, class_2)
labels = list(labels)
samples = class_1 + class_2
problem = svmutil.svm_problem(labels, samples)
# Don't print to stdout, use radial basis functions.
param = svmutil.svm_parameter('-q -t 2')
model = svmutil.svm_train(problem, param)
# Load test data.
with open(test_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
class_1 = map(list, class_1)
class_2 = map(list, class_2)
labels = list(labels)
if check:
# Sadly, this prints to stdout too :-/
svmutil.svm_predict(labels, class_1 + class_2,
model) # Prints accuracy.
if draw:
def classify(x, y, model=model):
return array(
svmutil.svm_predict([0] * len(x), map(list, zip(x, y)),
model)[0])
imtools.plot_2d_boundary(
[-6, 6, -6, 6], [array(class_1), array(class_2)], classify,
[1, -1])
show()
def svr_training(X_features,Y_classes,classes=[],output='svr_',training_options = '-s 3 -t 0 -b 1'):
"""
Configure multiple SV Machines based on a one-against-all (1AA) approach
Input:
- X_features ndarray(float) : Array of instance features (instances on rows)
- Y_classes ndarray(int) : Array of classes identification
- classes ['int'] : Classes to be used for the 1AA approach
Output:
- model_classes [] : SVM models for classes given in 'classes'
---
"""
model_classes = []
training_options = '-s 3 -t 0 -b 1'
diro = 'models/'
try:
os.mkdir(diro)
except:
pass;
for i_class in classes:
classe = 'class'+str(i_class)
this_class_indx, other_class_indx = sample_selection(Y_classes,i_class)
X = X_features[np.concatenate((this_class_indx,other_class_indx))]
Y = np.zeros((len(X),1))
Y[:len(this_class_indx)] = 1
Y[len(this_class_indx):] = -1
Y_list,X_list = _convert_arrays2lists(Y,X)
model_classes.append(svm_train(Y_list,X_list,training_options))
svm_save_model(diro+output+classe+'.model',model_classes[-1])
np.savetxt(output+classe+'_svr.dat',np.concatenate((Y,X),axis=1),fmt='%f')
return model_classes
def computeLayer(self, layer):
"""
@param layer: the output HmaxLayer to store results in.
"""
LevelFilter.computeLayer(layer)
#need to enable learning mode from UI (after S2 trained)
#during training, need to pass in class labels
vec = layer.array[:,0,0].tolist()
if self.isLearning:
if len(self.learned)>=100 and self.svmModel==None:
#model = svm_train(y, x [, 'training_options'])
self.svmModel = svmutil.svm_train(self.classes, self.learned)
return
self.classes.append(0)
self.learned.append(vec)
elif self.svmModel!=None:
#p_labs, p_acc, p_vals = svm_predict(y, x, model [,'predicting_options'])
pLabs, pAcc, pVals = svmutil.svm_predict([0], vec, self.svmModel)
print "SVM Result: ", pLabs, pAcc, pVals
def process(training_file, test_file, check, draw):
# Load training data.
with open(training_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# Convert data to lists for libsvm.
class_1 = map(list, class_1)
class_2 = map(list, class_2)
labels = list(labels)
samples = class_1 + class_2
problem = svmutil.svm_problem(labels, samples)
# Don't print to stdout, use radial basis functions.
param = svmutil.svm_parameter('-q -t 2')
model = svmutil.svm_train(problem, param)
# Load test data.
with open(test_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
class_1 = map(list, class_1)
class_2 = map(list, class_2)
labels = list(labels)
if check:
# Sadly, this prints to stdout too :-/
svmutil.svm_predict(labels, class_1 + class_2, model) # Prints accuracy.
