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recover some updates
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@QinbinLi
QinbinLi committed Apr 29, 2019
1 parent 0f0ab9e commit cbc08c6
Showing 1 changed file with 83 additions and 38 deletions.
121 changes: 83 additions & 38 deletions python/thundersvm/thundersvm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,35 +130,38 @@ def fit(self, X, y):
csr_data = (c_float * (self.n_sv * self.n_features))()
data_size = (c_int * 1)()
thundersvm.get_sv(csr_row, csr_col, csr_data, data_size, c_void_p(self.model))
self.row = np.array([csr_row[index] for index in range(0, self.n_sv + 1)])
self.col = np.array([csr_col[index] for index in range(0, data_size[0])])
self.data = np.array([csr_data[index] for index in range(0, data_size[0])])

self.support_vectors_ = sp.csr_matrix((self.data, self.col, self.row))
if self._sparse == False:
self.support_vectors_ = self.support_vectors_.toarray(order = 'C')

dual_coef = (c_float * ((self.n_classes - 1) * self.n_sv))()
thundersvm.get_coef(dual_coef, self.n_classes, self.n_sv, c_void_p(self.model))

self.dual_coef_ = np.array([dual_coef[index] for index in range(0, (self.n_classes - 1) * self.n_sv)]).astype(float)
self.dual_coef_ = np.reshape(self.dual_coef_, (self.n_classes - 1, self.n_sv))

rho_size = int(self.n_classes * (self.n_classes - 1) / 2)
self.n_binary_model = rho_size
rho = (c_float * rho_size)()
thundersvm.get_rho(rho, rho_size, c_void_p(self.model))
self.intercept_ = np.array([rho[index] for index in range(0, rho_size)]).astype(float)

if self.kernel == 'linear':
coef = (c_float * (self.n_binary_model * self.n_features))()
thundersvm.get_linear_coef(coef, self.n_binary_model, self.n_features, c_void_p(self.model))
self.coef_ = np.array([coef[index] for index in range(0, self.n_binary_model * self.n_features)]).astype(float)
self.coef_ = np.reshape(self.coef_, (self.n_binary_model, self.n_features))

self.intercept_ = np.array([rho[index] for index in range(0, rho_size)]).astype(float)

self.row = np.array([csr_row[index] for index in range(0, self.n_sv + 1)])
self.col = np.array([csr_col[index] for index in range(0, data_size[0])])
self.data = np.array([csr_data[index] for index in range(0, data_size[0])])

self.support_vectors_ = sp.csr_matrix((self.data, self.col, self.row))
if self._sparse == False:
self.support_vectors_ = self.support_vectors_.toarray(order = 'C')



n_support_ = (c_int * self.n_classes)()
thundersvm.get_support_classes(n_support_, self.n_classes, c_void_p(self.model))

self.n_support_ = np.array([n_support_[index] for index in range(0, self.n_classes)]).astype(int)

self.shape_fit_ = X.shape
Expand Down Expand Up @@ -234,13 +237,13 @@ def _sparse_fit(self, X, y, solver_type, kernel):
n_classes = (c_int * 1)()
self._train_succeed = (c_int * 1)()
thundersvm.sparse_model_scikit(
X.shape[0], data, indptr, indices, label, solver_type,
kernel_type, self.degree, c_float(self._gamma), c_float(self.coef0),
c_float(self.C), c_float(self.nu), c_float(self.epsilon), c_float(self.tol),
self.probability, weight_size, weight_label, weight,
self.verbose, self.max_iter, self.n_jobs, self.max_mem_size,
self.gpu_id,
n_features, n_classes, self._train_succeed, c_void_p(self.model))
X.shape[0], data, indptr, indices, label, solver_type,
kernel_type, self.degree, c_float(self._gamma), c_float(self.coef0),
c_float(self.C), c_float(self.nu), c_float(self.epsilon), c_float(self.tol),
self.probability, weight_size, weight_label, weight,
self.verbose, self.max_iter, self.n_jobs, self.max_mem_size,
self.gpu_id,
n_features, n_classes, self._train_succeed, c_void_p(self.model))
self.n_features = n_features[0]
self.n_classes = n_classes[0]

Expand Down Expand Up @@ -323,7 +326,7 @@ def _dense_predict(self, X):
samples, features, data,
c_void_p(self.model),
self.predict_label_ptr, self.verbose)

self.predict_label = np.array([self.predict_label_ptr[index] for index in range(0, X.shape[0])])
return self.predict_label

Expand All @@ -341,7 +344,7 @@ def _sparse_predict(self, X):
self.predict_label_ptr, self.verbose)

predict_label = [self.predict_label_ptr[index] for index in range(0, X.shape[0])]

self.predict_label = np.asarray(predict_label)
return self.predict_label

Expand Down Expand Up @@ -412,6 +415,48 @@ def load_from_file(self, path):
kernel = (c_char * 20)()
thundersvm.init_model_param(kernel, degree, gamma,
coef0, probability,c_void_p(self.model))
n_classes = (c_int * 1)()
thundersvm.get_n_classes(c_void_p(self.model), n_classes)
self.n_classes = n_classes[0]
n_support_ = (c_int * self.n_classes)()
thundersvm.get_support_classes(n_support_, self.n_classes, c_void_p(self.model))
self.n_support_ = np.array([n_support_[index] for index in range(0, self.n_classes)]).astype(int)

