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Merge pull request #162 from VarIr/performance
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WIP Python performance
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@QinbinLi
QinbinLi committed Sep 10, 2019
2 parents 9b499a6 + 6af1b3d commit 8445763
Showing 1 changed file with 32 additions and 39 deletions.
71 changes: 32 additions & 39 deletions python/thundersvm/thundersvm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,18 +88,14 @@ def __del__(self):
if self.model is not None:
thundersvm.model_free(c_void_p(self.model))

def label_validate(self, y):

return column_or_1d(y, warn=True).astype(np.float64)

def fit(self, X, y):
if self.model is not None:
thundersvm.model_free(c_void_p(self.model))
self.model = None
sparse = sp.isspmatrix(X)
self._sparse = sparse and not callable(self.kernel)
X, y = check_X_y(X, y, dtype=np.float64, order='C', accept_sparse='csr')
y = self.label_validate(y)
y = column_or_1d(y, warn=True).astype(np.float64)

solver_type = SVM_TYPE.index(self._impl)

Expand Down Expand Up @@ -129,40 +125,39 @@ def fit(self, X, y):
data_size = (c_int * 1)()
sv_indices = (c_int * self.n_sv)()
thundersvm.get_sv(csr_row, csr_col, csr_data, data_size, sv_indices, 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.row = np.frombuffer(csr_row, dtype=np.int32)
self.col = np.frombuffer(csr_col, dtype=np.int32)[:data_size[0]]
self.data = np.frombuffer(csr_data, dtype=np.float32)[:data_size[0]]

self.support_vectors_ = sp.csr_matrix((self.data, self.col, self.row))
if self._sparse == False:
if not self._sparse:
self.support_vectors_ = self.support_vectors_.toarray(order='C')
self.support_ = np.array([sv_indices[index] for index in range(0, self.n_sv)]).astype(int)
self.support_ = np.frombuffer(sv_indices, dtype=np.int32).astype(int)

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))
self.dual_coef_ = np.frombuffer(dual_coef, dtype=np.float32)\
.astype(float)\
.reshape((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)
self.intercept_ = np.frombuffer(rho, dtype=np.float32).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.coef_ = np.frombuffer(coef, dtype=np.float32)\
.astype(float)\
.reshape((self.n_binary_model, self.n_features))

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_support_ = np.frombuffer(n_support_, dtype=np.int32).astype(int)
self.shape_fit_ = X.shape

return self
Expand Down Expand Up @@ -325,8 +320,8 @@ def predict_proba(self, X):
# c_void_p(self.model),
# self.predict_label_ptr)
thundersvm.get_pro(c_void_p(self.model), self.predict_pro_ptr)
self.predict_prob = np.array([self.predict_pro_ptr[index] for index in range(0, size)])
self.predict_prob = np.reshape(self.predict_prob, (samples, self.n_classes))
self.predict_prob = np.frombuffer(self.predict_pro_ptr, dtype=np.float32)\
.reshape((samples, self.n_classes))
return self.predict_prob

def _dense_predict(self, X):
Expand All @@ -344,7 +339,7 @@ def _dense_predict(self, X):
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])])
self.predict_label = np.frombuffer(self.predict_label_ptr, dtype=np.float32)
return self.predict_label

def _sparse_predict(self, X):
Expand All @@ -360,9 +355,7 @@ def _sparse_predict(self, X):
c_void_p(self.model),
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)
self.predict_label = np.frombuffer(self.predict_label_ptr, dtype=np.float32)
return self.predict_label

def decision_function(self, X):
Expand Down Expand Up @@ -392,8 +385,9 @@ def _dense_decision_function(self, X):
thundersvm.dense_decision(
samples, features, data, c_void_p(self.model), dec_size, dec_value_ptr
)
self.dec_values = np.array([dec_value_ptr[index] for index in range(0, dec_size)]).astype(float)
self.dec_values = np.reshape(self.dec_values, (X.shape[0], self.n_binary_model))
self.dec_values = np.frombuffer(dec_value_ptr, dtype=np.float32)\
.astype(float)\
.reshape((X.shape[0], self.n_binary_model))
return self.dec_values

def _sparse_decision_function(self, X):
Expand All @@ -409,8 +403,8 @@ def _sparse_decision_function(self, X):
thundersvm.sparse_decision(
X.shape[0], data, indptr, indices,
c_void_p(self.model), dec_size, dec_value_ptr)
self.dec_values = np.array([dec_value_ptr[index] for index in range(0, dec_size)])
self.dec_values = np.reshape(self.dec_values, (X.shape[0], self.n_binary_model))
self.dec_values = np.frombuffer(dec_value_ptr, dtype=np.float32)\
.reshape((X.shape[0], self.n_binary_model))
return self.dec_values

def save_to_file(self, path):
Expand All @@ -435,8 +429,7 @@ def load_from_file(self, path):
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_support_ = np.frombuffer(n_support_, dtype=np.int32).astype(int)
self.n_sv = thundersvm.n_sv(c_void_p(self.model))

n_feature = (c_int * 1)()
Expand All @@ -448,24 +441,24 @@ def load_from_file(self, path):
data_size = (c_int * 1)()
sv_indices = (c_int * self.n_sv)()
thundersvm.get_sv(csr_row, csr_col, csr_data, data_size, sv_indices, 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.row = np.frombuffer(csr_row, dtype=np.int32)
self.col = np.frombuffer(csr_col, dtype=np.int32)[:data_size[0]]
self.data = np.frombuffer(csr_data, dtype=np.float32)[: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')
self.support_ = np.array([sv_indices[index] for index in range(0, self.n_sv)]).astype(int)
self.support_ = np.frombuffer(sv_indices, dtype=np.int32)
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))
self.dual_coef_ = np.frombuffer(dual_coef, dtype=np.float32)\
.astype(float)\
.reshape((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)
self.intercept_ = np.frombuffer(rho, dtype=np.float32).astype(float)

# if self.kernel == 'linear':
# coef = (c_float * (self.n_binary_model * self.n_sv))()
Expand Down

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