if draw:
def classify(x, y, model=model):
return array(svmutil.svm_predict([0] * len(x), map(list, zip(x, y)),
model)[0])
imtools.plot_2d_boundary(
[-6, 6, -6, 6], [array(class_1), array(class_2)], classify, [1, -1])
show()
def test_convert_svmr_linear(self):
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
prob = svmutil.svm_problem(y, X.tolist())
param = svmutil.svm_parameter()
param.svm_type = SVR
param.kernel_type = svmutil.LINEAR
param.eps = 1
if noprint:
param.print_func = noprint
libsvm_model = svmutil.svm_train(prob, param)
node = convert(libsvm_model, "LibSvmSvmrLinear",
[('input', FloatTensorType())])
self.assertTrue(node is not None)
dump_data_and_model(X[:5].astype(numpy.float32),
SkAPIReg(libsvm_model),
node,
basename="LibSvmSvmrLinear-Dec3")
def run_kfold(param_dict, rows, numfold, kmers=[1,2,3]):
"""
Run k KFold
Args:
param_dict: dictionary mapping param string to its value
rows: input rows
numfold: k for cross validation
kmers: list of kmers, default [1,2,3]
Return:
dictionary of model performance (SCC, MSE) if benchmark is True, else
return predictions for each fold
"""
kf = KFold(numfold, shuffle=True)
splitted = kf.split(rows)
param_str = "-s 3 -b 1 -q " # epsilon-SVR, prob estimate true, quiet mode
param_str += " ".join(["-{} {}".format(k,v) for k,v in param_dict.items()])
params = svmutil.svm_parameter(param_str)
foldidx = 1
fold_results = []
for train_idx, test_idx in splitted:
train_list = [rows[i] for i in train_idx]
test_list = [rows[i] for i in test_idx]
y_train, x_train = libsvm_generate_matrix(train_list, kmers)
y_test, x_test = libsvm_generate_matrix(test_list, kmers)
train_prob = svmutil.svm_problem(y_train, x_train)
model = svmutil.svm_train(train_prob, params)
#svmutil.svm_save_model('model_name.model', m)
# y is only needed when we need the model performance
svmpred = svmutil.svm_predict(y_test, x_test, model, options="-q")
fold_results.append({"test":test_list, "svmpred":svmpred})
return fold_results
def train(data, path):
if os.path.exists(path):
print 'Model path exists, do nothing.'
return
print "Loading features."
integerizer = tools.integerization.CIntegerization()
labels = []
samples = []
for classid, feature in extractfeat.extractFeat(data, integerizer):
labels.append(classid)
samples.append(feature)
print "Training SVM."
problem = svm.svm_problem(labels, samples)
param = svm.svm_parameter()
param.kernel_type = svm.LINEAR#
param.C=1
#param.degree=2
param.eps=1
param.probability=1
model = svmutil.svm_train(problem, param)
print "Saving model."
os.mkdir(path)
svmutil.svm_save_model(os.path.join(path, "scr"), model)
integerizer.write(os.path.join(path, "int"))
def train(c, train_x, train_y):
params = f'-h 0 -s 0 -t 4 -c {c} -q'
return svmutil.svm_train(train_y, train_x, params)
def Training(self, kernel, target, data, IsKernel=False):
problem = svm.svm_problem(target, data, isKernel=IsKernel)
parameter = svm.svm_parameter(kernel)
model = svmutil.svm_train(problem, parameter)
return model
sum1+=model_summary.wv[tok[0]]
# print(sum1)
sum1 = sum1/len(headlines[h])
# print(sum1)
sum1 = np.concatenate((sum1,cat_vec[combine_f["Category"][h]],cat_vec[combine_f["Post_Type"][h]], arrays[h]))
head_vec.append(sum1)
head_vec = np.array(head_vec)
feat_len = int(0.8*len(head_vec))
train = head_vec[:feat_len]
res_train = combine_f["class"][:feat_len].tolist()
test = head_vec[feat_len:]
res_test = combine_f["class"][feat_len:].tolist()
print(len(test), len(res_test))
model_svm = svmutil.svm_train(res_train, train,'-t 2 -c 2')
y = svmutil.svm_predict(res_test, test, model_svm)
confusion_matrix(res_test, y[0], labels=[0,1])
tn, fp, fn, tp = confusion_matrix(res_test, y[0], labels=[0,1]).ravel()
recall = tp/(tp+fn)
recall
precision = tp/(tp+fp)
precision
f1 = precision*recall*2/(precision+recall)
f1
from train import *
from tools import *
from libsvm.svmutil import svm_train, svm_save_model
from ml_ops import search, train, bootstrap
import argparse, sys, datetime
modes = ['train', 'bootstrap', 'search']
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--mode", help="specify the mode [-m train|bootstrap|search]", required=True, choices=modes)
args = parser.parse_args()
if args.mode == 'train':
hists, labels = train()
svm = svm_train(labels, hists, '-s 0 -t 0 -c 1')
model_name = 'svm_trained_' + str(datetime.datetime.now()).replace(' ', '_') + '.dat'