self.n_sv = thundersvm.n_sv(c_void_p(self.model))

n_feature = (c_int * 1)()
thundersvm.get_sv_max_index(c_void_p(self.model), n_feature)
self.n_features = n_feature[0]

csr_row = (c_int * (self.n_sv + 1))()
csr_col = (c_int * (self.n_sv * self.n_features))()
csr_data = (c_float * (self.n_sv * self.n_features))()
data_size = (c_int * 1)()
thundersvm.get_sv(csr_row, csr_col, csr_data, data_size, c_void_p(self.model))
self.row = np.array([csr_row[index] for index in range(0, self.n_sv + 1)])
self.col = np.array([csr_col[index] for index in range(0, data_size[0])])
self.data = np.array([csr_data[index] for index in range(0, data_size[0])])
self.support_vectors_ = sp.csr_matrix((self.data, self.col, self.row))
# if self._sparse == False:
# self.support_vectors_ = self.support_vectors_.toarray(order = 'C')

dual_coef = (c_float * ((self.n_classes - 1) * self.n_sv))()
thundersvm.get_coef(dual_coef, self.n_classes, self.n_sv, c_void_p(self.model))
self.dual_coef_ = np.array([dual_coef[index] for index in range(0, (self.n_classes - 1) * self.n_sv)]).astype(float)
self.dual_coef_ = np.reshape(self.dual_coef_, (self.n_classes - 1, self.n_sv))

rho_size = int(self.n_classes * (self.n_classes - 1) / 2)
self.n_binary_model = rho_size
rho = (c_float * rho_size)()
thundersvm.get_rho(rho, rho_size, c_void_p(self.model))
self.intercept_ = np.array([rho[index] for index in range(0, rho_size)]).astype(float)

# if self.kernel == 'linear':
# coef = (c_float * (self.n_binary_model * self.n_sv))()
# thundersvm.get_linear_coef(coef, self.n_binary_model, self.n_features, c_void_p(self.model))
# self.coef_ = np.array([coef[index] for index in range(0, self.n_binary_model * self.n_features)]).astype(float)
# self.coef_ = np.reshape(self.coef_, (self.n_binary_model, self.n_features))

self.kernel = kernel.value
self.degree = degree[0]
if gamma[0] != 0.0:
Expand All @@ -421,20 +466,20 @@ def load_from_file(self, path):


class SVC(SvmModel, ClassifierMixin):
_impl = 'c_svc'
def __init__(self, kernel = 'rbf', degree = 3,
gamma = 'auto', coef0 = 0.0, C = 1.0,
tol = 0.001, probability = False, class_weight = None,
shrinking = False, cache_size = None, verbose = False,
max_iter = -1, n_jobs = -1, max_mem_size = -1, random_state = None, decision_function_shape = 'ovo', gpu_id=0):
self.decision_function_shape = decision_function_shape
super(SVC, self).__init__(
kernel=kernel, degree=degree, gamma=gamma,
coef0=coef0, C=C, nu=0., epsilon=0.,
tol=tol, probability=probability,
class_weight=class_weight, shrinking = shrinking,
cache_size = cache_size, verbose = verbose,
max_iter = max_iter, n_jobs = n_jobs, max_mem_size = max_mem_size, random_state = random_state, gpu_id=gpu_id)
_impl = 'c_svc'
def __init__(self, kernel = 'rbf', degree = 3,
gamma = 'auto', coef0 = 0.0, C = 1.0,
tol = 0.001, probability = False, class_weight = None,
shrinking = False, cache_size = None, verbose = False,
max_iter = -1, n_jobs = -1, max_mem_size = -1, random_state = None, decision_function_shape = 'ovo', gpu_id=0):
self.decision_function_shape = decision_function_shape
super(SVC, self).__init__(
kernel=kernel, degree=degree, gamma=gamma,
coef0=coef0, C=C, nu=0., epsilon=0.,
tol=tol, probability=probability,
class_weight=class_weight, shrinking = shrinking,
cache_size = cache_size, verbose = verbose,
max_iter = max_iter, n_jobs = n_jobs, max_mem_size = max_mem_size, random_state = random_state, gpu_id=gpu_id)



Expand Down Expand Up @@ -488,13 +533,13 @@ def __init__(self, kernel = 'rbf', degree = 3, gamma = 'auto',
class NuSVR(SvmModel, RegressorMixin):
_impl = 'nu_svr'
def __init__(self, kernel = 'rbf', degree = 3, gamma = 'auto',
coef0 = 0.0, nu = 0.5, C = 1.0, tol = 0.001, probability = False,
shrinking = False, cache_size = None, verbose = False,
max_iter = -1, n_jobs = -1, max_mem_size = -1, gpu_id=0):
coef0 = 0.0, nu = 0.5, C = 1.0, tol = 0.001, probability = False,
shrinking = False, cache_size = None, verbose = False,
max_iter = -1, n_jobs = -1, max_mem_size = -1, gpu_id=0):
super(NuSVR, self).__init__(
kernel = kernel, degree = degree, gamma = gamma,
coef0 = coef0, nu = nu, C = C, epsilon = 0.,
tol = tol, probability = probability, class_weight = None,
shrinking = shrinking, cache_size = cache_size, verbose = verbose,
max_iter = max_iter, n_jobs = n_jobs, max_mem_size = max_mem_size, random_state = None, gpu_id=gpu_id
)
)

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