svm_save_model(model_name, svm)
if args.mode == 'bootstrap':
hists, labels = train()
hists, labels = bootstrap(hists, labels)
svm = svm_train(labels, hists, '-s 0 -t 0 -c 1')
model_name = 'svm_bootstrapped_' + str(datetime.datetime.now()).replace(' ', '_') + '.dat'
svm_save_model(model_name, svm)
if args.mode == 'search':
search()
f.close()
train_data = train_true[::]
train_data.extend(train_fake)
tmp = list()
for li in train_data:
tmp.append(dict(enumerate(li)))
train_data = tmp
train_label = [1 for i in range(len(train_true))
] + [-1 for i in range(len(train_fake))]
eva_data = eva_true[::]
eva_data.extend(eva_fake)
tmp = list()
for li in eva_data:
tmp.append(dict(enumerate(li)))
eva_data = tmp
eva_label = [1 for i in range(len(eva_true))
] + [-1 for i in range(len(eva_fake))]
model = svmutil.svm_train([1 for i in range(len(train_true))] +
[-1 for i in range(len(train_fake))], train_data,
'-c 0.03125 -g 0.25')
print type(model)
p_label, p_acc, p_val = svmutil.svm_predict([1 for i in range(len(eva_true))] +
[-1 for i in range(len(eva_fake))],
eva_data, model)
print p_acc
def train(y, x):
train_len = int(1.0 * len(y))
prob = svmutil.svm_problem(y[:train_len], x[:train_len])
param = svmutil.svm_parameter('-t 2 -c 4 -b 1 -e 1e-12')
m = svmutil.svm_train(prob, param)
return m
acc = numpy.sum(1.0 * (res == test_labels)) / len(test_labels)
print 'Bayes Accuracy:', acc
print_confusion(res, test_labels, classnames)
# FIXME: Bayes accuracy gets very bad if the input dimensions aren't reduced
# enough. Probably some float underflow due to things not using log
# probabilities?
# Test SVM.
features = map(list, features)
test_features = map(list, test_features)
str_int_map = {} # libSVM needs int labels.
for i, c in enumerate(classnames):
str_int_map[c], str_int_map[i] = i, c
def convert_labels(labels, str_int_map):
return [str_int_map[l] for l in labels]
problem = svmutil.svm_problem(convert_labels(labels, str_int_map), features)
# Use a linear kernel, radial basis functions have horrible results (~20% acc)
param = svmutil.svm_parameter('-q -t 0')
model = svmutil.svm_train(problem, param)
res = svmutil.svm_predict(convert_labels(test_labels, str_int_map),
test_features, model)[0]
res = convert_labels(res, str_int_map)
acc = numpy.sum(1.0 * (res == test_labels)) / len(test_labels)
print 'SVM Accuracy:', acc
print_confusion(res, test_labels, classnames)
################################################################################
## Train a SVM classification model
#
print("Fitting the classifier to the training set")
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
prob = svm_problem(y_train,X_train_pca)
param = svm_parameter("-q")
param.kernel='rbf'
#param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
# 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
param.C=32
param.gamma=0.0001
print("Pass")
#parameters = GridSearchCV( param_grid)
model= svm_train(prob,param)
#clf = clf.fit(X_train_pca, y_train)
#print("Best estimator found by grid search:")
#print(m.best_estimator_)
y_pred, pred_acc, pred_val = svm_predict(y_test,X_test_pca,model)
################################################################################
## Quantitative evaluation of the model quality on the test set
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
print("Predicting people's names on the test set")
def train_svm(train_features, train_labels, arguments='-s 0 -t 0'): # -c param 10-4, 10+4
print "SVM is being trained"
svm = svm_train(train_labels, train_features, arguments)
print "SVM is trained"
return svm
def train():
(data, chords) = load_data()
print len(data)
model = svmutil.svm_train(chords, data)
svmutil.svm_save_model(model_file, model)
import os
from libsvm import svmutil
import ocr
OCR_PATH = '/Users/thakis/Downloads/data/sudoku_images/ocr_data/'
features, labels = ocr.load_ocr_data(os.path.join(OCR_PATH, 'training'))
test_features, test_labels = \
ocr.load_ocr_data(os.path.join(OCR_PATH, 'testing'))
features = map(list, features)
test_features = map(list, test_features)
problem = svmutil.svm_problem(labels, features)
param = svmutil.svm_parameter('-q -t 0')
model = svmutil.svm_train(problem, param)
print 'Training data fit:'
svmutil.svm_predict(labels, features, model)
print 'Testing data fit:'
svmutil.svm_predict(test_labels, test_features, model)
